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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 293.19717, "r_x1": 295.34586, "r_y1": 293.19717, "r_x2": 295.34586, "r_y2": 284.82254, "r_x3": 53.79800000000001, "r_y3": 284.82254, "coord_origin": "TOPLEFT" }, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 304.15616000000006, "r_x1": 294.04709, "r_y1": 304.15616000000006, "r_x2": 294.04709, "r_y2": 295.78152, "r_x3": 53.79800000000001, "r_y3": 295.78152, "coord_origin": "TOPLEFT" }, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 315.11517, "r_x1": 294.04645, "r_y1": 315.11517, "r_x2": 294.04645, "r_y2": 306.74053999999995, "r_x3": 53.79800000000001, "r_y3": 306.74053999999995, "coord_origin": "TOPLEFT" }, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 326.07416, "r_x1": 294.27573, "r_y1": 326.07416, "r_x2": 294.27573, "r_y2": 317.69952, "r_x3": 53.79800000000001, "r_y3": 317.69952, "coord_origin": "TOPLEFT" }, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 337.03317, "r_x1": 294.04712, "r_y1": 337.03317, "r_x2": 294.04712, "r_y2": 328.65854, "r_x3": 53.79800000000001, "r_y3": 328.65854, "coord_origin": "TOPLEFT" }, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 347.99219, "r_x1": 294.0437, "r_y1": 347.99219, "r_x2": 294.0437, "r_y2": 339.61755, "r_x3": 53.79800000000001, "r_y3": 339.61755, "coord_origin": "TOPLEFT" }, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 358.95117, "r_x1": 294.04715, "r_y1": 358.95117, "r_x2": 294.04715, "r_y2": 350.57654, "r_x3": 53.79800000000001, "r_y3": 350.57654, "coord_origin": "TOPLEFT" }, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 369.90918000000005, "r_x1": 294.04364, "r_y1": 369.90918000000005, "r_x2": 294.04364, "r_y2": 361.53455, "r_x3": 53.79800000000001, "r_y3": 361.53455, "coord_origin": "TOPLEFT" }, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 380.87714000000005, "r_x1": 92.863388, "r_y1": 380.87714000000005, "r_x2": 92.863388, "r_y2": 372.53839, "r_x3": 53.79800000000001, "r_y3": 372.53839, "coord_origin": "TOPLEFT" }, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 92.863998, "r_y0": 380.86816, "r_x1": 294.04361, "r_y1": 380.86816, "r_x2": 294.04361, "r_y2": 372.49353, "r_x3": 92.863998, "r_y3": 372.49353, "coord_origin": "TOPLEFT" }, "text": ", a new, publicly available, document-layout annotation", "orig": ", a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 391.82718, "r_x1": 294.04718, "r_y1": 391.82718, "r_x2": 294.04718, "r_y2": 383.45255, "r_x3": 53.79800000000001, "r_y3": 383.45255, "coord_origin": "TOPLEFT" }, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 402.78616, "r_x1": 294.0437, "r_y1": 402.78616, "r_x2": 294.0437, "r_y2": 394.41153, "r_x3": 53.79800000000001, "r_y3": 394.41153, "coord_origin": "TOPLEFT" }, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 413.74518, "r_x1": 294.04535, "r_y1": 413.74518, "r_x2": 294.04535, "r_y2": 405.37054, "r_x3": 53.79800000000001, "r_y3": 405.37054, "coord_origin": "TOPLEFT" }, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 424.70416000000006, "r_x1": 294.04715, "r_y1": 424.70416000000006, "r_x2": 294.04715, "r_y2": 416.32953, "r_x3": 53.79800000000001, "r_y3": 416.32953, "coord_origin": "TOPLEFT" }, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 435.66318, "r_x1": 294.04712, "r_y1": 435.66318, "r_x2": 294.04712, "r_y2": 427.28853999999995, "r_x3": 53.79800000000001, "r_y3": 427.28853999999995, "coord_origin": "TOPLEFT" }, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 446.62216, "r_x1": 295.03, "r_y1": 446.62216, "r_x2": 295.03, "r_y2": 438.24753, "r_x3": 53.79800000000001, "r_y3": 438.24753, "coord_origin": "TOPLEFT" }, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 457.58118, "r_x1": 294.21616, "r_y1": 457.58118, "r_x2": 294.21616, "r_y2": 449.20654, "r_x3": 53.466999, "r_y3": 449.20654, "coord_origin": "TOPLEFT" }, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 468.54016, "r_x1": 294.04712, "r_y1": 468.54016, "r_x2": 294.04712, "r_y2": 460.16553, "r_x3": 53.79800000000001, "r_y3": 460.16553, "coord_origin": "TOPLEFT" }, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 479.49817, "r_x1": 295.56018, "r_y1": 479.49817, "r_x2": 295.56018, "r_y2": 471.12354, "r_x3": 53.79800000000001, "r_y3": 471.12354, "coord_origin": "TOPLEFT" }, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 490.45718, "r_x1": 295.42783, "r_y1": 490.45718, "r_x2": 295.42783, "r_y2": 482.08255, "r_x3": 53.79800000000001, "r_y3": 482.08255, "coord_origin": "TOPLEFT" }, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 501.41617, "r_x1": 294.04715, "r_y1": 501.41617, "r_x2": 294.04715, "r_y2": 493.04153, "r_x3": 53.79800000000001, "r_y3": 493.04153, "coord_origin": "TOPLEFT" }, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", 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Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.79189, "r_x1": 187.72285, "r_y1": 672.79189, "r_x2": 187.72285, "r_y2": 666.27837, "r_x3": 53.79800000000001, "r_y3": 666.27837, "coord_origin": "TOPLEFT" }, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 681.56586, "r_x1": 197.86275, "r_y1": 681.56586, "r_x2": 197.86275, "r_y2": 675.08023, "r_x3": 53.79800000000001, "r_y3": 675.08023, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, 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339.26736, "coord_origin": "TOPLEFT" }, "text": " the image with a digital camera or a", "orig": " the image with a digital camera or a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.15176, "r_y0": 342.22049, "r_x1": 337.91086, "r_y1": 342.22049, "r_x2": 337.91086, "r_y2": 340.69882, "r_x3": 328.15176, "r_y3": 340.69882, "coord_origin": "TOPLEFT" }, "text": " micro TV unit", "orig": " micro TV unit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.8313, "r_y0": 343.71207, "r_x1": 329.09155, "r_y1": 343.71207, "r_x2": 329.09155, "r_y2": 342.19043000000005, "r_x3": 327.8313, "r_y3": 342.19043000000005, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 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254.94812000000002, "r_x3": 351.82651, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "cont.", "orig": "cont.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.85242, "r_y0": 258.68066, "r_x1": 362.60028, "r_y1": 258.68066, "r_x2": 362.60028, "r_y2": 254.76782000000003, "r_x3": 360.85242, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 265.23395000000005, "r_x1": 345.84351, "r_y1": 265.23395000000005, "r_x2": 345.84351, "r_y2": 263.49492999999995, "r_x3": 326.88037, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false 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329.03836, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "There are two objectives. The lower", "orig": "There are two objectives. 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Then, by rotating the", "orig": " objective first. Then, by rotating the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 278.11566000000005, "r_x1": 350.81885, "r_y1": 278.11566000000005, "r_x2": 350.81885, "r_y2": 276.37665000000004, "r_x3": 326.88037, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": " case, the magnification can be", "orig": " case, the magnification can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 279.72589000000005, "r_x1": 335.46707, "r_y1": 279.72589000000005, "r_x2": 335.46707, "r_y2": 277.98688000000004, "r_x3": 326.88037, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 282.94632, "r_x1": 354.57755, "r_y1": 282.94632, "r_x2": 354.57755, "r_y2": 281.20728, "r_x3": 326.88037, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 284.55652, "r_x1": 335.1752, "r_y1": 284.55652, "r_x2": 335.1752, "r_y2": 282.81750000000005, "r_x3": 326.88037, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 286.16675, "r_x1": 328.34784, "r_y1": 286.16675, "r_x2": 328.34784, "r_y2": 284.4277, "r_x3": 326.88037, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.08157, "r_y0": 286.16675, "r_x1": 354.76245, "r_y1": 286.16675, "r_x2": 354.76245, "r_y2": 284.4277, "r_x3": 329.08157, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "The distance between the observer's", "orig": "The distance between the observer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 287.77695, "r_x1": 354.6499, "r_y1": 287.77695, "r_x2": 354.6499, "r_y2": 286.03793, "r_x3": 326.88037, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " pupils is the interpupillary distance.", "orig": " pupils is the interpupillary distance.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 289.38718, "r_x1": 328.25125, "r_y1": 289.38718, "r_x2": 328.25125, "r_y2": 287.64813, "r_x3": 326.88037, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.93671, "r_y0": 289.38718, "r_x1": 354.29825, "r_y1": 289.38718, "r_x2": 354.29825, "r_y2": 287.64813, "r_x3": 328.93671, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "To adjust the interpupillary distance", "orig": "To adjust the interpupillary distance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 290.99738, "r_x1": 355.02075, "r_y1": 290.99738, "r_x2": 355.02075, "r_y2": 289.25836, "r_x3": 326.88181, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": " rotate the prism caps until both eyes", "orig": " rotate the prism caps until both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 292.6076, "r_x1": 350.82028, "r_y1": 292.6076, "r_x2": 350.82028, "r_y2": 290.86855999999995, "r_x3": 326.88181, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " coincide with the image in the", "orig": " coincide with the image in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 294.2178, "r_x1": 336.2067, "r_y1": 294.2178, "r_x2": 336.2067, "r_y2": 292.47879, "r_x3": 326.88181, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 297.43823, "r_x1": 335.3941, "r_y1": 297.43823, "r_x2": 335.3941, "r_y2": 295.69922, "r_x3": 326.88181, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 299.04846, "r_x1": 328.34314, "r_y1": 299.04846, "r_x2": 328.34314, "r_y2": 297.30942, "r_x3": 326.88181, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.07379, "r_y0": 299.04846, "r_x1": 353.18555, "r_y1": 299.04846, "r_x2": 353.18555, "r_y2": 297.30942, "r_x3": 329.07379, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "Remove the lens protective cover.", "orig": "Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 300.65866, "r_x1": 328.35919, "r_y1": 300.65866, "r_x2": 328.35919, "r_y2": 298.91965, "r_x3": 326.88324, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.0972, "r_y0": 300.65866, "r_x1": 353.45065, "r_y1": 300.65866, "r_x2": 353.45065, "r_y2": 298.91965, "r_x3": 329.0972, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "Place the specimen on the working", "orig": "Place the specimen on the working", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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329.02783, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": "Focus the specimen with the left eye", "orig": "Focus the specimen with the left eye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 305.48932, "r_x1": 355.96307, "r_y1": 305.48932, "r_x2": 355.96307, "r_y2": 303.75027, "r_x3": 326.88324, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " first while turning the focus knob until", "orig": " first while turning the focus knob until", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 307.09952000000004, "r_x1": 354.46594, "r_y1": 307.09952000000004, "r_x2": 354.46594, "r_y2": 305.3605, "r_x3": 326.88324, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 308.70975, "r_x1": 328.25488, "r_y1": 308.70975, "r_x2": 328.25488, "r_y2": 306.9707, "r_x3": 326.88324, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.9407, "r_y0": 308.70975, "r_x1": 356.37335, "r_y1": 308.70975, "r_x2": 356.37335, "r_y2": 306.9707, "r_x3": 328.9407, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "Rotate the right eyepiece ring until the", "orig": "Rotate the right eyepiece ring until the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 310.31995, "r_x1": 355.38867, "r_y1": 310.31995, "r_x2": 355.38867, "r_y2": 308.58093, "r_x3": 326.88324, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " images in each eyepiece coincide and", "orig": " images in each eyepiece coincide and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 311.93018, "r_x1": 343.17249, "r_y1": 311.93018, "r_x2": 343.17249, "r_y2": 310.19113, "r_x3": 326.88324, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 315.15059999999994, "r_x1": 344.13388, "r_y1": 315.15059999999994, "r_x2": 344.13388, "r_y2": 313.41156, "r_x3": 326.88324, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 316.76079999999996, "r_x1": 328.37418, "r_y1": 316.76079999999996, "r_x2": 328.37418, "r_y2": 315.02178999999995, "r_x3": 326.88324, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.11963, "r_y0": 316.76079999999996, "r_x1": 348.50162, "r_y1": 316.76079999999996, "r_x2": 348.50162, "r_y2": 315.02178999999995, "r_x3": 329.11963, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord.", "orig": "Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 318.37103, "r_x1": 328.34061, "r_y1": 318.37103, "r_x2": 328.34061, "r_y2": 316.63199, "r_x3": 326.88324, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06931, "r_y0": 318.37103, "r_x1": 353.11588, "r_y1": 318.37103, "r_x2": 353.11588, "r_y2": 316.63199, "r_x3": 329.06931, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "When the bulb is cool, remove the", "orig": "When the bulb is cool, remove the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 319.9812299999999, "r_x1": 353.79517, "r_y1": 319.9812299999999, "r_x2": 353.79517, "r_y2": 318.2422199999999, "r_x3": 326.88464, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " oblique illuminator cap and remove", "orig": " oblique illuminator cap and remove", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 321.59146, "r_x1": 348.02094, "r_y1": 321.59146, "r_x2": 348.02094, "r_y2": 319.85242000000005, "r_x3": 326.88464, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 323.20166, "r_x1": 328.37512, "r_y1": 323.20166, "r_x2": 328.37512, "r_y2": 321.46265, "r_x3": 326.88464, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 139, 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"TOPLEFT" }, "text": "Open the window in the base plate and", "orig": "Open the window in the base plate and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 326.42209, "r_x1": 350.13828, "r_y1": 326.42209, "r_x2": 350.13828, "r_y2": 324.68307000000004, "r_x3": 326.88608, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 328.03232, "r_x1": 351.59677, "r_y1": 328.03232, "r_x2": 351.59677, "r_y2": 326.29327, "r_x3": 326.88608, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 329.64252, "r_x1": 336.89197, "r_y1": 329.64252, "r_x2": 336.89197, "r_y2": 327.9035, "r_x3": 326.88608, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " illuminator.", "orig": " illuminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 145, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 265.23395000000005, "r_x1": 366.93256, "r_y1": 265.23395000000005, "r_x2": 366.93256, "r_y2": 263.49492999999995, "r_x3": 358.42023, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 146, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 266.84418000000005, "r_x1": 359.89841, "r_y1": 266.84418000000005, "r_x2": 359.89841, "r_y2": 265.10515999999996, "r_x3": 358.42023, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 386.70093, "r_y1": 290.99738, "r_x2": 386.70093, "r_y2": 289.25836, "r_x3": 360.49353, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "If the image is not clear to both eyes", "orig": "If the image is not clear to both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 292.6076, "r_x1": 388.03534, "r_y1": 292.6076, "r_x2": 388.03534, "r_y2": 290.86855999999995, "r_x3": 358.42453, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " at the same time, the diopter ring may", "orig": " at the same time, the diopter ring may", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 294.2178, "r_x1": 373.13724, "r_y1": 294.2178, "r_x2": 373.13724, "r_y2": 292.47879, "r_x3": 358.42453, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 297.43823, "r_x1": 381.74539, "r_y1": 297.43823, "r_x2": 381.74539, "r_y2": 295.69922, "r_x3": 358.42453, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 299.04846, "r_x1": 359.83682, "r_y1": 299.04846, "r_x2": 359.83682, "r_y2": 297.30942, "r_x3": 358.42453, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.54297, "r_y0": 299.04846, "r_x1": 388.08289, "r_y1": 299.04846, "r_x2": 388.08289, "r_y2": 297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 302.26889000000006, "r_x1": 387.72266, "r_y1": 302.26889000000006, "r_x2": 387.72266, "r_y2": 300.52985, "r_x3": 358.42453, "r_y3": 300.52985, "coord_origin": "TOPLEFT" }, "text": " differences in acuity between the right", "orig": " differences in acuity between the right", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 303.8790900000001, "r_x1": 384.1991, "r_y1": 303.8790900000001, "r_x2": 384.1991, "r_y2": 302.14008000000007, "r_x3": 358.42453, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": " and left eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": 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"coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": 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382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", 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"orig": "Open the window in the base plate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 205, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42883, "r_y0": 347.35489, "r_x1": 383.2782, "r_y1": 347.35489, "r_x2": 383.2782, "r_y2": 345.61584, "r_x3": 358.42883, "r_y3": 345.61584, "coord_origin": "TOPLEFT" }, "text": " and replace the halogen lamp or", "orig": " and replace the halogen lamp or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 206, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42883, "r_y0": 348.96509, "r_x1": 383.13953, "r_y1": 348.96509, "r_x2": 383.13953, "r_y2": 347.22607, "r_x3": 358.42883, "r_y3": 347.22607, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 207, "rgba": { "r": 0, "g": 0, "b": 0, 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"text": "Adjustment", "orig": "Adjustment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 215, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 441.3895, "r_y0": 282.91939999999994, "r_x1": 444.40371999999996, "r_y1": 282.91939999999994, "r_x2": 444.40371999999996, "r_y2": 281.48593, "r_x3": 441.3895, "r_y3": 281.48593, "coord_origin": "TOPLEFT" }, "text": "Knob", "orig": "Knob", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 216, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 398.79288, "r_y0": 258.85931000000005, "r_x1": 428.91568, "r_y1": 258.85931000000005, "r_x2": 428.91568, "r_y2": 254.94646999999998, "r_x3": 398.79288, "r_y3": 254.94646999999998, "coord_origin": "TOPLEFT" }, "text": "MODEL AY11236", "orig": "MODEL AY11236", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 217, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { 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To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 255, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 538.31085, "r_y0": 387.87842, "r_x1": 549.49921, "r_y1": 387.87842, "r_x2": 549.49921, "r_y2": 385.75732, "r_x3": 538.31085, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 256, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 390.1571, "r_x1": 547.58185, "r_y1": 390.1571, "r_x2": 547.58185, "r_y2": 388.03601, "r_x3": 449.10074000000003, "r_y3": 388.03601, "coord_origin": "TOPLEFT" }, "text": "the circling line of minimums. 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The new circling tables and explanatory information is located in the Legend of the TPP.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 257, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 395.33386, "r_x1": 449.6163, "r_y1": 395.33386, "r_x2": 449.6163, "r_y2": 393.2128000000001, "r_x3": 449.10074000000003, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 258, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.1319, "r_y0": 395.33386, "r_x1": 529.53082, "r_y1": 395.33386, "r_x2": 529.53082, "r_y2": 393.2128000000001, "r_x3": 450.1319, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "orig": 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"confidence": 1.0, "from_ocr": false }, { "index": 261, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 417.50446, "r_x1": 496.2829, "r_y1": 417.50446, "r_x2": 496.2829, "r_y2": 415.59549, "r_x3": 449.95525999999995, "r_y3": 415.59549, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 262, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 411.1644, "r_x1": 551.16101, "r_y1": 411.1644, "r_x2": 551.16101, "r_y2": 409.25543, "r_x3": 501.13077, "r_y3": 409.25543, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 264, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 422.73331, "r_x1": 469.35599, "r_y1": 422.73331, "r_x2": 469.35599, "r_y2": 420.18802, "r_x3": 449.10074000000003, "r_y3": 420.18802, "coord_origin": "TOPLEFT" }, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 265, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 427.21017, "r_x1": 525.93616, "r_y1": 427.21017, "r_x2": 525.93616, "r_y2": 425.08908, "r_x3": 449.10074000000003, "r_y3": 425.08908, "coord_origin": "TOPLEFT" }, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 266, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 429.48886, "r_x1": 522.0343, "r_y1": 429.48886, "r_x2": 522.0343, "r_y2": 427.3678, "r_x3": 449.10074000000003, "r_y3": 427.3678, "coord_origin": "TOPLEFT" }, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 267, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 431.76755, "r_x1": 524.67151, "r_y1": 431.76755, "r_x2": 524.67151, "r_y2": 429.64648, "r_x3": 449.10074000000003, "r_y3": 429.64648, "coord_origin": "TOPLEFT" }, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 268, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 434.04623, "r_x1": 527.172, "r_y1": 434.04623, "r_x2": 527.172, "r_y2": 431.92514000000006, "r_x3": 449.10074000000003, "r_y3": 431.92514000000006, "coord_origin": "TOPLEFT" }, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 269, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 436.32492, "r_x1": 502.39545, "r_y1": 436.32492, "r_x2": 502.39545, "r_y2": 434.20383, "r_x3": 449.10074000000003, "r_y3": 434.20383, "coord_origin": "TOPLEFT" }, "text": "dimensions (length and width) are shown for all active runways.", "orig": "dimensions (length and width) are shown for all active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 270, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 440.88228999999995, "r_x1": 512.92676, "r_y1": 440.88228999999995, "r_x2": 512.92676, "r_y2": 438.7611999999999, "r_x3": 449.10074000000003, "r_y3": 438.7611999999999, "coord_origin": "TOPLEFT" }, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 271, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 445.97900000000004, "r_x1": 460.02307, "r_y1": 445.97900000000004, "r_x2": 460.02307, "r_y2": 444.07001, "r_x3": 449.95525999999995, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 272, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 445.97900000000004, "r_x1": 473.98819, "r_y1": 445.97900000000004, "r_x2": 473.98819, "r_y2": 444.07001, "r_x3": 464.89963, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 273, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 448.02979, "r_x1": 474.96744, "r_y1": 448.02979, "r_x2": 474.96744, "r_y2": 446.12085, "r_x3": 464.89963, "r_y3": 446.12085, "coord_origin": "TOPLEFT" }, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 274, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 478.91357, "r_y0": 445.97900000000004, "r_x1": 489.45648, "r_y1": 445.97900000000004, "r_x2": 489.45648, "r_y2": 444.07001, "r_x3": 478.91357, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 275, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 493.06420999999995, "r_y0": 445.97900000000004, "r_x1": 505.03076, "r_y1": 445.97900000000004, "r_x2": 505.03076, "r_y2": 444.07001, "r_x3": 493.06420999999995, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 276, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 509.5809, "r_y0": 445.97900000000004, "r_x1": 524.30237, "r_y1": 445.97900000000004, "r_x2": 524.30237, "r_y2": 444.07001, "r_x3": 509.5809, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 277, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 456.72104, "r_x1": 458.31406, "r_y1": 456.72104, "r_x2": 458.31406, "r_y2": 454.81207, "r_x3": 449.95525999999995, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 278, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 458.77185000000003, "r_x1": 461.92083999999994, "r_y1": 458.77185000000003, "r_x2": 461.92083999999994, "r_y2": 456.86288, "r_x3": 449.95525999999995, "r_y3": 456.86288, "coord_origin": "TOPLEFT" }, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 279, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 460.82268999999997, "r_x1": 457.08014, "r_y1": 460.82268999999997, "r_x2": 457.08014, "r_y2": 458.91373, "r_x3": 449.95525999999995, "r_y3": 458.91373, "coord_origin": "TOPLEFT" }, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 280, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 456.72104, "r_x1": 472.87732, "r_y1": 456.72104, "r_x2": 472.87732, "r_y2": 454.81207, "r_x3": 464.89963, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 281, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 458.77185000000003, "r_x1": 472.49792, "r_y1": 458.77185000000003, "r_x2": 472.49792, "r_y2": 456.86288, "r_x3": 464.89963, "r_y3": 456.86288, "coord_origin": "TOPLEFT" }, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 282, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 478.91357, "r_y0": 456.72104, "r_x1": 483.61584, "r_y1": 456.72104, "r_x2": 483.61584, "r_y2": 454.81207, "r_x3": 478.91357, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Closed", "orig": "Closed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 283, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 478.91357, "r_y0": 458.77185000000003, "r_x1": 486.60754000000003, "r_y1": 458.77185000000003, "r_x2": 486.60754000000003, "r_y2": 456.86288, "r_x3": 478.91357, "r_y3": 456.86288, "coord_origin": "TOPLEFT" }, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 284, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 493.06420999999995, "r_y0": 456.72104, "r_x1": 504.20648, "r_y1": 456.72104, "r_x2": 504.20648, "r_y2": 454.81207, "r_x3": 493.06420999999995, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 285, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 471.45081, "r_x1": 548.59674, "r_y1": 471.45081, "r_x2": 548.59674, "r_y2": 469.32974, "r_x3": 449.10074000000003, "r_y3": 469.32974, "coord_origin": "TOPLEFT" }, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. 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The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. 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400.46155, "coord_origin": "TOPLEFT" }, "text": "Indicative dates only, subject to change/Board confi rmation", "orig": "Indicative dates only, subject to change/Board confi rmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 324, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 406.60156, "r_x1": 391.771, "r_y1": 406.60156, "r_x2": 391.771, "r_y2": 404.34503, "r_x3": 329.40536, "r_y3": 404.34503, "coord_origin": "TOPLEFT" }, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 325, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 408.63513000000006, "r_x1": 369.65308, "r_y1": 408.63513000000006, "r_x2": 369.65308, "r_y2": 406.37857, 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1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 328, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 471.00577, "r_x1": 395.01788, "r_y1": 471.00577, "r_x2": 395.01788, "r_y2": 468.74924, "r_x3": 363.54486, "r_y3": 468.74924, "coord_origin": "TOPLEFT" }, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 329, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 473.03934, "r_x1": 394.08322, "r_y1": 473.03934, "r_x2": 394.08322, "r_y2": 470.78281, "r_x3": 363.54486, "r_y3": 470.78281, "coord_origin": "TOPLEFT" }, "text": "the AGL business has an established history ", "orig": "the AGL business has an established history ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 330, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 475.07288, "r_x1": 390.60727, "r_y1": 475.07288, "r_x2": 390.60727, "r_y2": 472.81635, "r_x3": 363.54486, "r_y3": 472.81635, "coord_origin": "TOPLEFT" }, "text": "and reputation for serving the gas and ", "orig": "and reputation for serving the gas and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 331, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 477.10645, "r_x1": 393.49612, "r_y1": 477.10645, "r_x2": 393.49612, "r_y2": 474.84988, "r_x3": 363.54486, "r_y3": 474.84988, "coord_origin": "TOPLEFT" }, "text": "electricity needs of Australian households. ", "orig": "electricity needs of Australian households. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 332, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 479.13998, "r_x1": 394.11481, "r_y1": 479.13998, "r_x2": 394.11481, "r_y2": 476.88345, "r_x3": 363.54486, "r_y3": 476.88345, "coord_origin": "TOPLEFT" }, "text": "In 1841, when AGL supplied the gas to light ", "orig": "In 1841, when AGL supplied the gas to light ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 333, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 481.17352, "r_x1": 393.75891, "r_y1": 481.17352, "r_x2": 393.75891, "r_y2": 478.91699, "r_x3": 363.54486, "r_y3": 478.91699, "coord_origin": "TOPLEFT" }, "text": "the fi rst public street lamp, it was reported ", "orig": "the fi rst public street lamp, it was reported ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 334, "rgba": { "r": 0, 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394.27283, "r_y2": 485.01764, "r_x3": 363.54486, "r_y3": 485.01764, "coord_origin": "TOPLEFT" }, "text": "by mechanical ingenuity.\u201d Within two years, ", "orig": "by mechanical ingenuity.\u201d Within two years, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 337, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 489.30774, "r_x1": 396.65939, "r_y1": 489.30774, "r_x2": 396.65939, "r_y2": 487.05121, "r_x3": 363.54486, "r_y3": 487.05121, "coord_origin": "TOPLEFT" }, "text": "165 gas lamps were lighting the City of Sydney.", "orig": "165 gas lamps were lighting the City of Sydney.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 338, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.4054, "r_y0": 431.09412, "r_x1": 384.19696, "r_y1": 431.09412, "r_x2": 384.19696, "r_y2": 419.93124, "r_x3": 329.4054, "r_y3": 419.93124, "coord_origin": "TOPLEFT" }, "text": "Looking back on ", "orig": "Looking back on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 339, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.4054, "r_y0": 441.26669, "r_x1": 372.16626, "r_y1": 441.26669, "r_x2": 372.16626, "r_y2": 430.10379, "r_x3": 329.4054, "r_y3": 430.10379, "coord_origin": "TOPLEFT" }, "text": "175 years of ", "orig": "175 years of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 340, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.4054, "r_y0": 451.43924, "r_x1": 385.3981, "r_y1": 451.43924, "r_x2": 385.3981, "r_y2": 440.27636999999993, "r_x3": 329.4054, "r_y3": 440.27636999999993, "coord_origin": "TOPLEFT" }, "text": "looking forward.", "orig": "looking forward.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 341, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 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"orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 344, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 490.30679, "r_y0": 254.98479999999995, "r_x1": 492.09982, "r_y1": 254.98479999999995, "r_x2": 492.09982, "r_y2": 251.47478999999998, "r_x3": 490.30679, "r_y3": 251.47478999999998, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 345, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 265.92719, "r_x1": 500.05637, "r_y1": 265.92719, "r_x2": 500.05637, "r_y2": 263.88922, "r_x3": 498.15335, "r_y3": 263.88922, "coord_origin": "TOPLEFT" }, "text": "In ", "orig": "In ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 346, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.05637, "r_y0": 265.86310000000003, "r_x1": 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}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 352, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 280.9357, "r_x1": 505.64642000000003, "r_y1": 280.9357, "r_x2": 505.64642000000003, "r_y2": 277.34619, "r_x3": 498.15335, "r_y3": 277.34619, "coord_origin": "TOPLEFT" }, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 353, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 283.85983, "r_x1": 543.92957, "r_y1": 283.85983, "r_x2": 543.92957, "r_y2": 281.82187, "r_x3": 498.15335, "r_y3": 281.82187, "coord_origin": "TOPLEFT" }, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 354, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 285.90289, "r_x1": 545.67834, "r_y1": 285.90289, "r_x2": 545.67834, "r_y2": 283.8649, "r_x3": 498.15335, "r_y3": 283.8649, "coord_origin": "TOPLEFT" }, "text": "the messages printed on them. Understanding these three ways ", "orig": "the messages printed on them. Understanding these three ways ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 355, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 287.94592, "r_x1": 545.26471, "r_y1": 287.94592, "r_x2": 545.26471, "r_y2": 285.90796, "r_x3": 498.15335, "r_y3": 285.90796, "coord_origin": "TOPLEFT" }, "text": "of classifying signs will help you figure out the meaning of signs ", "orig": "of classifying signs will help you figure out the meaning of signs ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 356, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 289.98895, "r_x1": 513.31335, "r_y1": 289.98895, "r_x2": 513.31335, "r_y2": 287.95099, "r_x3": 498.15335, "r_y3": 287.95099, "coord_origin": "TOPLEFT" }, "text": "that are new to you. ", "orig": "that are new to you. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 357, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 505.43439, "r_y0": 304.89639, "r_x1": 508.53033000000005, "r_y1": 304.89639, "r_x2": 508.53033000000005, "r_y2": 303.07596, "r_x3": 505.43439, "r_y3": 303.07596, "coord_origin": "TOPLEFT" }, "text": "Stop", "orig": "Stop", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 358, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 527.45502, "r_y0": 305.07397, "r_x1": 541.44678, "r_y1": 305.07397, "r_x2": 541.44678, "r_y2": 303.25354, "r_x3": 527.45502, "r_y3": 303.25354, "coord_origin": "TOPLEFT" }, "text": "Yield the right-of-way", "orig": "Yield the right-of-way", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 359, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.79385, "r_y0": 323.01016, "r_x1": 510.41632, "r_y1": 323.01016, 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 293.19717, "r_x1": 295.34586, "r_y1": 293.19717, "r_x2": 295.34586, "r_y2": 284.82254, "r_x3": 53.79800000000001, "r_y3": 284.82254, "coord_origin": "TOPLEFT" }, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 304.15616000000006, "r_x1": 294.04709, "r_y1": 304.15616000000006, "r_x2": 294.04709, "r_y2": 295.78152, "r_x3": 53.79800000000001, "r_y3": 295.78152, "coord_origin": "TOPLEFT" }, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 315.11517, "r_x1": 294.04645, "r_y1": 315.11517, "r_x2": 294.04645, "r_y2": 306.74053999999995, "r_x3": 53.79800000000001, "r_y3": 306.74053999999995, "coord_origin": "TOPLEFT" }, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 326.07416, "r_x1": 294.27573, "r_y1": 326.07416, "r_x2": 294.27573, "r_y2": 317.69952, "r_x3": 53.79800000000001, "r_y3": 317.69952, "coord_origin": "TOPLEFT" }, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 337.03317, "r_x1": 294.04712, "r_y1": 337.03317, "r_x2": 294.04712, "r_y2": 328.65854, "r_x3": 53.79800000000001, "r_y3": 328.65854, "coord_origin": "TOPLEFT" }, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 347.99219, "r_x1": 294.0437, "r_y1": 347.99219, "r_x2": 294.0437, "r_y2": 339.61755, "r_x3": 53.79800000000001, "r_y3": 339.61755, "coord_origin": "TOPLEFT" }, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 358.95117, "r_x1": 294.04715, "r_y1": 358.95117, "r_x2": 294.04715, "r_y2": 350.57654, "r_x3": 53.79800000000001, "r_y3": 350.57654, "coord_origin": "TOPLEFT" }, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 369.90918000000005, "r_x1": 294.04364, "r_y1": 369.90918000000005, "r_x2": 294.04364, "r_y2": 361.53455, "r_x3": 53.79800000000001, "r_y3": 361.53455, "coord_origin": "TOPLEFT" }, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 380.87714000000005, "r_x1": 92.863388, "r_y1": 380.87714000000005, "r_x2": 92.863388, "r_y2": 372.53839, "r_x3": 53.79800000000001, "r_y3": 372.53839, "coord_origin": "TOPLEFT" }, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 92.863998, "r_y0": 380.86816, "r_x1": 294.04361, "r_y1": 380.86816, "r_x2": 294.04361, "r_y2": 372.49353, "r_x3": 92.863998, "r_y3": 372.49353, "coord_origin": "TOPLEFT" }, "text": ", a new, publicly available, document-layout annotation", "orig": ", a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 391.82718, "r_x1": 294.04718, "r_y1": 391.82718, "r_x2": 294.04718, "r_y2": 383.45255, "r_x3": 53.79800000000001, "r_y3": 383.45255, "coord_origin": "TOPLEFT" }, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 402.78616, "r_x1": 294.0437, "r_y1": 402.78616, "r_x2": 294.0437, "r_y2": 394.41153, "r_x3": 53.79800000000001, "r_y3": 394.41153, "coord_origin": "TOPLEFT" }, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 413.74518, "r_x1": 294.04535, "r_y1": 413.74518, "r_x2": 294.04535, "r_y2": 405.37054, "r_x3": 53.79800000000001, "r_y3": 405.37054, "coord_origin": "TOPLEFT" }, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 424.70416000000006, "r_x1": 294.04715, "r_y1": 424.70416000000006, "r_x2": 294.04715, "r_y2": 416.32953, "r_x3": 53.79800000000001, "r_y3": 416.32953, "coord_origin": "TOPLEFT" }, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 435.66318, "r_x1": 294.04712, "r_y1": 435.66318, "r_x2": 294.04712, "r_y2": 427.28853999999995, "r_x3": 53.79800000000001, "r_y3": 427.28853999999995, "coord_origin": "TOPLEFT" }, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 446.62216, "r_x1": 295.03, "r_y1": 446.62216, "r_x2": 295.03, "r_y2": 438.24753, "r_x3": 53.79800000000001, "r_y3": 438.24753, "coord_origin": "TOPLEFT" }, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 457.58118, "r_x1": 294.21616, "r_y1": 457.58118, "r_x2": 294.21616, "r_y2": 449.20654, "r_x3": 53.466999, "r_y3": 449.20654, "coord_origin": "TOPLEFT" }, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 468.54016, "r_x1": 294.04712, "r_y1": 468.54016, "r_x2": 294.04712, "r_y2": 460.16553, "r_x3": 53.79800000000001, "r_y3": 460.16553, "coord_origin": "TOPLEFT" }, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 479.49817, "r_x1": 295.56018, "r_y1": 479.49817, "r_x2": 295.56018, "r_y2": 471.12354, "r_x3": 53.79800000000001, "r_y3": 471.12354, "coord_origin": "TOPLEFT" }, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 490.45718, "r_x1": 295.42783, "r_y1": 490.45718, "r_x2": 295.42783, "r_y2": 482.08255, "r_x3": 53.79800000000001, "r_y3": 482.08255, "coord_origin": "TOPLEFT" }, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 501.41617, "r_x1": 294.04715, "r_y1": 501.41617, "r_x2": 294.04715, "r_y2": 493.04153, "r_x3": 53.79800000000001, "r_y3": 493.04153, "coord_origin": "TOPLEFT" }, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 512.37518, "r_x1": 295.55618, "r_y1": 512.37518, "r_x2": 295.55618, "r_y2": 504.00055, "r_x3": 53.79800000000001, "r_y3": 504.00055, "coord_origin": "TOPLEFT" }, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 523.33417, "r_x1": 294.21643, "r_y1": 523.33417, "r_x2": 294.21643, "r_y2": 514.95953, "r_x3": 53.79800000000001, "r_y3": 514.95953, "coord_origin": "TOPLEFT" }, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": 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"r_y2": 345.05338, "r_x3": 328.21185, "r_y3": 345.05338, "coord_origin": "TOPLEFT" }, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.83005, "r_y0": 348.36847, "r_x1": 329.12726, "r_y1": 348.36847, "r_x2": 329.12726, "r_y2": 346.84680000000003, "r_x3": 327.83005, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.77588, "r_y0": 348.36847, "r_x1": 351.18005, "r_y1": 348.36847, "r_x2": 351.18005, "r_y2": 346.84680000000003, "r_x3": 329.77588, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "Make sure that both the images in", "orig": "Make sure that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.25311, "r_y0": 258.86096, "r_x1": 350.07861, "r_y1": 258.86096, "r_x2": 350.07861, "r_y2": 254.94812000000002, "r_x3": 327.25311, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "OPERATION ", "orig": "OPERATION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.07861, "r_y0": 258.68066, "r_x1": 351.82651, "r_y1": 258.68066, "r_x2": 351.82651, "r_y2": 254.76782000000003, "r_x3": 350.07861, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": "(", "orig": "(", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 351.82651, "r_y0": 258.86096, "r_x1": 360.85242, "r_y1": 258.86096, "r_x2": 360.85242, "r_y2": 254.94812000000002, "r_x3": 351.82651, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "cont.", "orig": "cont.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.85242, "r_y0": 258.68066, "r_x1": 362.60028, "r_y1": 258.68066, "r_x2": 362.60028, "r_y2": 254.76782000000003, "r_x3": 360.85242, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 265.23395000000005, "r_x1": 345.84351, "r_y1": 265.23395000000005, "r_x2": 345.84351, "r_y2": 263.49492999999995, "r_x3": 326.88037, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 266.84418000000005, "r_x1": 340.54153, "r_y1": 266.84418000000005, "r_x2": 340.54153, "r_y2": 265.10515999999996, "r_x3": 326.88037, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 268.45441000000005, "r_x1": 328.31903, "r_y1": 268.45441000000005, "r_x2": 328.31903, "r_y2": 266.71533, "r_x3": 326.88037, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.03836, "r_y0": 268.45441000000005, "r_x1": 354.21472, "r_y1": 268.45441000000005, "r_x2": 354.21472, "r_y2": 266.71533, "r_x3": 329.03836, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "There are two objectives. The lower", "orig": "There are two objectives. The lower", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 270.06458, "r_x1": 355.19193, "r_y1": 270.06458, "r_x2": 355.19193, "r_y2": 268.32556, "r_x3": 326.88037, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": " magnification objective has a greater", "orig": " magnification objective has a greater", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 271.6748, "r_x1": 345.80057, "r_y1": 271.6748, "r_x2": 345.80057, "r_y2": 269.93579, "r_x3": 326.88037, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 273.28503, "r_x1": 328.33862, "r_y1": 273.28503, "r_x2": 328.33862, "r_y2": 271.54602, "r_x3": 326.88037, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06775, "r_y0": 273.28503, "r_x1": 352.39969, "r_y1": 273.28503, "r_x2": 352.39969, "r_y2": 271.54602, "r_x3": 329.06775, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "In order to observe the specimen", "orig": "In order to observe the specimen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 274.89526, "r_x1": 352.90042, "r_y1": 274.89526, "r_x2": 352.90042, "r_y2": 273.15619000000004, "r_x3": 326.88037, "r_y3": 273.15619000000004, "coord_origin": "TOPLEFT" }, "text": " easily use the lower magnification", "orig": " easily use the lower magnification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 276.50543000000005, "r_x1": 354.59546, "r_y1": 276.50543000000005, "r_x2": 354.59546, "r_y2": 274.76642000000004, "r_x3": 326.88037, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": " objective first. Then, by rotating the", "orig": " objective first. Then, by rotating the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 278.11566000000005, "r_x1": 350.81885, "r_y1": 278.11566000000005, "r_x2": 350.81885, "r_y2": 276.37665000000004, "r_x3": 326.88037, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": " case, the magnification can be", "orig": " case, the magnification can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 279.72589000000005, "r_x1": 335.46707, "r_y1": 279.72589000000005, "r_x2": 335.46707, "r_y2": 277.98688000000004, "r_x3": 326.88037, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 282.94632, "r_x1": 354.57755, "r_y1": 282.94632, "r_x2": 354.57755, "r_y2": 281.20728, "r_x3": 326.88037, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 284.55652, "r_x1": 335.1752, "r_y1": 284.55652, "r_x2": 335.1752, "r_y2": 282.81750000000005, "r_x3": 326.88037, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 286.16675, "r_x1": 328.34784, "r_y1": 286.16675, "r_x2": 328.34784, "r_y2": 284.4277, "r_x3": 326.88037, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.08157, "r_y0": 286.16675, "r_x1": 354.76245, "r_y1": 286.16675, "r_x2": 354.76245, "r_y2": 284.4277, "r_x3": 329.08157, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "The distance between the observer's", "orig": "The distance between the observer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 287.77695, "r_x1": 354.6499, "r_y1": 287.77695, "r_x2": 354.6499, "r_y2": 286.03793, "r_x3": 326.88037, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " pupils is the interpupillary distance.", "orig": " pupils is the interpupillary distance.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 289.38718, "r_x1": 328.25125, "r_y1": 289.38718, "r_x2": 328.25125, "r_y2": 287.64813, "r_x3": 326.88037, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.93671, "r_y0": 289.38718, "r_x1": 354.29825, "r_y1": 289.38718, "r_x2": 354.29825, "r_y2": 287.64813, "r_x3": 328.93671, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "To adjust the interpupillary distance", "orig": "To adjust the interpupillary distance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 290.99738, "r_x1": 355.02075, "r_y1": 290.99738, "r_x2": 355.02075, "r_y2": 289.25836, "r_x3": 326.88181, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": " rotate the prism caps until both eyes", "orig": " rotate the prism caps until both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 292.6076, "r_x1": 350.82028, "r_y1": 292.6076, "r_x2": 350.82028, "r_y2": 290.86855999999995, "r_x3": 326.88181, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " coincide with the image in the", "orig": " coincide with the image in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 294.2178, "r_x1": 336.2067, "r_y1": 294.2178, "r_x2": 336.2067, "r_y2": 292.47879, "r_x3": 326.88181, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 297.43823, "r_x1": 335.3941, "r_y1": 297.43823, "r_x2": 335.3941, "r_y2": 295.69922, "r_x3": 326.88181, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 299.04846, "r_x1": 328.34314, "r_y1": 299.04846, "r_x2": 328.34314, "r_y2": 297.30942, "r_x3": 326.88181, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.07379, "r_y0": 299.04846, "r_x1": 353.18555, "r_y1": 299.04846, "r_x2": 353.18555, "r_y2": 297.30942, "r_x3": 329.07379, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "Remove the lens protective cover.", "orig": "Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 300.65866, "r_x1": 328.35919, "r_y1": 300.65866, "r_x2": 328.35919, "r_y2": 298.91965, "r_x3": 326.88324, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.0972, "r_y0": 300.65866, "r_x1": 353.45065, "r_y1": 300.65866, "r_x2": 353.45065, "r_y2": 298.91965, "r_x3": 329.0972, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "Place the specimen on the working", "orig": "Place the specimen on the working", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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329.02783, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": "Focus the specimen with the left eye", "orig": "Focus the specimen with the left eye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 305.48932, "r_x1": 355.96307, "r_y1": 305.48932, "r_x2": 355.96307, "r_y2": 303.75027, "r_x3": 326.88324, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " first while turning the focus knob until", "orig": " first while turning the focus knob until", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 307.09952000000004, "r_x1": 354.46594, "r_y1": 307.09952000000004, "r_x2": 354.46594, "r_y2": 305.3605, "r_x3": 326.88324, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 308.70975, "r_x1": 328.25488, "r_y1": 308.70975, "r_x2": 328.25488, "r_y2": 306.9707, "r_x3": 326.88324, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.9407, "r_y0": 308.70975, "r_x1": 356.37335, "r_y1": 308.70975, "r_x2": 356.37335, "r_y2": 306.9707, "r_x3": 328.9407, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "Rotate the right eyepiece ring until the", "orig": "Rotate the right eyepiece ring until the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 310.31995, "r_x1": 355.38867, "r_y1": 310.31995, "r_x2": 355.38867, "r_y2": 308.58093, "r_x3": 326.88324, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " images in each eyepiece coincide and", "orig": " images in each eyepiece coincide and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 311.93018, "r_x1": 343.17249, "r_y1": 311.93018, "r_x2": 343.17249, "r_y2": 310.19113, "r_x3": 326.88324, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 315.15059999999994, "r_x1": 344.13388, "r_y1": 315.15059999999994, "r_x2": 344.13388, "r_y2": 313.41156, "r_x3": 326.88324, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 316.76079999999996, "r_x1": 328.37418, "r_y1": 316.76079999999996, "r_x2": 328.37418, "r_y2": 315.02178999999995, "r_x3": 326.88324, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.11963, "r_y0": 316.76079999999996, "r_x1": 348.50162, "r_y1": 316.76079999999996, "r_x2": 348.50162, "r_y2": 315.02178999999995, "r_x3": 329.11963, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord.", "orig": "Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 318.37103, "r_x1": 328.34061, "r_y1": 318.37103, "r_x2": 328.34061, "r_y2": 316.63199, "r_x3": 326.88324, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06931, "r_y0": 318.37103, "r_x1": 353.11588, "r_y1": 318.37103, "r_x2": 353.11588, "r_y2": 316.63199, "r_x3": 329.06931, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "When the bulb is cool, remove the", "orig": "When the bulb is cool, remove the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 319.9812299999999, "r_x1": 353.79517, "r_y1": 319.9812299999999, "r_x2": 353.79517, "r_y2": 318.2422199999999, "r_x3": 326.88464, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " oblique illuminator cap and remove", "orig": " oblique illuminator cap and remove", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 321.59146, "r_x1": 348.02094, "r_y1": 321.59146, "r_x2": 348.02094, "r_y2": 319.85242000000005, "r_x3": 326.88464, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 323.20166, "r_x1": 328.37512, "r_y1": 323.20166, "r_x2": 328.37512, "r_y2": 321.46265, "r_x3": 326.88464, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 139, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.12036, "r_y0": 323.20166, "r_x1": 352.96808, "r_y1": 323.20166, "r_x2": 352.96808, "r_y2": 321.46265, "r_x3": 329.12036, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "Replace with a new halogen bulb.", "orig": "Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 140, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 324.81189, "r_x1": 328.36884, "r_y1": 324.81189, "r_x2": 328.36884, "r_y2": 323.07285, "r_x3": 326.88608, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 141, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.1102, "r_y0": 324.81189, "r_x1": 356.5412, "r_y1": 324.81189, "r_x2": 356.5412, "r_y2": 323.07285, "r_x3": 329.1102, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "Open the window in the base plate and", "orig": "Open the window in the base plate and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 326.42209, "r_x1": 350.13828, "r_y1": 326.42209, "r_x2": 350.13828, "r_y2": 324.68307000000004, "r_x3": 326.88608, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 328.03232, "r_x1": 351.59677, "r_y1": 328.03232, "r_x2": 351.59677, "r_y2": 326.29327, "r_x3": 326.88608, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 329.64252, "r_x1": 336.89197, "r_y1": 329.64252, "r_x2": 336.89197, "r_y2": 327.9035, "r_x3": 326.88608, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " illuminator.", "orig": " illuminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 145, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 265.23395000000005, "r_x1": 366.93256, "r_y1": 265.23395000000005, "r_x2": 366.93256, "r_y2": 263.49492999999995, "r_x3": 358.42023, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 146, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 266.84418000000005, "r_x1": 359.89841, "r_y1": 266.84418000000005, "r_x2": 359.89841, "r_y2": 265.10515999999996, "r_x3": 358.42023, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 386.70093, "r_y1": 290.99738, "r_x2": 386.70093, "r_y2": 289.25836, "r_x3": 360.49353, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "If the image is not clear to both eyes", "orig": "If the image is not clear to both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 292.6076, "r_x1": 388.03534, "r_y1": 292.6076, "r_x2": 388.03534, "r_y2": 290.86855999999995, "r_x3": 358.42453, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " at the same time, the diopter ring may", "orig": " at the same time, the diopter ring may", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 294.2178, "r_x1": 373.13724, "r_y1": 294.2178, "r_x2": 373.13724, "r_y2": 292.47879, "r_x3": 358.42453, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 297.43823, "r_x1": 381.74539, "r_y1": 297.43823, "r_x2": 381.74539, "r_y2": 295.69922, "r_x3": 358.42453, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 299.04846, "r_x1": 359.83682, "r_y1": 299.04846, "r_x2": 359.83682, "r_y2": 297.30942, "r_x3": 358.42453, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.54297, "r_y0": 299.04846, "r_x1": 388.08289, "r_y1": 299.04846, "r_x2": 388.08289, "r_y2": 297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 302.26889000000006, "r_x1": 387.72266, "r_y1": 302.26889000000006, "r_x2": 387.72266, "r_y2": 300.52985, "r_x3": 358.42453, "r_y3": 300.52985, "coord_origin": "TOPLEFT" }, "text": " differences in acuity between the right", "orig": " differences in acuity between the right", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 303.8790900000001, "r_x1": 384.1991, "r_y1": 303.8790900000001, "r_x2": 384.1991, "r_y2": 302.14008000000007, "r_x3": 358.42453, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": " and left eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 328.03232, "r_x1": 385.20682, "r_y1": 328.03232, "r_x2": 385.20682, "r_y2": 326.29327, "r_x3": 358.42596, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " and right eyepieces, then before", "orig": " and right eyepieces, then before", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 329.64252, "r_x1": 382.21964, "r_y1": 329.64252, "r_x2": 382.21964, "r_y2": 327.9035, "r_x3": 358.42596, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " viewing set the diopter ring", "orig": " viewing set the diopter ring", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 331.25275, "r_x1": 382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.64169, "r_y0": 336.08337, "r_x1": 385.75333, "r_y1": 336.08337, "r_x2": 385.75333, "r_y2": 334.34436, "r_x3": 360.64169, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord from the", "orig": "Disconnect the power cord from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 337.6936, "r_x1": 372.01416, "r_y1": 337.6936, "r_x2": 372.01416, "r_y2": 335.95456, "r_x3": 358.42596, "r_y3": 335.95456, "coord_origin": "TOPLEFT" }, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 339.3038, "r_x1": 359.88327, "r_y1": 339.3038, "r_x2": 359.88327, "r_y2": 337.56479, "r_x3": 358.42596, "r_y3": 337.56479, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.61191, "r_y0": 339.3038, "r_x1": 384.65726, "r_y1": 339.3038, "r_x2": 384.65726, "r_y2": 337.56479, "r_x3": 360.61191, "r_y3": 337.56479, "coord_origin": "TOPLEFT" }, "text": "When the bulb is cool, remove the", "orig": "When the bulb is cool, remove the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 340.9140300000001, "r_x1": 385.33649, "r_y1": 340.9140300000001, "r_x2": 385.33649, "r_y2": 339.17499, "r_x3": 358.42596, "r_y3": 339.17499, "coord_origin": "TOPLEFT" }, "text": " oblique illuminator cap and remove", "orig": " oblique illuminator 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449.10074000000003, "r_y3": 378.66302, "coord_origin": "TOPLEFT" }, "text": "Circling Minimums", "orig": "Circling Minimums", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 252, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 385.34148999999996, "r_x1": 449.64444, "r_y1": 385.34148999999996, "r_x2": 449.64444, "r_y2": 383.2203999999999, "r_x3": 449.10074000000003, "r_y3": 383.2203999999999, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 253, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.18811, "r_y0": 385.34148999999996, "r_x1": 550.77124, "r_y1": 385.34148999999996, "r_x2": 550.77124, "r_y2": 383.2203999999999, "r_x3": 450.18811, "r_y3": 383.2203999999999, "coord_origin": "TOPLEFT" }, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "orig": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 254, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 387.87842, "r_x1": 536.14716, "r_y1": 387.87842, "r_x2": 536.14716, "r_y2": 385.75732, "r_x3": 449.10074000000003, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 255, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 538.31085, "r_y0": 387.87842, "r_x1": 549.49921, "r_y1": 387.87842, "r_x2": 549.49921, "r_y2": 385.75732, "r_x3": 538.31085, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 256, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 390.1571, "r_x1": 547.58185, "r_y1": 390.1571, "r_x2": 547.58185, "r_y2": 388.03601, "r_x3": 449.10074000000003, "r_y3": 388.03601, "coord_origin": "TOPLEFT" }, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 257, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 395.33386, "r_x1": 449.6163, "r_y1": 395.33386, "r_x2": 449.6163, "r_y2": 393.2128000000001, "r_x3": 449.10074000000003, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 258, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.1319, "r_y0": 395.33386, "r_x1": 529.53082, "r_y1": 395.33386, "r_x2": 529.53082, "r_y2": 393.2128000000001, "r_x3": 450.1319, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 259, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 532.05829, "r_y0": 395.33386, "r_x1": 550.42261, "r_y1": 395.33386, "r_x2": 550.42261, "r_y2": 393.2128000000001, "r_x3": 532.05829, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": " on the circling line of ", "orig": " on the circling line of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 260, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 397.61255, "r_x1": 455.74692, "r_y1": 397.61255, "r_x2": 455.74692, "r_y2": 395.49149, "r_x3": 449.10074000000003, "r_y3": 395.49149, "coord_origin": "TOPLEFT" }, "text": "minima.", "orig": "minima.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 261, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 417.50446, "r_x1": 496.2829, "r_y1": 417.50446, "r_x2": 496.2829, "r_y2": 415.59549, "r_x3": 449.95525999999995, "r_y3": 415.59549, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 262, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 411.1644, "r_x1": 551.16101, "r_y1": 411.1644, "r_x2": 551.16101, "r_y2": 409.25543, "r_x3": 501.13077, "r_y3": 409.25543, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 264, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 422.73331, "r_x1": 469.35599, "r_y1": 422.73331, "r_x2": 469.35599, "r_y2": 420.18802, "r_x3": 449.10074000000003, "r_y3": 420.18802, "coord_origin": "TOPLEFT" }, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 265, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 427.21017, "r_x1": 525.93616, "r_y1": 427.21017, "r_x2": 525.93616, "r_y2": 425.08908, "r_x3": 449.10074000000003, "r_y3": 425.08908, "coord_origin": "TOPLEFT" }, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 266, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 429.48886, "r_x1": 522.0343, "r_y1": 429.48886, "r_x2": 522.0343, "r_y2": 427.3678, "r_x3": 449.10074000000003, "r_y3": 427.3678, "coord_origin": "TOPLEFT" }, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 267, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 431.76755, "r_x1": 524.67151, "r_y1": 431.76755, "r_x2": 524.67151, "r_y2": 429.64648, "r_x3": 449.10074000000003, "r_y3": 429.64648, "coord_origin": "TOPLEFT" }, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 268, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 434.04623, "r_x1": 527.172, "r_y1": 434.04623, "r_x2": 527.172, "r_y2": 431.92514000000006, "r_x3": 449.10074000000003, "r_y3": 431.92514000000006, "coord_origin": "TOPLEFT" }, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 269, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 436.32492, "r_x1": 502.39545, "r_y1": 436.32492, "r_x2": 502.39545, "r_y2": 434.20383, "r_x3": 449.10074000000003, "r_y3": 434.20383, "coord_origin": "TOPLEFT" }, "text": "dimensions (length and width) are shown for all active runways.", "orig": "dimensions (length and width) are shown for all active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 270, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 440.88228999999995, "r_x1": 512.92676, "r_y1": 440.88228999999995, "r_x2": 512.92676, "r_y2": 438.7611999999999, "r_x3": 449.10074000000003, "r_y3": 438.7611999999999, "coord_origin": "TOPLEFT" }, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 271, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 445.97900000000004, "r_x1": 460.02307, "r_y1": 445.97900000000004, "r_x2": 460.02307, "r_y2": 444.07001, "r_x3": 449.95525999999995, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 272, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 445.97900000000004, "r_x1": 473.98819, "r_y1": 445.97900000000004, "r_x2": 473.98819, "r_y2": 444.07001, "r_x3": 464.89963, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 273, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 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Circling only approaches will not show a TDZE.", "orig": "the landing surface. 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"r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.75735, "r_y0": 387.10205, "r_x1": 341.16534, "r_y1": 387.10205, "r_x2": 341.16534, "r_y2": 384.84552, "r_x3": 331.75735, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "August 2012 ", "orig": "August 2012 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 304, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.4722, "r_y0": 387.10205, "r_x1": 372.90613, "r_y1": 387.10205, "r_x2": 372.90613, "r_y2": 384.84552, "r_x3": 350.4722, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "Ex-dividend trading commences", "orig": "Ex-dividend trading commences", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 305, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 389.64483999999993, "r_x1": 330.20337, "r_y1": 389.64483999999993, "r_x2": 330.20337, "r_y2": 387.38828, "r_x3": 329.40536, "r_y3": 387.38828, 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400.46155, "coord_origin": "TOPLEFT" }, "text": "Indicative dates only, subject to change/Board confi rmation", "orig": "Indicative dates only, subject to change/Board confi rmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 324, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 406.60156, "r_x1": 391.771, "r_y1": 406.60156, "r_x2": 391.771, "r_y2": 404.34503, "r_x3": 329.40536, "r_y3": 404.34503, "coord_origin": "TOPLEFT" }, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 325, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 408.63513000000006, "r_x1": 369.65308, "r_y1": 408.63513000000006, "r_x2": 369.65308, "r_y2": 406.37857, 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", "orig": "electricity needs of Australian households. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 332, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 479.13998, "r_x1": 394.11481, "r_y1": 479.13998, "r_x2": 394.11481, "r_y2": 476.88345, "r_x3": 363.54486, "r_y3": 476.88345, "coord_origin": "TOPLEFT" }, "text": "In 1841, when AGL supplied the gas to light ", "orig": "In 1841, when AGL supplied the gas to light ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 333, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 481.17352, "r_x1": 393.75891, "r_y1": 481.17352, "r_x2": 393.75891, "r_y2": 478.91699, "r_x3": 363.54486, "r_y3": 478.91699, "coord_origin": "TOPLEFT" }, "text": "the fi rst public street lamp, it was reported ", "orig": "the fi rst public street lamp, it was reported ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 334, "rgba": { "r": 0, 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}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 352, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 280.9357, "r_x1": 505.64642000000003, "r_y1": 280.9357, "r_x2": 505.64642000000003, "r_y2": 277.34619, "r_x3": 498.15335, "r_y3": 277.34619, "coord_origin": "TOPLEFT" }, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 353, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 283.85983, "r_x1": 543.92957, "r_y1": 283.85983, "r_x2": 543.92957, "r_y2": 281.82187, "r_x3": 498.15335, "r_y3": 281.82187, "coord_origin": "TOPLEFT" }, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 354, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 285.90289, "r_x1": 545.67834, "r_y1": 285.90289, "r_x2": 545.67834, "r_y2": 283.8649, "r_x3": 498.15335, "r_y3": 283.8649, "coord_origin": "TOPLEFT" }, "text": "the messages printed on them. 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511.69116, "r_y2": 357.95056, "r_x3": 499.39645, "r_y3": 357.95056, "coord_origin": "TOPLEFT" }, "text": "construction zones", "orig": "construction zones", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 375, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 516.75891, "r_y0": 358.0834, "r_x1": 527.42938, "r_y1": 358.0834, "r_x2": 527.42938, "r_y2": 356.26297000000005, "r_x3": 516.75891, "r_y3": 356.26297000000005, "coord_origin": "TOPLEFT" }, "text": "Railway crossing", "orig": "Railway crossing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 376, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 534.5141, "r_y0": 354.75024, "r_x1": 547.89862, "r_y1": 354.75024, "r_x2": 547.89862, "r_y2": 352.92981, "r_x3": 534.5141, "r_y3": 352.92981, "coord_origin": "TOPLEFT" }, "text": "Shows distance and ", "orig": "Shows distance and ", "text_direction": "left_to_right", "confidence": 1.0, 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270.14075, "coord_origin": "TOPLEFT" }, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 380, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 274.34888, "r_x1": 492.31219, "r_y1": 274.34888, "r_x2": 492.31219, "r_y2": 272.84717, "r_x3": 479.97293, "r_y3": 272.84717, "coord_origin": "TOPLEFT" }, "text": "- regulatory signs", "orig": "- regulatory signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 381, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 276.64679, "r_x1": 486.72598000000005, "r_y1": 276.64679, "r_x2": 486.72598000000005, "r_y2": 275.14513999999997, "r_x3": 479.97293, "r_y3": 275.14513999999997, "coord_origin": "TOPLEFT" }, "text": "- school, ", "orig": "- school, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 382, "rgba": { "r": 0, "g": 0, "b": 0, "a": 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"coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 208, "label": "text", "bbox": { "l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 209, "label": "text", "bbox": { "l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 210, "label": "text", "bbox": { "l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 211, "label": "text", "bbox": { "l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 212, "label": "text", "bbox": { "l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 213, "label": "text", "bbox": { "l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 214, "label": "text", "bbox": { "l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 215, "label": "text", "bbox": { "l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 216, "label": "text", "bbox": { "l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 217, "label": "text", "bbox": { "l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 218, "label": "text", "bbox": { "l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 219, "label": "text", "bbox": { "l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 220, "label": "text", "bbox": { "l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 221, "label": "text", "bbox": { "l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 222, "label": "text", "bbox": { "l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 223, "label": "text", "bbox": { "l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 224, "label": "text", "bbox": { "l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 225, "label": "text", "bbox": { "l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 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297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 231, "label": "text", "bbox": { "l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 232, "label": "text", "bbox": { "l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": 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eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 234, "label": "text", "bbox": { "l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 235, "label": "text", "bbox": { "l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 236, "label": "text", "bbox": { "l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 237, "label": "text", "bbox": { "l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 238, "label": "text", "bbox": { "l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 239, "label": "text", "bbox": { "l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 240, "label": "text", "bbox": { "l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 241, "label": "text", "bbox": { "l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 242, "label": "text", "bbox": { "l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 243, "label": "text", "bbox": { "l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 244, "label": "text", "bbox": { "l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 245, "label": "text", "bbox": { "l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 246, "label": "text", "bbox": { "l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 247, "label": "text", "bbox": { "l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 248, "label": "text", "bbox": { "l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 249, "label": "text", "bbox": { "l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 250, "label": "text", "bbox": { "l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 190, "rgba": { 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}, { "id": 252, "label": "text", "bbox": { "l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 331.25275, "r_x1": 382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 253, "label": "text", "bbox": { "l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 254, "label": "text", "bbox": { "l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 255, "label": "text", "bbox": { "l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { 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"coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 323, "label": "text", "bbox": { "l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 324, "label": "text", "bbox": { "l": 449.10074000000003, 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 405, "label": "text", "bbox": { "l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 406, "label": "text", "bbox": { "l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 407, "label": "text", "bbox": { "l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 293.19717, "r_x1": 295.34586, "r_y1": 293.19717, "r_x2": 295.34586, "r_y2": 284.82254, "r_x3": 53.79800000000001, "r_y3": 284.82254, "coord_origin": "TOPLEFT" }, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 304.15616000000006, "r_x1": 294.04709, "r_y1": 304.15616000000006, "r_x2": 294.04709, "r_y2": 295.78152, "r_x3": 53.79800000000001, "r_y3": 295.78152, "coord_origin": "TOPLEFT" }, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 315.11517, "r_x1": 294.04645, "r_y1": 315.11517, "r_x2": 294.04645, "r_y2": 306.74053999999995, "r_x3": 53.79800000000001, "r_y3": 306.74053999999995, "coord_origin": "TOPLEFT" }, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 326.07416, "r_x1": 294.27573, "r_y1": 326.07416, "r_x2": 294.27573, "r_y2": 317.69952, "r_x3": 53.79800000000001, "r_y3": 317.69952, "coord_origin": "TOPLEFT" }, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 337.03317, "r_x1": 294.04712, "r_y1": 337.03317, "r_x2": 294.04712, "r_y2": 328.65854, "r_x3": 53.79800000000001, "r_y3": 328.65854, "coord_origin": "TOPLEFT" }, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 347.99219, "r_x1": 294.0437, "r_y1": 347.99219, "r_x2": 294.0437, "r_y2": 339.61755, "r_x3": 53.79800000000001, "r_y3": 339.61755, "coord_origin": "TOPLEFT" }, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 358.95117, "r_x1": 294.04715, "r_y1": 358.95117, "r_x2": 294.04715, "r_y2": 350.57654, "r_x3": 53.79800000000001, "r_y3": 350.57654, "coord_origin": "TOPLEFT" }, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 369.90918000000005, "r_x1": 294.04364, "r_y1": 369.90918000000005, "r_x2": 294.04364, "r_y2": 361.53455, "r_x3": 53.79800000000001, "r_y3": 361.53455, "coord_origin": "TOPLEFT" }, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 380.87714000000005, "r_x1": 92.863388, "r_y1": 380.87714000000005, "r_x2": 92.863388, "r_y2": 372.53839, "r_x3": 53.79800000000001, "r_y3": 372.53839, "coord_origin": "TOPLEFT" }, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 92.863998, "r_y0": 380.86816, "r_x1": 294.04361, "r_y1": 380.86816, "r_x2": 294.04361, "r_y2": 372.49353, "r_x3": 92.863998, "r_y3": 372.49353, "coord_origin": "TOPLEFT" }, "text": ", a new, publicly available, document-layout annotation", "orig": ", a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 391.82718, "r_x1": 294.04718, "r_y1": 391.82718, "r_x2": 294.04718, "r_y2": 383.45255, "r_x3": 53.79800000000001, "r_y3": 383.45255, "coord_origin": "TOPLEFT" }, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 402.78616, "r_x1": 294.0437, "r_y1": 402.78616, "r_x2": 294.0437, "r_y2": 394.41153, "r_x3": 53.79800000000001, "r_y3": 394.41153, "coord_origin": "TOPLEFT" }, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 413.74518, "r_x1": 294.04535, "r_y1": 413.74518, "r_x2": 294.04535, "r_y2": 405.37054, "r_x3": 53.79800000000001, "r_y3": 405.37054, "coord_origin": "TOPLEFT" }, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 424.70416000000006, "r_x1": 294.04715, "r_y1": 424.70416000000006, "r_x2": 294.04715, "r_y2": 416.32953, "r_x3": 53.79800000000001, "r_y3": 416.32953, "coord_origin": "TOPLEFT" }, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 435.66318, "r_x1": 294.04712, "r_y1": 435.66318, "r_x2": 294.04712, "r_y2": 427.28853999999995, "r_x3": 53.79800000000001, "r_y3": 427.28853999999995, "coord_origin": "TOPLEFT" }, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 446.62216, "r_x1": 295.03, "r_y1": 446.62216, "r_x2": 295.03, "r_y2": 438.24753, "r_x3": 53.79800000000001, "r_y3": 438.24753, "coord_origin": "TOPLEFT" }, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 457.58118, "r_x1": 294.21616, "r_y1": 457.58118, "r_x2": 294.21616, "r_y2": 449.20654, "r_x3": 53.466999, "r_y3": 449.20654, "coord_origin": "TOPLEFT" }, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 468.54016, "r_x1": 294.04712, "r_y1": 468.54016, "r_x2": 294.04712, "r_y2": 460.16553, "r_x3": 53.79800000000001, "r_y3": 460.16553, "coord_origin": "TOPLEFT" }, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 479.49817, "r_x1": 295.56018, "r_y1": 479.49817, "r_x2": 295.56018, "r_y2": 471.12354, "r_x3": 53.79800000000001, "r_y3": 471.12354, "coord_origin": "TOPLEFT" }, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 490.45718, "r_x1": 295.42783, "r_y1": 490.45718, "r_x2": 295.42783, "r_y2": 482.08255, "r_x3": 53.79800000000001, "r_y3": 482.08255, "coord_origin": "TOPLEFT" }, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 501.41617, "r_x1": 294.04715, "r_y1": 501.41617, "r_x2": 294.04715, "r_y2": 493.04153, "r_x3": 53.79800000000001, "r_y3": 493.04153, "coord_origin": "TOPLEFT" }, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 512.37518, "r_x1": 295.55618, "r_y1": 512.37518, "r_x2": 295.55618, "r_y2": 504.00055, "r_x3": 53.79800000000001, "r_y3": 504.00055, "coord_origin": "TOPLEFT" }, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 523.33417, "r_x1": 294.21643, "r_y1": 523.33417, "r_x2": 294.21643, "r_y2": 514.95953, "r_x3": 53.79800000000001, "r_y3": 514.95953, "coord_origin": "TOPLEFT" }, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 534.29318, "r_x1": 212.05495, "r_y1": 534.29318, "r_x2": 212.05495, "r_y2": 525.91855, "r_x3": 53.79800000000001, "r_y3": 525.91855, "coord_origin": "TOPLEFT" }, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis." }, { "label": "section_header", "id": 4, "page_no": 0, "cluster": { "id": 4, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT" }, "confidence": 0.9149598479270935, "cells": [ { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 561.30602, "r_x1": 134.81989, "r_y1": 561.30602, "r_x2": 134.81989, "r_y2": 550.99692, "r_x3": 53.79800000000001, "r_y3": 550.99692, "coord_origin": "TOPLEFT" }, "text": "CCS CONCEPTS", "orig": "CCS CONCEPTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "CCS CONCEPTS" }, { "label": "text", "id": 11, "page_no": 0, "cluster": { "id": 11, "label": "text", "bbox": { "l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT" }, "confidence": 0.8458839058876038, "cells": [ { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.57419, "r_x1": 56.945206000000006, "r_y1": 574.57419, "r_x2": 56.945206000000006, "r_y2": 566.19957, "r_x3": 53.79800000000001, "r_y3": 566.19957, "coord_origin": "TOPLEFT" }, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 58.440002, "r_y0": 574.5562600000001, "r_x1": 142.4462, "r_y1": 574.5562600000001, "r_x2": 142.4462, "r_y2": 566.0830100000001, "r_x3": 58.440002, "r_y3": 566.0830100000001, "coord_origin": "TOPLEFT" }, "text": "Information systems", "orig": "Information systems", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 143.938, "r_y0": 574.43073, "r_x1": 153.15099, "r_y1": 574.43073, "r_x2": 153.15099, "r_y2": 566.36096, "r_x3": 143.938, "r_y3": 566.36096, "coord_origin": "TOPLEFT" }, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 154.646, "r_y0": 574.5562600000001, "r_x1": 235.46015999999997, "r_y1": 574.5562600000001, "r_x2": 235.46015999999997, "r_y2": 566.0830100000001, "r_x3": 154.646, "r_y3": 566.0830100000001, "coord_origin": "TOPLEFT" }, "text": "Document structure", "orig": "Document structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 235.45700000000002, "r_y0": 574.57417, "r_x1": 242.17419, "r_y1": 574.57417, "r_x2": 242.17419, "r_y2": 566.19955, "r_x3": 235.45700000000002, "r_y3": 566.19955, "coord_origin": "TOPLEFT" }, "text": "; 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Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.79189, "r_x1": 187.72285, "r_y1": 672.79189, "r_x2": 187.72285, "r_y2": 666.27837, "r_x3": 53.79800000000001, "r_y3": 666.27837, "coord_origin": "TOPLEFT" }, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. 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343.71207, "r_x1": 354.9267, "r_y1": 343.71207, "r_x2": 354.9267, "r_y2": 342.19043000000005, "r_x3": 329.72168, "r_y3": 342.19043000000005, "coord_origin": "TOPLEFT" }, "text": "Loosen the retention screw, then rotate ", "orig": "Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.8313, "r_y0": 345.14355, "r_x1": 351.66949, "r_y1": 345.14355, "r_x2": 351.66949, "r_y2": 343.62192, "r_x3": 327.8313, "r_y3": 343.62192, "coord_origin": "TOPLEFT" }, "text": " the adjustment ring to change the ", "orig": " the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.21185, "r_y0": 346.57504, "r_x1": 346.33179, "r_y1": 346.57504, "r_x2": 346.33179, "r_y2": 345.05338, "r_x3": 328.21185, "r_y3": 345.05338, "coord_origin": "TOPLEFT" }, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.83005, "r_y0": 348.36847, "r_x1": 329.12726, "r_y1": 348.36847, "r_x2": 329.12726, "r_y2": 346.84680000000003, "r_x3": 327.83005, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.77588, "r_y0": 348.36847, "r_x1": 351.18005, "r_y1": 348.36847, "r_x2": 351.18005, "r_y2": 346.84680000000003, "r_x3": 329.77588, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "Make sure that both the images in", "orig": "Make sure that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.25311, "r_y0": 258.86096, "r_x1": 350.07861, "r_y1": 258.86096, "r_x2": 350.07861, "r_y2": 254.94812000000002, "r_x3": 327.25311, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "OPERATION ", "orig": "OPERATION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.07861, "r_y0": 258.68066, "r_x1": 351.82651, "r_y1": 258.68066, "r_x2": 351.82651, "r_y2": 254.76782000000003, "r_x3": 350.07861, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": "(", "orig": "(", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 351.82651, "r_y0": 258.86096, "r_x1": 360.85242, "r_y1": 258.86096, "r_x2": 360.85242, "r_y2": 254.94812000000002, "r_x3": 351.82651, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "cont.", "orig": "cont.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.85242, "r_y0": 258.68066, "r_x1": 362.60028, "r_y1": 258.68066, "r_x2": 362.60028, "r_y2": 254.76782000000003, "r_x3": 360.85242, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 265.23395000000005, "r_x1": 345.84351, "r_y1": 265.23395000000005, "r_x2": 345.84351, "r_y2": 263.49492999999995, "r_x3": 326.88037, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 266.84418000000005, "r_x1": 340.54153, "r_y1": 266.84418000000005, "r_x2": 340.54153, "r_y2": 265.10515999999996, "r_x3": 326.88037, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 268.45441000000005, "r_x1": 328.31903, "r_y1": 268.45441000000005, "r_x2": 328.31903, "r_y2": 266.71533, "r_x3": 326.88037, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.03836, "r_y0": 268.45441000000005, "r_x1": 354.21472, "r_y1": 268.45441000000005, "r_x2": 354.21472, "r_y2": 266.71533, "r_x3": 329.03836, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "There are two objectives. The lower", "orig": "There are two objectives. The lower", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 270.06458, "r_x1": 355.19193, "r_y1": 270.06458, "r_x2": 355.19193, "r_y2": 268.32556, "r_x3": 326.88037, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": " magnification objective has a greater", "orig": " magnification objective has a greater", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 271.6748, "r_x1": 345.80057, "r_y1": 271.6748, "r_x2": 345.80057, "r_y2": 269.93579, "r_x3": 326.88037, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 273.28503, "r_x1": 328.33862, "r_y1": 273.28503, "r_x2": 328.33862, "r_y2": 271.54602, "r_x3": 326.88037, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06775, "r_y0": 273.28503, "r_x1": 352.39969, "r_y1": 273.28503, "r_x2": 352.39969, "r_y2": 271.54602, "r_x3": 329.06775, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "In order to observe the specimen", "orig": "In order to observe the specimen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 274.89526, "r_x1": 352.90042, "r_y1": 274.89526, "r_x2": 352.90042, "r_y2": 273.15619000000004, "r_x3": 326.88037, "r_y3": 273.15619000000004, "coord_origin": "TOPLEFT" }, "text": " easily use the lower magnification", "orig": " easily use the lower magnification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 276.50543000000005, "r_x1": 354.59546, "r_y1": 276.50543000000005, "r_x2": 354.59546, "r_y2": 274.76642000000004, "r_x3": 326.88037, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": " objective first. Then, by rotating the", "orig": " objective first. Then, by rotating the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 278.11566000000005, "r_x1": 350.81885, "r_y1": 278.11566000000005, "r_x2": 350.81885, "r_y2": 276.37665000000004, "r_x3": 326.88037, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": " case, the magnification can be", "orig": " case, the magnification can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 279.72589000000005, "r_x1": 335.46707, "r_y1": 279.72589000000005, "r_x2": 335.46707, "r_y2": 277.98688000000004, "r_x3": 326.88037, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 282.94632, "r_x1": 354.57755, "r_y1": 282.94632, "r_x2": 354.57755, "r_y2": 281.20728, "r_x3": 326.88037, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 284.55652, "r_x1": 335.1752, "r_y1": 284.55652, "r_x2": 335.1752, "r_y2": 282.81750000000005, "r_x3": 326.88037, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 286.16675, "r_x1": 328.34784, "r_y1": 286.16675, "r_x2": 328.34784, "r_y2": 284.4277, "r_x3": 326.88037, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.08157, "r_y0": 286.16675, "r_x1": 354.76245, "r_y1": 286.16675, "r_x2": 354.76245, "r_y2": 284.4277, "r_x3": 329.08157, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "The distance between the observer's", "orig": "The distance between the observer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 287.77695, "r_x1": 354.6499, "r_y1": 287.77695, "r_x2": 354.6499, "r_y2": 286.03793, "r_x3": 326.88037, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " pupils is the interpupillary distance.", "orig": " pupils is the interpupillary distance.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 289.38718, "r_x1": 328.25125, "r_y1": 289.38718, "r_x2": 328.25125, "r_y2": 287.64813, "r_x3": 326.88037, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.93671, "r_y0": 289.38718, "r_x1": 354.29825, "r_y1": 289.38718, "r_x2": 354.29825, "r_y2": 287.64813, "r_x3": 328.93671, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "To adjust the interpupillary distance", "orig": "To adjust the interpupillary distance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 290.99738, "r_x1": 355.02075, "r_y1": 290.99738, "r_x2": 355.02075, "r_y2": 289.25836, "r_x3": 326.88181, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": " rotate the prism caps until both eyes", "orig": " rotate the prism caps until both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 292.6076, "r_x1": 350.82028, "r_y1": 292.6076, "r_x2": 350.82028, "r_y2": 290.86855999999995, "r_x3": 326.88181, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " coincide with the image in the", "orig": " coincide with the image in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 294.2178, "r_x1": 336.2067, "r_y1": 294.2178, "r_x2": 336.2067, "r_y2": 292.47879, "r_x3": 326.88181, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 297.43823, "r_x1": 335.3941, "r_y1": 297.43823, "r_x2": 335.3941, "r_y2": 295.69922, "r_x3": 326.88181, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 299.04846, "r_x1": 328.34314, "r_y1": 299.04846, "r_x2": 328.34314, "r_y2": 297.30942, "r_x3": 326.88181, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.07379, "r_y0": 299.04846, "r_x1": 353.18555, "r_y1": 299.04846, "r_x2": 353.18555, "r_y2": 297.30942, "r_x3": 329.07379, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "Remove the lens protective cover.", "orig": "Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 300.65866, "r_x1": 328.35919, "r_y1": 300.65866, "r_x2": 328.35919, "r_y2": 298.91965, "r_x3": 326.88324, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.0972, "r_y0": 300.65866, "r_x1": 353.45065, "r_y1": 300.65866, "r_x2": 353.45065, "r_y2": 298.91965, "r_x3": 329.0972, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "Place the specimen on the working", "orig": "Place the specimen on the working", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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329.02783, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": "Focus the specimen with the left eye", "orig": "Focus the specimen with the left eye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 305.48932, "r_x1": 355.96307, "r_y1": 305.48932, "r_x2": 355.96307, "r_y2": 303.75027, "r_x3": 326.88324, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " first while turning the focus knob until", "orig": " first while turning the focus knob until", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 307.09952000000004, "r_x1": 354.46594, "r_y1": 307.09952000000004, "r_x2": 354.46594, "r_y2": 305.3605, "r_x3": 326.88324, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 308.70975, "r_x1": 328.25488, "r_y1": 308.70975, "r_x2": 328.25488, "r_y2": 306.9707, "r_x3": 326.88324, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.9407, "r_y0": 308.70975, "r_x1": 356.37335, "r_y1": 308.70975, "r_x2": 356.37335, "r_y2": 306.9707, "r_x3": 328.9407, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "Rotate the right eyepiece ring until the", "orig": "Rotate the right eyepiece ring until the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 310.31995, "r_x1": 355.38867, "r_y1": 310.31995, "r_x2": 355.38867, "r_y2": 308.58093, "r_x3": 326.88324, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " images in each eyepiece coincide and", "orig": " images in each eyepiece coincide and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 311.93018, "r_x1": 343.17249, "r_y1": 311.93018, "r_x2": 343.17249, "r_y2": 310.19113, "r_x3": 326.88324, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 315.15059999999994, "r_x1": 344.13388, "r_y1": 315.15059999999994, "r_x2": 344.13388, "r_y2": 313.41156, "r_x3": 326.88324, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 316.76079999999996, "r_x1": 328.37418, "r_y1": 316.76079999999996, "r_x2": 328.37418, "r_y2": 315.02178999999995, "r_x3": 326.88324, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.11963, "r_y0": 316.76079999999996, "r_x1": 348.50162, "r_y1": 316.76079999999996, "r_x2": 348.50162, "r_y2": 315.02178999999995, "r_x3": 329.11963, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord.", "orig": "Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 318.37103, "r_x1": 328.34061, "r_y1": 318.37103, "r_x2": 328.34061, "r_y2": 316.63199, "r_x3": 326.88324, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06931, "r_y0": 318.37103, "r_x1": 353.11588, "r_y1": 318.37103, "r_x2": 353.11588, "r_y2": 316.63199, "r_x3": 329.06931, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "When the bulb is cool, remove the", "orig": "When the bulb is cool, remove the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 319.9812299999999, "r_x1": 353.79517, "r_y1": 319.9812299999999, "r_x2": 353.79517, "r_y2": 318.2422199999999, "r_x3": 326.88464, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " oblique illuminator cap and remove", "orig": " oblique illuminator cap and remove", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 321.59146, "r_x1": 348.02094, "r_y1": 321.59146, "r_x2": 348.02094, "r_y2": 319.85242000000005, "r_x3": 326.88464, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 323.20166, "r_x1": 328.37512, "r_y1": 323.20166, "r_x2": 328.37512, "r_y2": 321.46265, "r_x3": 326.88464, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 139, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.12036, "r_y0": 323.20166, "r_x1": 352.96808, "r_y1": 323.20166, "r_x2": 352.96808, "r_y2": 321.46265, "r_x3": 329.12036, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "Replace with a new halogen bulb.", "orig": "Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 140, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 324.81189, "r_x1": 328.36884, "r_y1": 324.81189, "r_x2": 328.36884, "r_y2": 323.07285, "r_x3": 326.88608, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 141, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.1102, "r_y0": 324.81189, "r_x1": 356.5412, "r_y1": 324.81189, "r_x2": 356.5412, "r_y2": 323.07285, "r_x3": 329.1102, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "Open the window in the base plate and", "orig": "Open the window in the base plate and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 326.42209, "r_x1": 350.13828, "r_y1": 326.42209, "r_x2": 350.13828, "r_y2": 324.68307000000004, "r_x3": 326.88608, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 328.03232, "r_x1": 351.59677, "r_y1": 328.03232, "r_x2": 351.59677, "r_y2": 326.29327, "r_x3": 326.88608, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 329.64252, "r_x1": 336.89197, "r_y1": 329.64252, "r_x2": 336.89197, "r_y2": 327.9035, "r_x3": 326.88608, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " illuminator.", "orig": " illuminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 145, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 265.23395000000005, "r_x1": 366.93256, "r_y1": 265.23395000000005, "r_x2": 366.93256, "r_y2": 263.49492999999995, "r_x3": 358.42023, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 146, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 266.84418000000005, "r_x1": 359.89841, "r_y1": 266.84418000000005, "r_x2": 359.89841, "r_y2": 265.10515999999996, "r_x3": 358.42023, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 386.70093, "r_y1": 290.99738, "r_x2": 386.70093, "r_y2": 289.25836, "r_x3": 360.49353, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "If the image is not clear to both eyes", "orig": "If the image is not clear to both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 292.6076, "r_x1": 388.03534, "r_y1": 292.6076, "r_x2": 388.03534, "r_y2": 290.86855999999995, "r_x3": 358.42453, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " at the same time, the diopter ring may", "orig": " at the same time, the diopter ring may", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 294.2178, "r_x1": 373.13724, "r_y1": 294.2178, "r_x2": 373.13724, "r_y2": 292.47879, "r_x3": 358.42453, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 297.43823, "r_x1": 381.74539, "r_y1": 297.43823, "r_x2": 381.74539, "r_y2": 295.69922, "r_x3": 358.42453, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 299.04846, "r_x1": 359.83682, "r_y1": 299.04846, "r_x2": 359.83682, "r_y2": 297.30942, "r_x3": 358.42453, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.54297, "r_y0": 299.04846, "r_x1": 388.08289, "r_y1": 299.04846, "r_x2": 388.08289, "r_y2": 297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 302.26889000000006, "r_x1": 387.72266, "r_y1": 302.26889000000006, "r_x2": 387.72266, "r_y2": 300.52985, "r_x3": 358.42453, "r_y3": 300.52985, "coord_origin": "TOPLEFT" }, "text": " differences in acuity between the right", "orig": " differences in acuity between the right", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 303.8790900000001, "r_x1": 384.1991, "r_y1": 303.8790900000001, "r_x2": 384.1991, "r_y2": 302.14008000000007, "r_x3": 358.42453, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": " and left eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 328.03232, "r_x1": 385.20682, "r_y1": 328.03232, "r_x2": 385.20682, "r_y2": 326.29327, "r_x3": 358.42596, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " and right eyepieces, then before", "orig": " and right eyepieces, then before", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 329.64252, "r_x1": 382.21964, "r_y1": 329.64252, "r_x2": 382.21964, "r_y2": 327.9035, "r_x3": 358.42596, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " viewing set the diopter ring", "orig": " viewing set the diopter ring", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 331.25275, "r_x1": 382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.64169, "r_y0": 336.08337, "r_x1": 385.75333, "r_y1": 336.08337, "r_x2": 385.75333, "r_y2": 334.34436, "r_x3": 360.64169, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord from the", "orig": "Disconnect the power cord from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 337.6936, "r_x1": 372.01416, "r_y1": 337.6936, "r_x2": 372.01416, "r_y2": 335.95456, "r_x3": 358.42596, "r_y3": 335.95456, "coord_origin": "TOPLEFT" }, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 339.3038, "r_x1": 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449.10074000000003, "r_y3": 378.66302, "coord_origin": "TOPLEFT" }, "text": "Circling Minimums", "orig": "Circling Minimums", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 252, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 385.34148999999996, "r_x1": 449.64444, "r_y1": 385.34148999999996, "r_x2": 449.64444, "r_y2": 383.2203999999999, "r_x3": 449.10074000000003, "r_y3": 383.2203999999999, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 253, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.18811, "r_y0": 385.34148999999996, "r_x1": 550.77124, "r_y1": 385.34148999999996, "r_x2": 550.77124, "r_y2": 383.2203999999999, "r_x3": 450.18811, "r_y3": 383.2203999999999, "coord_origin": "TOPLEFT" }, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "orig": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 254, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 387.87842, "r_x1": 536.14716, "r_y1": 387.87842, "r_x2": 536.14716, "r_y2": 385.75732, "r_x3": 449.10074000000003, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 255, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 538.31085, "r_y0": 387.87842, "r_x1": 549.49921, "r_y1": 387.87842, "r_x2": 549.49921, "r_y2": 385.75732, "r_x3": 538.31085, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 256, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 390.1571, "r_x1": 547.58185, "r_y1": 390.1571, "r_x2": 547.58185, "r_y2": 388.03601, "r_x3": 449.10074000000003, "r_y3": 388.03601, "coord_origin": "TOPLEFT" }, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 257, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 395.33386, "r_x1": 449.6163, "r_y1": 395.33386, "r_x2": 449.6163, "r_y2": 393.2128000000001, "r_x3": 449.10074000000003, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 258, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.1319, "r_y0": 395.33386, "r_x1": 529.53082, "r_y1": 395.33386, "r_x2": 529.53082, "r_y2": 393.2128000000001, "r_x3": 450.1319, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "orig": 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"confidence": 1.0, "from_ocr": false }, { "index": 261, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 417.50446, "r_x1": 496.2829, "r_y1": 417.50446, "r_x2": 496.2829, "r_y2": 415.59549, "r_x3": 449.95525999999995, "r_y3": 415.59549, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 262, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 411.1644, "r_x1": 551.16101, "r_y1": 411.1644, "r_x2": 551.16101, "r_y2": 409.25543, "r_x3": 501.13077, "r_y3": 409.25543, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 264, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 422.73331, "r_x1": 469.35599, "r_y1": 422.73331, "r_x2": 469.35599, "r_y2": 420.18802, "r_x3": 449.10074000000003, "r_y3": 420.18802, "coord_origin": "TOPLEFT" }, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 265, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 427.21017, "r_x1": 525.93616, "r_y1": 427.21017, "r_x2": 525.93616, "r_y2": 425.08908, "r_x3": 449.10074000000003, "r_y3": 425.08908, "coord_origin": "TOPLEFT" }, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 266, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 429.48886, "r_x1": 522.0343, "r_y1": 429.48886, "r_x2": 522.0343, "r_y2": 427.3678, "r_x3": 449.10074000000003, "r_y3": 427.3678, "coord_origin": "TOPLEFT" }, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 267, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 431.76755, "r_x1": 524.67151, "r_y1": 431.76755, "r_x2": 524.67151, "r_y2": 429.64648, "r_x3": 449.10074000000003, "r_y3": 429.64648, "coord_origin": "TOPLEFT" }, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 268, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 434.04623, "r_x1": 527.172, "r_y1": 434.04623, "r_x2": 527.172, "r_y2": 431.92514000000006, "r_x3": 449.10074000000003, "r_y3": 431.92514000000006, "coord_origin": "TOPLEFT" }, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 269, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 436.32492, "r_x1": 502.39545, "r_y1": 436.32492, "r_x2": 502.39545, "r_y2": 434.20383, "r_x3": 449.10074000000003, "r_y3": 434.20383, "coord_origin": "TOPLEFT" }, "text": "dimensions (length and width) are shown for all active runways.", "orig": "dimensions (length and width) are shown for all active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 270, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 440.88228999999995, "r_x1": 512.92676, "r_y1": 440.88228999999995, "r_x2": 512.92676, "r_y2": 438.7611999999999, "r_x3": 449.10074000000003, "r_y3": 438.7611999999999, "coord_origin": "TOPLEFT" }, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are 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Circling only approaches will not show a TDZE.", "orig": "the landing surface. 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382.30273, "coord_origin": "TOPLEFT" }, "text": "August 2012 ", "orig": "August 2012 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 301, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.4722, "r_y0": 384.55927, "r_x1": 384.81079, "r_y1": 384.55927, "r_x2": 384.81079, "r_y2": 382.30273, "r_x3": 350.4722, "r_y3": 382.30273, "coord_origin": "TOPLEFT" }, "text": "2012 full year result and fi nal dividend announced", "orig": "2012 full year result and fi nal dividend announced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 302, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 387.10205, "r_x1": 330.97336, "r_y1": 387.10205, "r_x2": 330.97336, "r_y2": 384.84552, "r_x3": 329.40536, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "30", "orig": "30", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 303, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.75735, "r_y0": 387.10205, "r_x1": 341.16534, "r_y1": 387.10205, "r_x2": 341.16534, "r_y2": 384.84552, "r_x3": 331.75735, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "August 2012 ", "orig": "August 2012 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 304, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.4722, "r_y0": 387.10205, "r_x1": 372.90613, "r_y1": 387.10205, "r_x2": 372.90613, "r_y2": 384.84552, "r_x3": 350.4722, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "Ex-dividend trading commences", "orig": "Ex-dividend trading commences", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 305, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 389.64483999999993, "r_x1": 330.20337, "r_y1": 389.64483999999993, "r_x2": 330.20337, "r_y2": 387.38828, "r_x3": 329.40536, "r_y3": 387.38828, 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400.46155, "coord_origin": "TOPLEFT" }, "text": "Indicative dates only, subject to change/Board confi rmation", "orig": "Indicative dates only, subject to change/Board confi rmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 324, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 406.60156, "r_x1": 391.771, "r_y1": 406.60156, "r_x2": 391.771, "r_y2": 404.34503, "r_x3": 329.40536, "r_y3": 404.34503, "coord_origin": "TOPLEFT" }, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 325, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 408.63513000000006, "r_x1": 369.65308, "r_y1": 408.63513000000006, "r_x2": 369.65308, "r_y2": 406.37857, 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1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 328, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 471.00577, "r_x1": 395.01788, "r_y1": 471.00577, "r_x2": 395.01788, "r_y2": 468.74924, "r_x3": 363.54486, "r_y3": 468.74924, "coord_origin": "TOPLEFT" }, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 329, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 473.03934, "r_x1": 394.08322, "r_y1": 473.03934, "r_x2": 394.08322, "r_y2": 470.78281, "r_x3": 363.54486, "r_y3": 470.78281, "coord_origin": "TOPLEFT" }, "text": "the AGL business has an established history ", "orig": "the AGL business has an established history ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 330, "rgba": { 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", "orig": "electricity needs of Australian households. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 332, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 479.13998, "r_x1": 394.11481, "r_y1": 479.13998, "r_x2": 394.11481, "r_y2": 476.88345, "r_x3": 363.54486, "r_y3": 476.88345, "coord_origin": "TOPLEFT" }, "text": "In 1841, when AGL supplied the gas to light ", "orig": "In 1841, when AGL supplied the gas to light ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 333, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 481.17352, "r_x1": 393.75891, "r_y1": 481.17352, "r_x2": 393.75891, "r_y2": 478.91699, "r_x3": 363.54486, "r_y3": 478.91699, "coord_origin": "TOPLEFT" }, "text": "the fi rst public street lamp, it was reported ", "orig": "the fi rst public street lamp, it was reported ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 334, "rgba": { "r": 0, 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"orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 344, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 490.30679, "r_y0": 254.98479999999995, "r_x1": 492.09982, "r_y1": 254.98479999999995, "r_x2": 492.09982, "r_y2": 251.47478999999998, "r_x3": 490.30679, "r_y3": 251.47478999999998, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 345, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 265.92719, "r_x1": 500.05637, "r_y1": 265.92719, "r_x2": 500.05637, "r_y2": 263.88922, "r_x3": 498.15335, "r_y3": 263.88922, "coord_origin": "TOPLEFT" }, "text": "In ", "orig": "In ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 346, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.05637, "r_y0": 265.86310000000003, "r_x1": 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}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 352, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 280.9357, "r_x1": 505.64642000000003, "r_y1": 280.9357, "r_x2": 505.64642000000003, "r_y2": 277.34619, "r_x3": 498.15335, "r_y3": 277.34619, "coord_origin": "TOPLEFT" }, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 353, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 283.85983, "r_x1": 543.92957, "r_y1": 283.85983, "r_x2": 543.92957, "r_y2": 281.82187, "r_x3": 498.15335, "r_y3": 281.82187, "coord_origin": "TOPLEFT" }, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 354, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 285.90289, "r_x1": 545.67834, "r_y1": 285.90289, "r_x2": 545.67834, "r_y2": 283.8649, "r_x3": 498.15335, "r_y3": 283.8649, "coord_origin": "TOPLEFT" }, "text": "the messages printed on them. Understanding these three ways ", "orig": "the messages printed on them. Understanding these three ways ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 355, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 287.94592, "r_x1": 545.26471, "r_y1": 287.94592, "r_x2": 545.26471, "r_y2": 285.90796, "r_x3": 498.15335, "r_y3": 285.90796, "coord_origin": "TOPLEFT" }, "text": "of classifying signs will help you figure out the meaning of signs ", "orig": "of classifying signs will help you figure out the meaning of signs ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 356, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 289.98895, "r_x1": 513.31335, "r_y1": 289.98895, "r_x2": 513.31335, "r_y2": 287.95099, "r_x3": 498.15335, "r_y3": 287.95099, "coord_origin": "TOPLEFT" }, "text": "that are new to you. ", "orig": "that are new to you. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 357, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 505.43439, "r_y0": 304.89639, "r_x1": 508.53033000000005, "r_y1": 304.89639, "r_x2": 508.53033000000005, "r_y2": 303.07596, "r_x3": 505.43439, "r_y3": 303.07596, "coord_origin": "TOPLEFT" }, "text": "Stop", "orig": "Stop", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 358, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 527.45502, "r_y0": 305.07397, "r_x1": 541.44678, "r_y1": 305.07397, "r_x2": 541.44678, "r_y2": 303.25354, "r_x3": 527.45502, "r_y3": 303.25354, "coord_origin": "TOPLEFT" }, "text": "Yield the right-of-way", "orig": "Yield the right-of-way", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 359, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.79385, "r_y0": 323.01016, "r_x1": 510.41632, "r_y1": 323.01016, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 367, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 516.97748, "r_y0": 339.88082999999995, "r_x1": 529.77484, "r_y1": 339.88082999999995, "r_x2": 529.77484, "r_y2": 338.06039, "r_x3": 516.97748, "r_y3": 338.06039, "coord_origin": "TOPLEFT" }, "text": "Shows a permitted ", "orig": "Shows a permitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 368, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 516.97748, "r_y0": 341.56842, "r_x1": 520.96399, "r_y1": 341.56842, "r_x2": 520.96399, "r_y2": 339.74799, "r_x3": 516.97748, "r_y3": 339.74799, "coord_origin": "TOPLEFT" }, "text": "action", "orig": "action", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 369, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 534.55847, "r_y0": 339.7032500000001, "r_x1": 548.58453, "r_y1": 339.7032500000001, "r_x2": 548.58453, "r_y2": 337.88281, "r_x3": 534.55847, "r_y3": 337.88281, "coord_origin": "TOPLEFT" }, "text": "Shows an action that ", "orig": "Shows an action that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 370, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 534.55847, "r_y0": 341.39084, "r_x1": 545.08862, "r_y1": 341.39084, "r_x2": 545.08862, "r_y2": 339.57040000000006, "r_x3": 534.55847, "r_y3": 339.57040000000006, "coord_origin": "TOPLEFT" }, "text": "is not permitted", "orig": "is not permitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 371, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 483.05853, "r_y0": 357.9946, "r_x1": 494.72577, "r_y1": 357.9946, "r_x2": 494.72577, "r_y2": 356.17416, "r_x3": 483.05853, "r_y3": 356.17416, "coord_origin": "TOPLEFT" }, "text": "Warns of hazards ", "orig": "Warns of hazards ", "text_direction": 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511.69116, "r_y2": 357.95056, "r_x3": 499.39645, "r_y3": 357.95056, "coord_origin": "TOPLEFT" }, "text": "construction zones", "orig": "construction zones", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 375, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 516.75891, "r_y0": 358.0834, "r_x1": 527.42938, "r_y1": 358.0834, "r_x2": 527.42938, "r_y2": 356.26297000000005, "r_x3": 516.75891, "r_y3": 356.26297000000005, "coord_origin": "TOPLEFT" }, "text": "Railway crossing", "orig": "Railway crossing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 376, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 534.5141, "r_y0": 354.75024, "r_x1": 547.89862, "r_y1": 354.75024, "r_x2": 547.89862, "r_y2": 352.92981, "r_x3": 534.5141, "r_y3": 352.92981, "coord_origin": "TOPLEFT" }, "text": "Shows distance and ", "orig": "Shows distance and ", "text_direction": "left_to_right", "confidence": 1.0, 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270.14075, "coord_origin": "TOPLEFT" }, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 380, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 274.34888, "r_x1": 492.31219, "r_y1": 274.34888, "r_x2": 492.31219, "r_y2": 272.84717, "r_x3": 479.97293, "r_y3": 272.84717, "coord_origin": "TOPLEFT" }, "text": "- regulatory signs", "orig": "- regulatory signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 381, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 276.64679, "r_x1": 486.72598000000005, "r_y1": 276.64679, "r_x2": 486.72598000000005, "r_y2": 275.14513999999997, "r_x3": 479.97293, "r_y3": 275.14513999999997, "coord_origin": "TOPLEFT" }, "text": "- school, ", "orig": "- school, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 382, "rgba": { "r": 0, "g": 0, "b": 0, "a": 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491.00775000000004, "r_y2": 281.52759, "r_x3": 479.97293, "r_y3": 281.52759, "coord_origin": "TOPLEFT" }, "text": "- lane use signs", "orig": "- lane use signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 385, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 285.3272099999999, "r_x1": 493.32748, "r_y1": 285.3272099999999, "r_x2": 493.32748, "r_y2": 283.82556, "r_x3": 479.97293, "r_y3": 283.82556, "coord_origin": "TOPLEFT" }, "text": "- turn control signs", "orig": "- turn control signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 386, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 479.97293, "r_y0": 287.62518, "r_x1": 490.4915199999999, "r_y1": 287.62518, "r_x2": 490.4915199999999, "r_y2": 286.1235, "r_x3": 479.97293, "r_y3": 286.1235, "coord_origin": "TOPLEFT" }, "text": "- parking signs", "orig": "- parking signs", "text_direction": "left_to_right", 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"coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 208, "label": "text", "bbox": { "l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 209, "label": "text", "bbox": { "l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 210, "label": "text", "bbox": { "l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 211, "label": "text", "bbox": { "l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 212, "label": "text", "bbox": { "l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 213, "label": "text", "bbox": { "l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 214, "label": "text", "bbox": { "l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 215, "label": "text", "bbox": { "l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 216, "label": "text", "bbox": { "l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 217, "label": "text", "bbox": { "l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 218, "label": "text", "bbox": { "l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 219, "label": "text", "bbox": { "l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 220, "label": "text", "bbox": { "l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 221, "label": "text", "bbox": { "l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 222, "label": "text", "bbox": { "l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 223, "label": "text", "bbox": { "l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 224, "label": "text", "bbox": { "l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 225, "label": "text", "bbox": { "l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 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297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 231, "label": "text", "bbox": { "l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 232, "label": "text", "bbox": { "l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": 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eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 234, "label": "text", "bbox": { "l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 235, "label": "text", "bbox": { "l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 236, "label": "text", "bbox": { "l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 237, "label": "text", "bbox": { "l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 238, "label": "text", "bbox": { "l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 239, "label": "text", "bbox": { "l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 240, "label": "text", "bbox": { "l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 241, "label": "text", "bbox": { "l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 242, "label": "text", "bbox": { "l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 243, "label": "text", "bbox": { "l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 244, "label": "text", "bbox": { "l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 245, "label": "text", "bbox": { "l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 246, "label": "text", "bbox": { "l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 247, "label": "text", "bbox": { "l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 248, "label": "text", "bbox": { "l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 249, "label": "text", "bbox": { "l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 250, "label": "text", "bbox": { "l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 190, "rgba": { 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}, { "id": 252, "label": "text", "bbox": { "l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 331.25275, "r_x1": 382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 253, "label": "text", "bbox": { "l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 254, "label": "text", "bbox": { "l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 255, "label": "text", "bbox": { "l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { 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"coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 323, "label": "text", "bbox": { "l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 324, "label": "text", "bbox": { "l": 449.10074000000003, 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 405, "label": "text", "bbox": { "l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 406, "label": "text", "bbox": { "l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 407, "label": "text", "bbox": { "l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com" }, { "label": "text", "id": 15, "page_no": 0, "cluster": { "id": 15, "label": "text", "bbox": { "l": 336.69302, "t": 192.05737, "r": 439.45727999999997, "b": 238.62531, "coord_origin": "TOPLEFT" }, "confidence": 0.8027974367141724, "cells": [ { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.52802, "r_y0": 203.22357, "r_x1": 414.84821, "r_y1": 203.22357, "r_x2": 414.84821, "r_y2": 192.05737, "r_x3": 361.52802, "r_y3": 192.05737, "coord_origin": "TOPLEFT" }, "text": "Peter Staar", "orig": "Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.02002, "r_y0": 214.71429, "r_x1": 416.12939, "r_y1": 214.71429, "r_x2": 416.12939, "r_y2": 205.40923999999995, "r_x3": 360.02002, "r_y3": 205.40923999999995, "coord_origin": "TOPLEFT" }, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 336.69302, "r_y0": 226.66931, "r_x1": 439.45727999999997, "r_y1": 226.66931, "r_x2": 439.45727999999997, "r_y2": 217.36425999999994, "r_x3": 336.69302, "r_y3": 217.36425999999994, "coord_origin": "TOPLEFT" }, "text": "Rueschlikon, Switzerland", "orig": "Rueschlikon, Switzerland", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 346.20703, "r_y0": 238.62531, "r_x1": 429.94269, "r_y1": 238.62531, "r_x2": 429.94269, "r_y2": 229.32025, "r_x3": 346.20703, "r_y3": 229.32025, "coord_origin": "TOPLEFT" }, "text": "taa@zurich.ibm.com", "orig": "taa@zurich.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com" }, { "label": "section_header", "id": 5, "page_no": 0, "cluster": { "id": 5, "label": "section_header", "bbox": { "l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT" }, "confidence": 0.8876155018806458, "cells": [ { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798035, "r_y0": 258.01202, "r_x1": 111.94354, "r_y1": 258.01202, "r_x2": 111.94354, "r_y2": 247.70288000000005, "r_x3": 53.798035, "r_y3": 247.70288000000005, "coord_origin": "TOPLEFT" }, "text": "ABSTRACT", "orig": "ABSTRACT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "ABSTRACT" }, { "label": "text", "id": 0, "page_no": 0, "cluster": { "id": 0, "label": "text", "bbox": { "l": 53.466999, "t": 262.90454, "r": 295.56018, "b": 534.29318, "coord_origin": "TOPLEFT" }, "confidence": 0.9823057055473328, "cells": [ { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 271.27917, "r_x1": 295.55591, "r_y1": 271.27917, "r_x2": 295.55591, "r_y2": 262.90454, "r_x3": 53.484001, "r_y3": 262.90454, "coord_origin": "TOPLEFT" }, "text": "Accurate document layout analysis is a key requirement for high-", "orig": "Accurate document layout analysis is a key requirement for high-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 282.23816, "r_x1": 294.04199, "r_y1": 282.23816, "r_x2": 294.04199, "r_y2": 273.86352999999997, "r_x3": 53.79800000000001, "r_y3": 273.86352999999997, "coord_origin": "TOPLEFT" }, "text": "quality PDF document conversion. With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 293.19717, "r_x1": 295.34586, "r_y1": 293.19717, "r_x2": 295.34586, "r_y2": 284.82254, "r_x3": 53.79800000000001, "r_y3": 284.82254, "coord_origin": "TOPLEFT" }, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 304.15616000000006, "r_x1": 294.04709, "r_y1": 304.15616000000006, "r_x2": 294.04709, "r_y2": 295.78152, "r_x3": 53.79800000000001, "r_y3": 295.78152, "coord_origin": "TOPLEFT" }, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 315.11517, "r_x1": 294.04645, "r_y1": 315.11517, "r_x2": 294.04645, "r_y2": 306.74053999999995, "r_x3": 53.79800000000001, "r_y3": 306.74053999999995, "coord_origin": "TOPLEFT" }, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 326.07416, "r_x1": 294.27573, "r_y1": 326.07416, "r_x2": 294.27573, "r_y2": 317.69952, "r_x3": 53.79800000000001, "r_y3": 317.69952, "coord_origin": "TOPLEFT" }, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 337.03317, "r_x1": 294.04712, "r_y1": 337.03317, "r_x2": 294.04712, "r_y2": 328.65854, "r_x3": 53.79800000000001, "r_y3": 328.65854, "coord_origin": "TOPLEFT" }, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 347.99219, "r_x1": 294.0437, "r_y1": 347.99219, "r_x2": 294.0437, "r_y2": 339.61755, "r_x3": 53.79800000000001, "r_y3": 339.61755, "coord_origin": "TOPLEFT" }, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 358.95117, "r_x1": 294.04715, "r_y1": 358.95117, "r_x2": 294.04715, "r_y2": 350.57654, "r_x3": 53.79800000000001, "r_y3": 350.57654, "coord_origin": "TOPLEFT" }, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 369.90918000000005, "r_x1": 294.04364, "r_y1": 369.90918000000005, "r_x2": 294.04364, "r_y2": 361.53455, "r_x3": 53.79800000000001, "r_y3": 361.53455, "coord_origin": "TOPLEFT" }, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 380.87714000000005, "r_x1": 92.863388, "r_y1": 380.87714000000005, "r_x2": 92.863388, "r_y2": 372.53839, "r_x3": 53.79800000000001, "r_y3": 372.53839, "coord_origin": "TOPLEFT" }, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 92.863998, "r_y0": 380.86816, "r_x1": 294.04361, "r_y1": 380.86816, "r_x2": 294.04361, "r_y2": 372.49353, "r_x3": 92.863998, "r_y3": 372.49353, "coord_origin": "TOPLEFT" }, "text": ", a new, publicly available, document-layout annotation", "orig": ", a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 391.82718, "r_x1": 294.04718, "r_y1": 391.82718, "r_x2": 294.04718, "r_y2": 383.45255, "r_x3": 53.79800000000001, "r_y3": 383.45255, "coord_origin": "TOPLEFT" }, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 402.78616, "r_x1": 294.0437, "r_y1": 402.78616, "r_x2": 294.0437, "r_y2": 394.41153, "r_x3": 53.79800000000001, "r_y3": 394.41153, "coord_origin": "TOPLEFT" }, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 413.74518, "r_x1": 294.04535, "r_y1": 413.74518, "r_x2": 294.04535, "r_y2": 405.37054, "r_x3": 53.79800000000001, "r_y3": 405.37054, "coord_origin": "TOPLEFT" }, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 424.70416000000006, "r_x1": 294.04715, "r_y1": 424.70416000000006, "r_x2": 294.04715, "r_y2": 416.32953, "r_x3": 53.79800000000001, "r_y3": 416.32953, "coord_origin": "TOPLEFT" }, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 435.66318, "r_x1": 294.04712, "r_y1": 435.66318, "r_x2": 294.04712, "r_y2": 427.28853999999995, "r_x3": 53.79800000000001, "r_y3": 427.28853999999995, "coord_origin": "TOPLEFT" }, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 446.62216, "r_x1": 295.03, "r_y1": 446.62216, "r_x2": 295.03, "r_y2": 438.24753, "r_x3": 53.79800000000001, "r_y3": 438.24753, "coord_origin": "TOPLEFT" }, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 457.58118, "r_x1": 294.21616, "r_y1": 457.58118, "r_x2": 294.21616, "r_y2": 449.20654, "r_x3": 53.466999, "r_y3": 449.20654, "coord_origin": "TOPLEFT" }, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 468.54016, "r_x1": 294.04712, "r_y1": 468.54016, "r_x2": 294.04712, "r_y2": 460.16553, "r_x3": 53.79800000000001, "r_y3": 460.16553, "coord_origin": "TOPLEFT" }, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 479.49817, "r_x1": 295.56018, "r_y1": 479.49817, "r_x2": 295.56018, "r_y2": 471.12354, "r_x3": 53.79800000000001, "r_y3": 471.12354, "coord_origin": "TOPLEFT" }, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 490.45718, "r_x1": 295.42783, "r_y1": 490.45718, "r_x2": 295.42783, "r_y2": 482.08255, "r_x3": 53.79800000000001, "r_y3": 482.08255, "coord_origin": "TOPLEFT" }, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 501.41617, "r_x1": 294.04715, "r_y1": 501.41617, "r_x2": 294.04715, "r_y2": 493.04153, "r_x3": 53.79800000000001, "r_y3": 493.04153, "coord_origin": "TOPLEFT" }, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 512.37518, "r_x1": 295.55618, "r_y1": 512.37518, "r_x2": 295.55618, "r_y2": 504.00055, "r_x3": 53.79800000000001, "r_y3": 504.00055, "coord_origin": "TOPLEFT" }, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 523.33417, "r_x1": 294.21643, "r_y1": 523.33417, "r_x2": 294.21643, "r_y2": 514.95953, "r_x3": 53.79800000000001, "r_y3": 514.95953, "coord_origin": "TOPLEFT" }, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 534.29318, "r_x1": 212.05495, "r_y1": 534.29318, "r_x2": 212.05495, "r_y2": 525.91855, "r_x3": 53.79800000000001, "r_y3": 525.91855, "coord_origin": "TOPLEFT" }, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. 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Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.79189, "r_x1": 187.72285, "r_y1": 672.79189, "r_x2": 187.72285, "r_y2": 666.27837, "r_x3": 53.79800000000001, "r_y3": 666.27837, "coord_origin": "TOPLEFT" }, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)." }, { "label": "text", "id": 134, "page_no": 0, "cluster": { "id": 134, "label": "text", "bbox": { "l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 681.56586, "r_x1": 197.86275, "r_y1": 681.56586, "r_x2": 197.86275, "r_y2": 675.08023, "r_x3": 53.79800000000001, "r_y3": 675.08023, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA" }, { "label": "text", "id": 135, "page_no": 0, "cluster": { "id": 135, "label": "text", "bbox": { "l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, 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digital camera or a", "orig": " the image with a digital camera or a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.15176, "r_y0": 342.22049, "r_x1": 337.91086, "r_y1": 342.22049, "r_x2": 337.91086, "r_y2": 340.69882, "r_x3": 328.15176, "r_y3": 340.69882, "coord_origin": "TOPLEFT" }, "text": " micro TV unit", "orig": " micro TV unit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.8313, "r_y0": 343.71207, "r_x1": 329.09155, "r_y1": 343.71207, "r_x2": 329.09155, "r_y2": 342.19043000000005, "r_x3": 327.8313, "r_y3": 342.19043000000005, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.72168, "r_y0": 343.71207, "r_x1": 354.9267, "r_y1": 343.71207, "r_x2": 354.9267, "r_y2": 342.19043000000005, "r_x3": 329.72168, "r_y3": 342.19043000000005, "coord_origin": "TOPLEFT" }, "text": "Loosen the retention screw, then rotate ", "orig": "Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.8313, "r_y0": 345.14355, "r_x1": 351.66949, "r_y1": 345.14355, "r_x2": 351.66949, "r_y2": 343.62192, "r_x3": 327.8313, "r_y3": 343.62192, "coord_origin": "TOPLEFT" }, "text": " the adjustment ring to change the ", "orig": " the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.21185, "r_y0": 346.57504, "r_x1": 346.33179, "r_y1": 346.57504, "r_x2": 346.33179, "r_y2": 345.05338, "r_x3": 328.21185, "r_y3": 345.05338, "coord_origin": "TOPLEFT" }, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.83005, "r_y0": 348.36847, "r_x1": 329.12726, "r_y1": 348.36847, "r_x2": 329.12726, "r_y2": 346.84680000000003, "r_x3": 327.83005, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.77588, "r_y0": 348.36847, "r_x1": 351.18005, "r_y1": 348.36847, "r_x2": 351.18005, "r_y2": 346.84680000000003, "r_x3": 329.77588, "r_y3": 346.84680000000003, "coord_origin": "TOPLEFT" }, "text": "Make sure that both the images in", "orig": "Make sure that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.25311, "r_y0": 258.86096, "r_x1": 350.07861, "r_y1": 258.86096, "r_x2": 350.07861, "r_y2": 254.94812000000002, "r_x3": 327.25311, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "OPERATION ", "orig": "OPERATION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.07861, "r_y0": 258.68066, "r_x1": 351.82651, "r_y1": 258.68066, "r_x2": 351.82651, "r_y2": 254.76782000000003, "r_x3": 350.07861, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": "(", "orig": "(", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 351.82651, "r_y0": 258.86096, "r_x1": 360.85242, "r_y1": 258.86096, "r_x2": 360.85242, "r_y2": 254.94812000000002, "r_x3": 351.82651, "r_y3": 254.94812000000002, "coord_origin": "TOPLEFT" }, "text": "cont.", "orig": "cont.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.85242, "r_y0": 258.68066, "r_x1": 362.60028, "r_y1": 258.68066, "r_x2": 362.60028, "r_y2": 254.76782000000003, "r_x3": 360.85242, "r_y3": 254.76782000000003, "coord_origin": "TOPLEFT" }, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 265.23395000000005, "r_x1": 345.84351, "r_y1": 265.23395000000005, "r_x2": 345.84351, "r_y2": 263.49492999999995, "r_x3": 326.88037, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 266.84418000000005, "r_x1": 340.54153, "r_y1": 266.84418000000005, "r_x2": 340.54153, "r_y2": 265.10515999999996, "r_x3": 326.88037, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 268.45441000000005, "r_x1": 328.31903, "r_y1": 268.45441000000005, "r_x2": 328.31903, "r_y2": 266.71533, "r_x3": 326.88037, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.03836, "r_y0": 268.45441000000005, "r_x1": 354.21472, "r_y1": 268.45441000000005, "r_x2": 354.21472, "r_y2": 266.71533, "r_x3": 329.03836, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": "There are two objectives. The lower", "orig": "There are two objectives. The lower", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 270.06458, "r_x1": 355.19193, "r_y1": 270.06458, "r_x2": 355.19193, "r_y2": 268.32556, "r_x3": 326.88037, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": " magnification objective has a greater", "orig": " magnification objective has a greater", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 271.6748, "r_x1": 345.80057, "r_y1": 271.6748, "r_x2": 345.80057, "r_y2": 269.93579, "r_x3": 326.88037, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 273.28503, "r_x1": 328.33862, "r_y1": 273.28503, "r_x2": 328.33862, "r_y2": 271.54602, "r_x3": 326.88037, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06775, "r_y0": 273.28503, "r_x1": 352.39969, "r_y1": 273.28503, "r_x2": 352.39969, "r_y2": 271.54602, "r_x3": 329.06775, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": "In order to observe the specimen", "orig": "In order to observe the specimen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 274.89526, "r_x1": 352.90042, "r_y1": 274.89526, "r_x2": 352.90042, "r_y2": 273.15619000000004, "r_x3": 326.88037, "r_y3": 273.15619000000004, "coord_origin": "TOPLEFT" }, "text": " easily use the lower magnification", "orig": " easily use the lower magnification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 276.50543000000005, "r_x1": 354.59546, "r_y1": 276.50543000000005, "r_x2": 354.59546, "r_y2": 274.76642000000004, "r_x3": 326.88037, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": " objective first. Then, by rotating the", "orig": " objective first. Then, by rotating the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 278.11566000000005, "r_x1": 350.81885, "r_y1": 278.11566000000005, "r_x2": 350.81885, "r_y2": 276.37665000000004, "r_x3": 326.88037, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": " case, the magnification can be", "orig": " case, the magnification can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 279.72589000000005, "r_x1": 335.46707, "r_y1": 279.72589000000005, "r_x2": 335.46707, "r_y2": 277.98688000000004, "r_x3": 326.88037, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 282.94632, "r_x1": 354.57755, "r_y1": 282.94632, "r_x2": 354.57755, "r_y2": 281.20728, "r_x3": 326.88037, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 284.55652, "r_x1": 335.1752, "r_y1": 284.55652, "r_x2": 335.1752, "r_y2": 282.81750000000005, "r_x3": 326.88037, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 286.16675, "r_x1": 328.34784, "r_y1": 286.16675, "r_x2": 328.34784, "r_y2": 284.4277, "r_x3": 326.88037, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.08157, "r_y0": 286.16675, "r_x1": 354.76245, "r_y1": 286.16675, "r_x2": 354.76245, "r_y2": 284.4277, "r_x3": 329.08157, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": "The distance between the observer's", "orig": "The distance between the observer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 287.77695, "r_x1": 354.6499, "r_y1": 287.77695, "r_x2": 354.6499, "r_y2": 286.03793, "r_x3": 326.88037, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " pupils is the interpupillary distance.", "orig": " pupils is the interpupillary distance.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88037, "r_y0": 289.38718, "r_x1": 328.25125, "r_y1": 289.38718, "r_x2": 328.25125, "r_y2": 287.64813, "r_x3": 326.88037, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.93671, "r_y0": 289.38718, "r_x1": 354.29825, "r_y1": 289.38718, "r_x2": 354.29825, "r_y2": 287.64813, "r_x3": 328.93671, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": "To adjust the interpupillary distance", "orig": "To adjust the interpupillary distance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 290.99738, "r_x1": 355.02075, "r_y1": 290.99738, "r_x2": 355.02075, "r_y2": 289.25836, "r_x3": 326.88181, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": " rotate the prism caps until both eyes", "orig": " rotate the prism caps until both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 292.6076, "r_x1": 350.82028, "r_y1": 292.6076, "r_x2": 350.82028, "r_y2": 290.86855999999995, "r_x3": 326.88181, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " coincide with the image in the", "orig": " coincide with the image in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 294.2178, "r_x1": 336.2067, "r_y1": 294.2178, "r_x2": 336.2067, "r_y2": 292.47879, "r_x3": 326.88181, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 297.43823, "r_x1": 335.3941, "r_y1": 297.43823, "r_x2": 335.3941, "r_y2": 295.69922, "r_x3": 326.88181, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88181, "r_y0": 299.04846, "r_x1": 328.34314, "r_y1": 299.04846, "r_x2": 328.34314, "r_y2": 297.30942, "r_x3": 326.88181, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.07379, "r_y0": 299.04846, "r_x1": 353.18555, "r_y1": 299.04846, "r_x2": 353.18555, "r_y2": 297.30942, "r_x3": 329.07379, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "Remove the lens protective cover.", "orig": "Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 300.65866, "r_x1": 328.35919, "r_y1": 300.65866, "r_x2": 328.35919, "r_y2": 298.91965, "r_x3": 326.88324, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.0972, "r_y0": 300.65866, "r_x1": 353.45065, "r_y1": 300.65866, "r_x2": 353.45065, "r_y2": 298.91965, "r_x3": 329.0972, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": "Place the specimen on the working", "orig": "Place the specimen on the working", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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329.02783, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": "Focus the specimen with the left eye", "orig": "Focus the specimen with the left eye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 305.48932, "r_x1": 355.96307, "r_y1": 305.48932, "r_x2": 355.96307, "r_y2": 303.75027, "r_x3": 326.88324, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " first while turning the focus knob until", "orig": " first while turning the focus knob until", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 307.09952000000004, "r_x1": 354.46594, "r_y1": 307.09952000000004, "r_x2": 354.46594, "r_y2": 305.3605, "r_x3": 326.88324, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 308.70975, "r_x1": 328.25488, "r_y1": 308.70975, "r_x2": 328.25488, "r_y2": 306.9707, "r_x3": 326.88324, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.9407, "r_y0": 308.70975, "r_x1": 356.37335, "r_y1": 308.70975, "r_x2": 356.37335, "r_y2": 306.9707, "r_x3": 328.9407, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": "Rotate the right eyepiece ring until the", "orig": "Rotate the right eyepiece ring until the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 310.31995, "r_x1": 355.38867, "r_y1": 310.31995, "r_x2": 355.38867, "r_y2": 308.58093, "r_x3": 326.88324, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " images in each eyepiece coincide and", "orig": " images in each eyepiece coincide and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 311.93018, "r_x1": 343.17249, "r_y1": 311.93018, "r_x2": 343.17249, "r_y2": 310.19113, "r_x3": 326.88324, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 315.15059999999994, "r_x1": 344.13388, "r_y1": 315.15059999999994, "r_x2": 344.13388, "r_y2": 313.41156, "r_x3": 326.88324, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 316.76079999999996, "r_x1": 328.37418, "r_y1": 316.76079999999996, "r_x2": 328.37418, "r_y2": 315.02178999999995, "r_x3": 326.88324, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.11963, "r_y0": 316.76079999999996, "r_x1": 348.50162, "r_y1": 316.76079999999996, "r_x2": 348.50162, "r_y2": 315.02178999999995, "r_x3": 329.11963, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": "Disconnect the power cord.", "orig": "Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88324, "r_y0": 318.37103, "r_x1": 328.34061, "r_y1": 318.37103, "r_x2": 328.34061, "r_y2": 316.63199, "r_x3": 326.88324, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.06931, "r_y0": 318.37103, "r_x1": 353.11588, "r_y1": 318.37103, "r_x2": 353.11588, "r_y2": 316.63199, "r_x3": 329.06931, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": "When the bulb is cool, remove the", "orig": "When the bulb is cool, remove the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 319.9812299999999, "r_x1": 353.79517, "r_y1": 319.9812299999999, "r_x2": 353.79517, "r_y2": 318.2422199999999, "r_x3": 326.88464, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " oblique illuminator cap and remove", "orig": " oblique illuminator cap and remove", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 321.59146, "r_x1": 348.02094, "r_y1": 321.59146, "r_x2": 348.02094, "r_y2": 319.85242000000005, "r_x3": 326.88464, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88464, "r_y0": 323.20166, "r_x1": 328.37512, "r_y1": 323.20166, "r_x2": 328.37512, "r_y2": 321.46265, "r_x3": 326.88464, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 139, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.12036, "r_y0": 323.20166, "r_x1": 352.96808, "r_y1": 323.20166, "r_x2": 352.96808, "r_y2": 321.46265, "r_x3": 329.12036, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": "Replace with a new halogen bulb.", "orig": "Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 140, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 324.81189, "r_x1": 328.36884, "r_y1": 324.81189, "r_x2": 328.36884, "r_y2": 323.07285, "r_x3": 326.88608, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 141, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.1102, "r_y0": 324.81189, "r_x1": 356.5412, "r_y1": 324.81189, "r_x2": 356.5412, "r_y2": 323.07285, "r_x3": 329.1102, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": "Open the window in the base plate and", "orig": "Open the window in the base plate and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 326.42209, "r_x1": 350.13828, "r_y1": 326.42209, "r_x2": 350.13828, "r_y2": 324.68307000000004, "r_x3": 326.88608, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 328.03232, "r_x1": 351.59677, "r_y1": 328.03232, "r_x2": 351.59677, "r_y2": 326.29327, "r_x3": 326.88608, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 329.64252, "r_x1": 336.89197, "r_y1": 329.64252, "r_x2": 336.89197, "r_y2": 327.9035, "r_x3": 326.88608, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " illuminator.", "orig": " illuminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 145, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 265.23395000000005, "r_x1": 366.93256, "r_y1": 265.23395000000005, "r_x2": 366.93256, "r_y2": 263.49492999999995, "r_x3": 358.42023, "r_y3": 263.49492999999995, "coord_origin": "TOPLEFT" }, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 146, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 266.84418000000005, "r_x1": 359.89841, "r_y1": 266.84418000000005, "r_x2": 359.89841, "r_y2": 265.10515999999996, "r_x3": 358.42023, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 284.55652, "r_x1": 381.56656, "r_y1": 284.55652, "r_x2": 381.56656, "r_y2": 282.81750000000005, "r_x3": 358.4231, "r_y3": 282.81750000000005, "coord_origin": "TOPLEFT" }, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 286.16675, "r_x1": 386.63403, "r_y1": 286.16675, "r_x2": 386.63403, "r_y2": 284.4277, "r_x3": 358.4231, "r_y3": 284.4277, "coord_origin": "TOPLEFT" }, "text": " magnification, then move to a higher", "orig": " magnification, then move to a higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 386.70093, "r_y1": 290.99738, "r_x2": 386.70093, "r_y2": 289.25836, "r_x3": 360.49353, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "If the image is not clear to both eyes", "orig": "If the image is not clear to both eyes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 292.6076, "r_x1": 388.03534, "r_y1": 292.6076, "r_x2": 388.03534, "r_y2": 290.86855999999995, "r_x3": 358.42453, "r_y3": 290.86855999999995, "coord_origin": "TOPLEFT" }, "text": " at the same time, the diopter ring may", "orig": " at the same time, the diopter ring may", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 294.2178, "r_x1": 373.13724, "r_y1": 294.2178, "r_x2": 373.13724, "r_y2": 292.47879, "r_x3": 358.42453, "r_y3": 292.47879, "coord_origin": "TOPLEFT" }, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 297.43823, "r_x1": 381.74539, "r_y1": 297.43823, "r_x2": 381.74539, "r_y2": 295.69922, "r_x3": 358.42453, "r_y3": 295.69922, "coord_origin": "TOPLEFT" }, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 299.04846, "r_x1": 359.83682, "r_y1": 299.04846, "r_x2": 359.83682, "r_y2": 297.30942, "r_x3": 358.42453, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.54297, "r_y0": 299.04846, "r_x1": 388.08289, "r_y1": 299.04846, "r_x2": 388.08289, "r_y2": 297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 302.26889000000006, "r_x1": 387.72266, "r_y1": 302.26889000000006, "r_x2": 387.72266, "r_y2": 300.52985, "r_x3": 358.42453, "r_y3": 300.52985, "coord_origin": "TOPLEFT" }, "text": " differences in acuity between the right", "orig": " differences in acuity between the right", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 303.8790900000001, "r_x1": 384.1991, "r_y1": 303.8790900000001, "r_x2": 384.1991, "r_y2": 302.14008000000007, "r_x3": 358.42453, "r_y3": 302.14008000000007, "coord_origin": "TOPLEFT" }, "text": " and left eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 328.03232, "r_x1": 385.20682, "r_y1": 328.03232, "r_x2": 385.20682, "r_y2": 326.29327, "r_x3": 358.42596, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " and right eyepieces, then before", "orig": " and right eyepieces, then before", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 329.64252, "r_x1": 382.21964, "r_y1": 329.64252, "r_x2": 382.21964, "r_y2": 327.9035, "r_x3": 358.42596, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " viewing set the diopter ring", "orig": " viewing set the diopter ring", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 331.25275, "r_x1": 382.63382, "r_y1": 331.25275, "r_x2": 382.63382, "r_y2": 329.5137, "r_x3": 358.42596, "r_y3": 329.5137, "coord_origin": "TOPLEFT" }, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 334.47317999999996, "r_x1": 375.67661, "r_y1": 334.47317999999996, "r_x2": 375.67661, "r_y2": 332.73412999999994, "r_x3": 358.42596, "r_y3": 332.73412999999994, "coord_origin": "TOPLEFT" }, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 336.08337, "r_x1": 359.90311, "r_y1": 336.08337, "r_x2": 359.90311, "r_y2": 334.34436, "r_x3": 358.42596, "r_y3": 334.34436, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", 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F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "orig": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 254, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 387.87842, "r_x1": 536.14716, "r_y1": 387.87842, "r_x2": 536.14716, "r_y2": 385.75732, "r_x3": 449.10074000000003, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 255, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 538.31085, "r_y0": 387.87842, "r_x1": 549.49921, "r_y1": 387.87842, "r_x2": 549.49921, "r_y2": 385.75732, "r_x3": 538.31085, "r_y3": 385.75732, "coord_origin": "TOPLEFT" }, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 256, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 390.1571, "r_x1": 547.58185, "r_y1": 390.1571, "r_x2": 547.58185, "r_y2": 388.03601, "r_x3": 449.10074000000003, "r_y3": 388.03601, "coord_origin": "TOPLEFT" }, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 257, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 395.33386, "r_x1": 449.6163, "r_y1": 395.33386, "r_x2": 449.6163, "r_y2": 393.2128000000001, "r_x3": 449.10074000000003, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 258, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.1319, "r_y0": 395.33386, "r_x1": 529.53082, "r_y1": 395.33386, "r_x2": 529.53082, "r_y2": 393.2128000000001, "r_x3": 450.1319, "r_y3": 393.2128000000001, "coord_origin": "TOPLEFT" }, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "orig": 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"confidence": 1.0, "from_ocr": false }, { "index": 261, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 417.50446, "r_x1": 496.2829, "r_y1": 417.50446, "r_x2": 496.2829, "r_y2": 415.59549, "r_x3": 449.95525999999995, "r_y3": 415.59549, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 262, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 411.1644, "r_x1": 551.16101, "r_y1": 411.1644, "r_x2": 551.16101, "r_y2": 409.25543, "r_x3": 501.13077, "r_y3": 409.25543, "coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 264, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 422.73331, "r_x1": 469.35599, "r_y1": 422.73331, "r_x2": 469.35599, "r_y2": 420.18802, "r_x3": 449.10074000000003, "r_y3": 420.18802, "coord_origin": "TOPLEFT" }, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 265, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 427.21017, "r_x1": 525.93616, "r_y1": 427.21017, "r_x2": 525.93616, "r_y2": 425.08908, "r_x3": 449.10074000000003, "r_y3": 425.08908, "coord_origin": "TOPLEFT" }, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 266, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 429.48886, "r_x1": 522.0343, "r_y1": 429.48886, "r_x2": 522.0343, "r_y2": 427.3678, "r_x3": 449.10074000000003, "r_y3": 427.3678, "coord_origin": "TOPLEFT" }, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 267, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 431.76755, "r_x1": 524.67151, "r_y1": 431.76755, "r_x2": 524.67151, "r_y2": 429.64648, "r_x3": 449.10074000000003, "r_y3": 429.64648, "coord_origin": "TOPLEFT" }, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 268, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 434.04623, "r_x1": 527.172, "r_y1": 434.04623, "r_x2": 527.172, "r_y2": 431.92514000000006, "r_x3": 449.10074000000003, "r_y3": 431.92514000000006, "coord_origin": "TOPLEFT" }, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 269, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 436.32492, "r_x1": 502.39545, "r_y1": 436.32492, "r_x2": 502.39545, "r_y2": 434.20383, "r_x3": 449.10074000000003, "r_y3": 434.20383, "coord_origin": "TOPLEFT" }, "text": "dimensions (length and width) are shown for all active runways.", "orig": "dimensions (length and width) are shown for all active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 270, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 440.88228999999995, "r_x1": 512.92676, "r_y1": 440.88228999999995, "r_x2": 512.92676, "r_y2": 438.7611999999999, "r_x3": 449.10074000000003, "r_y3": 438.7611999999999, "coord_origin": "TOPLEFT" }, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 271, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 445.97900000000004, "r_x1": 460.02307, "r_y1": 445.97900000000004, "r_x2": 460.02307, "r_y2": 444.07001, "r_x3": 449.95525999999995, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 272, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 445.97900000000004, "r_x1": 473.98819, "r_y1": 445.97900000000004, "r_x2": 473.98819, "r_y2": 444.07001, "r_x3": 464.89963, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 273, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 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}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 276, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 509.5809, "r_y0": 445.97900000000004, "r_x1": 524.30237, "r_y1": 445.97900000000004, "r_x2": 524.30237, "r_y2": 444.07001, "r_x3": 509.5809, "r_y3": 444.07001, "coord_origin": "TOPLEFT" }, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 277, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.95525999999995, "r_y0": 456.72104, "r_x1": 458.31406, "r_y1": 456.72104, "r_x2": 458.31406, "r_y2": 454.81207, "r_x3": 449.95525999999995, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 278, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { 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"coord_origin": "TOPLEFT" }, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 281, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 464.89963, "r_y0": 458.77185000000003, "r_x1": 472.49792, "r_y1": 458.77185000000003, "r_x2": 472.49792, "r_y2": 456.86288, "r_x3": 464.89963, "r_y3": 456.86288, "coord_origin": "TOPLEFT" }, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 282, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 478.91357, "r_y0": 456.72104, "r_x1": 483.61584, "r_y1": 456.72104, "r_x2": 483.61584, "r_y2": 454.81207, "r_x3": 478.91357, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Closed", "orig": "Closed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 283, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 478.91357, "r_y0": 458.77185000000003, "r_x1": 486.60754000000003, "r_y1": 458.77185000000003, "r_x2": 486.60754000000003, "r_y2": 456.86288, "r_x3": 478.91357, "r_y3": 456.86288, "coord_origin": "TOPLEFT" }, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 284, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 493.06420999999995, "r_y0": 456.72104, "r_x1": 504.20648, "r_y1": 456.72104, "r_x2": 504.20648, "r_y2": 454.81207, "r_x3": 493.06420999999995, "r_y3": 454.81207, "coord_origin": "TOPLEFT" }, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 285, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 471.45081, "r_x1": 548.59674, "r_y1": 471.45081, "r_x2": 548.59674, "r_y2": 469.32974, "r_x3": 449.10074000000003, "r_y3": 469.32974, "coord_origin": "TOPLEFT" }, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 286, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 473.72949, "r_x1": 500.08181999999994, "r_y1": 473.72949, "r_x2": 500.08181999999994, "r_y2": 471.60843, "r_x3": 449.10074000000003, "r_y3": 471.60843, "coord_origin": "TOPLEFT" }, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 287, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 478.28687, "r_x1": 449.59933000000007, "r_y1": 478.28687, "r_x2": 449.59933000000007, "r_y2": 476.16577, "r_x3": 449.10074000000003, "r_y3": 476.16577, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 288, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 450.09796, "r_y0": 478.28687, "r_x1": 547.82562, "r_y1": 478.28687, "r_x2": 547.82562, "r_y2": 476.16577, "r_x3": 450.09796, "r_y3": 476.16577, "coord_origin": "TOPLEFT" }, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 289, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 547.82623, "r_y0": 478.28687, "r_x1": 548.45862, "r_y1": 478.28687, "r_x2": 548.45862, "r_y2": 476.16577, "r_x3": 547.82623, "r_y3": 476.16577, "coord_origin": "TOPLEFT" }, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 290, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 480.56555, "r_x1": 470.52609000000007, "r_y1": 480.56555, "r_x2": 470.52609000000007, "r_y2": 478.44446, "r_x3": 449.10074000000003, "r_y3": 478.44446, "coord_origin": "TOPLEFT" }, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 291, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 485.12292, "r_x1": 493.37906000000004, "r_y1": 485.12292, "r_x2": 493.37906000000004, "r_y2": 483.00183, "r_x3": 449.10074000000003, "r_y3": 483.00183, "coord_origin": "TOPLEFT" }, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 292, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 489.6803, "r_x1": 549.16168, "r_y1": 489.6803, "r_x2": 549.16168, "r_y2": 487.5592, "r_x3": 449.10074000000003, "r_y3": 487.5592, "coord_origin": "TOPLEFT" }, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 293, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 491.95898, "r_x1": 546.90881, "r_y1": 491.95898, "r_x2": 546.90881, "r_y2": 489.83789, "r_x3": 449.10074000000003, "r_y3": 489.83789, "coord_origin": "TOPLEFT" }, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 294, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 494.23767, "r_x1": 551.80023, "r_y1": 494.23767, "r_x2": 551.80023, "r_y2": 492.11658, "r_x3": 449.10074000000003, "r_y3": 492.11658, "coord_origin": "TOPLEFT" }, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "orig": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 295, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 496.51636, "r_x1": 505.85068000000007, "r_y1": 496.51636, "r_x2": 505.85068000000007, "r_y2": 494.39526, "r_x3": 449.10074000000003, "r_y3": 494.39526, "coord_origin": "TOPLEFT" }, "text": "the landing surface. Circling only approaches will not show a TDZE.", "orig": "the landing surface. Circling only approaches will not show a TDZE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 296, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.80661000000003, "r_y0": 519.01764, "r_x1": 502.08792, "r_y1": 519.01764, "r_x2": 502.08792, "r_y2": 515.9437, "r_x3": 498.80661000000003, "r_y3": 515.9437, "coord_origin": "TOPLEFT" }, "text": "114", "orig": "114", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 297, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 444.56319999999994, "r_y0": 471.87128, "r_x1": 446.25998, "r_y1": 471.87128, "r_x2": 446.25998, "r_y2": 422.84869, "r_x3": 444.56319999999994, "r_y3": 422.84869, "coord_origin": "TOPLEFT" }, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "orig": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 298, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 382.13336, "r_x1": 355.13138, "r_y1": 382.13336, "r_x2": 355.13138, "r_y2": 379.37537, "r_x3": 329.40536, "r_y3": 379.37537, "coord_origin": "TOPLEFT" }, "text": "AGL 2013 Financial Calendar", "orig": "AGL 2013 Financial Calendar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 299, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 384.55927, "r_x1": 330.96848, "r_y1": 384.55927, "r_x2": 330.96848, "r_y2": 382.30273, "r_x3": 329.40536, "r_y3": 382.30273, "coord_origin": "TOPLEFT" }, "text": "22", "orig": "22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 300, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.75003, "r_y0": 384.55927, "r_x1": 341.12875, "r_y1": 384.55927, "r_x2": 341.12875, "r_y2": 382.30273, "r_x3": 331.75003, "r_y3": 382.30273, "coord_origin": "TOPLEFT" }, "text": "August 2012 ", "orig": "August 2012 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 301, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 350.4722, "r_y0": 384.55927, "r_x1": 384.81079, "r_y1": 384.55927, "r_x2": 384.81079, "r_y2": 382.30273, "r_x3": 350.4722, "r_y3": 382.30273, "coord_origin": "TOPLEFT" }, "text": "2012 full year result and fi nal dividend announced", "orig": "2012 full year result and fi nal dividend announced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 302, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 387.10205, "r_x1": 330.97336, "r_y1": 387.10205, "r_x2": 330.97336, "r_y2": 384.84552, "r_x3": 329.40536, "r_y3": 384.84552, "coord_origin": "TOPLEFT" }, "text": "30", "orig": "30", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 303, "rgba": { 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400.46155, "coord_origin": "TOPLEFT" }, "text": "Indicative dates only, subject to change/Board confi rmation", "orig": "Indicative dates only, subject to change/Board confi rmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 324, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 406.60156, "r_x1": 391.771, "r_y1": 406.60156, "r_x2": 391.771, "r_y2": 404.34503, "r_x3": 329.40536, "r_y3": 404.34503, "coord_origin": "TOPLEFT" }, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 325, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.40536, "r_y0": 408.63513000000006, "r_x1": 369.65308, "r_y1": 408.63513000000006, "r_x2": 369.65308, "r_y2": 406.37857, 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1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 328, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 471.00577, "r_x1": 395.01788, "r_y1": 471.00577, "r_x2": 395.01788, "r_y2": 468.74924, "r_x3": 363.54486, "r_y3": 468.74924, "coord_origin": "TOPLEFT" }, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 329, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 363.54486, "r_y0": 473.03934, "r_x1": 394.08322, "r_y1": 473.03934, "r_x2": 394.08322, "r_y2": 470.78281, "r_x3": 363.54486, "r_y3": 470.78281, "coord_origin": "TOPLEFT" }, "text": "the AGL business has an established history ", "orig": "the AGL business has an established history ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 330, "rgba": { 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", "orig": "electricity needs of Australian households. 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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"coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 326.42209, "r_x1": 350.13828, "r_y1": 326.42209, "r_x2": 350.13828, "r_y2": 324.68307000000004, "r_x3": 326.88608, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 203, "label": "text", "bbox": { "l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 328.03232, "r_x1": 351.59677, "r_y1": 328.03232, "r_x2": 351.59677, "r_y2": 326.29327, "r_x3": 326.88608, "r_y3": 326.29327, "coord_origin": "TOPLEFT" }, "text": " fluorescent lamp of transmitted", "orig": " fluorescent lamp of transmitted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 204, "label": "text", "bbox": { "l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 326.88608, "r_y0": 329.64252, "r_x1": 336.89197, "r_y1": 329.64252, "r_x2": 336.89197, "r_y2": 327.9035, "r_x3": 326.88608, "r_y3": 327.9035, "coord_origin": "TOPLEFT" }, "text": " illuminator.", "orig": " illuminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 205, "label": "text", "bbox": { "l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 145, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 265.23395000000005, "r_x1": 366.93256, "r_y1": 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"coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.63751, "r_y0": 266.84418000000005, "r_x1": 387.98407, "r_y1": 266.84418000000005, "r_x2": 387.98407, "r_y2": 265.10515999999996, "r_x3": 360.63751, "r_y3": 265.10515999999996, "coord_origin": "TOPLEFT" }, "text": "Turn the focusing knob away or toward", "orig": "Turn the focusing knob away or toward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 208, "label": "text", "bbox": { "l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42023, "r_y0": 268.45441000000005, "r_x1": 384.58948, "r_y1": 268.45441000000005, "r_x2": 384.58948, "r_y2": 266.71533, "r_x3": 358.42023, "r_y3": 266.71533, "coord_origin": "TOPLEFT" }, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 209, "label": "text", "bbox": { "l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42166, "r_y0": 270.06458, "r_x1": 359.78549, "r_y1": 270.06458, "r_x2": 359.78549, "r_y2": 268.32556, "r_x3": 358.42166, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 210, "label": "text", "bbox": { "l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.46741, "r_y0": 270.06458, "r_x1": 384.33441, "r_y1": 270.06458, "r_x2": 384.33441, "r_y2": 268.32556, "r_x3": 360.46741, "r_y3": 268.32556, "coord_origin": "TOPLEFT" }, "text": "If the image is unclear, adjust the", "orig": "If the image is unclear, adjust the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 211, "label": "text", "bbox": { "l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 271.6748, "r_x1": 384.61502, "r_y1": 271.6748, "r_x2": 384.61502, "r_y2": 269.93579, "r_x3": 358.4231, "r_y3": 269.93579, "coord_origin": "TOPLEFT" }, "text": " height of the elevator up or down,", "orig": " height of the elevator up or down,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 212, "label": "text", "bbox": { "l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 273.28503, "r_x1": 385.38922, "r_y1": 273.28503, "r_x2": 385.38922, "r_y2": 271.54602, "r_x3": 358.4231, "r_y3": 271.54602, "coord_origin": "TOPLEFT" }, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 213, "label": "text", "bbox": { "l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 276.50543000000005, "r_x1": 377.35046, "r_y1": 276.50543000000005, "r_x2": 377.35046, "r_y2": 274.76642000000004, "r_x3": 358.4231, "r_y3": 274.76642000000004, "coord_origin": "TOPLEFT" }, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 214, "label": "text", "bbox": { "l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 278.11566000000005, "r_x1": 359.89429, "r_y1": 278.11566000000005, "r_x2": 359.89429, "r_y2": 276.37665000000004, "r_x3": 358.4231, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 215, "label": "text", "bbox": { "l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.62988, "r_y0": 278.11566000000005, "r_x1": 386.37589, "r_y1": 278.11566000000005, "r_x2": 386.37589, "r_y2": 276.37665000000004, "r_x3": 360.62988, "r_y3": 276.37665000000004, "coord_origin": "TOPLEFT" }, "text": "Turn the zoom magnification knob to", "orig": "Turn the zoom magnification knob to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 216, "label": "text", "bbox": { "l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 279.72589000000005, "r_x1": 386.78732, "r_y1": 279.72589000000005, "r_x2": 386.78732, "r_y2": 277.98688000000004, "r_x3": 358.4231, "r_y3": 277.98688000000004, "coord_origin": "TOPLEFT" }, "text": " the desired magnification and field of", "orig": " the desired magnification and field of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 217, "label": "text", "bbox": { "l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 281.33609, "r_x1": 364.16855, "r_y1": 281.33609, "r_x2": 364.16855, "r_y2": 279.59704999999997, "r_x3": 358.4231, "r_y3": 279.59704999999997, "coord_origin": "TOPLEFT" }, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 218, "label": "text", "bbox": { "l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.4231, "r_y0": 282.94632, "r_x1": 359.86777, "r_y1": 282.94632, "r_x2": 359.86777, "r_y2": 281.20728, "r_x3": 358.4231, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 219, "label": "text", "bbox": { "l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.59012, "r_y0": 282.94632, "r_x1": 387.31656, "r_y1": 282.94632, "r_x2": 387.31656, "r_y2": 281.20728, "r_x3": 360.59012, "r_y3": 281.20728, "coord_origin": "TOPLEFT" }, "text": "In most situations, it is recommended", "orig": "In most situations, it is recommended", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 220, "label": "text", "bbox": { "l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 160, "rgba": { "r": 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], "children": [] }, { "id": 222, "label": "text", "bbox": { "l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 287.77695, "r_x1": 382.77115, "r_y1": 287.77695, "r_x2": 382.77115, "r_y2": 286.03793, "r_x3": 358.42453, "r_y3": 286.03793, "coord_origin": "TOPLEFT" }, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 223, "label": "text", "bbox": { "l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 289.38718, "r_x1": 367.98694, "r_y1": 289.38718, "r_x2": 367.98694, "r_y2": 287.64813, "r_x3": 358.42453, "r_y3": 287.64813, "coord_origin": "TOPLEFT" }, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 224, "label": "text", "bbox": { "l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 290.99738, "r_x1": 359.80386, "r_y1": 290.99738, "r_x2": 359.80386, "r_y2": 289.25836, "r_x3": 358.42453, "r_y3": 289.25836, "coord_origin": "TOPLEFT" }, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 225, "label": "text", "bbox": { "l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.49353, "r_y0": 290.99738, "r_x1": 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"DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 229, "label": "text", "bbox": { "l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 299.04846, "r_x1": 359.83682, "r_y1": 299.04846, "r_x2": 359.83682, "r_y2": 297.30942, "r_x3": 358.42453, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 230, "label": "text", "bbox": { "l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 360.54297, "r_y0": 299.04846, "r_x1": 388.08289, "r_y1": 299.04846, "r_x2": 388.08289, "r_y2": 297.30942, "r_x3": 360.54297, "r_y3": 297.30942, "coord_origin": "TOPLEFT" }, "text": "To adjust the eyepiece for viewing with", "orig": "To adjust the eyepiece for viewing with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 231, "label": "text", "bbox": { "l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 300.65866, "r_x1": 382.73251, "r_y1": 300.65866, "r_x2": 382.73251, "r_y2": 298.91965, "r_x3": 358.42453, "r_y3": 298.91965, "coord_origin": "TOPLEFT" }, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 232, "label": "text", "bbox": { "l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": 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eyes, follow the following", "orig": " and left eyes, follow the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 234, "label": "text", "bbox": { "l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 305.48932, "r_x1": 364.88672, "r_y1": 305.48932, "r_x2": 364.88672, "r_y2": 303.75027, "r_x3": 358.42453, "r_y3": 303.75027, "coord_origin": "TOPLEFT" }, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 235, "label": "text", "bbox": { "l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 307.09952000000004, "r_x1": 359.95078, "r_y1": 307.09952000000004, "r_x2": 359.95078, "r_y2": 305.3605, "r_x3": 358.42453, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "a.", "orig": "a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 236, "label": "text", "bbox": { "l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.47699, "r_y0": 307.09952000000004, "r_x1": 386.65988, "r_y1": 307.09952000000004, "r_x2": 386.65988, "r_y2": 305.3605, "r_x3": 361.47699, "r_y3": 305.3605, "coord_origin": "TOPLEFT" }, "text": "Observe an image through the left", "orig": "Observe an image through the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 237, "label": "text", "bbox": { "l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 308.70975, "r_x1": 386.7634, "r_y1": 308.70975, "r_x2": 386.7634, "r_y2": 306.9707, "r_x3": 358.42453, "r_y3": 306.9707, "coord_origin": "TOPLEFT" }, "text": " eyepiece and bring a specific point", "orig": " eyepiece and bring a specific point", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 238, "label": "text", "bbox": { "l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 310.31995, "r_x1": 385.41354, "r_y1": 310.31995, "r_x2": 385.41354, "r_y2": 308.58093, "r_x3": 358.42453, "r_y3": 308.58093, "coord_origin": "TOPLEFT" }, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 239, "label": "text", "bbox": { "l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42453, "r_y0": 311.93018, "r_x1": 359.93304, "r_y1": 311.93018, "r_x2": 359.93304, "r_y2": 310.19113, "r_x3": 358.42453, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "b.", "orig": "b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 240, "label": "text", "bbox": { "l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 361.44156, "r_y0": 311.93018, "r_x1": 382.56085, "r_y1": 311.93018, "r_x2": 382.56085, "r_y2": 310.19113, "r_x3": 361.44156, "r_y3": 310.19113, "coord_origin": "TOPLEFT" }, "text": "By turning the diopter ring ", "orig": "By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 241, "label": "text", "bbox": { "l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 313.54037, "r_x1": 385.4559, "r_y1": 313.54037, "r_x2": 385.4559, "r_y2": 311.80136, "r_x3": 358.42596, "r_y3": 311.80136, "coord_origin": "TOPLEFT" }, "text": " adjustment for the left eyepiece,", "orig": " adjustment for the left eyepiece,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 242, "label": "text", "bbox": { "l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 315.15059999999994, "r_x1": 384.56122, "r_y1": 315.15059999999994, "r_x2": 384.56122, "r_y2": 313.41156, "r_x3": 358.42596, "r_y3": 313.41156, "coord_origin": "TOPLEFT" }, "text": " bring the same point into sharp", "orig": " bring the same point into sharp", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 243, "label": "text", "bbox": { "l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 316.76079999999996, "r_x1": 366.74371, "r_y1": 316.76079999999996, "r_x2": 366.74371, "r_y2": 315.02178999999995, "r_x3": 358.42596, "r_y3": 315.02178999999995, "coord_origin": "TOPLEFT" }, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 244, "label": "text", "bbox": { "l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 318.37103, "r_x1": 383.93884, "r_y1": 318.37103, "r_x2": 383.93884, "r_y2": 316.63199, "r_x3": 358.42596, "r_y3": 316.63199, "coord_origin": "TOPLEFT" }, "text": " c.Then bring the same point into", "orig": " c.Then bring the same point into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 245, "label": "text", "bbox": { "l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 319.9812299999999, "r_x1": 385.69241, "r_y1": 319.9812299999999, "r_x2": 385.69241, "r_y2": 318.2422199999999, "r_x3": 358.42596, "r_y3": 318.2422199999999, "coord_origin": "TOPLEFT" }, "text": " focus through the right eyepiece", "orig": " focus through the right eyepiece", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 246, "label": "text", "bbox": { "l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 321.59146, "r_x1": 385.94861, "r_y1": 321.59146, "r_x2": 385.94861, "r_y2": 319.85242000000005, "r_x3": 358.42596, "r_y3": 319.85242000000005, "coord_origin": "TOPLEFT" }, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 247, "label": "text", "bbox": { "l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 323.20166, "r_x1": 385.54236, "r_y1": 323.20166, "r_x2": 385.54236, "r_y2": 321.46265, "r_x3": 358.42596, "r_y3": 321.46265, "coord_origin": "TOPLEFT" }, "text": " d.With more than one viewer, each", "orig": " d.With more than one viewer, each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 248, "label": "text", "bbox": { "l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 324.81189, "r_x1": 382.98718, "r_y1": 324.81189, "r_x2": 382.98718, "r_y2": 323.07285, "r_x3": 358.42596, "r_y3": 323.07285, "coord_origin": "TOPLEFT" }, "text": " viewer should note their own", "orig": " viewer should note their own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 249, "label": "text", "bbox": { "l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.42596, "r_y0": 326.42209, "r_x1": 385.06448, "r_y1": 326.42209, "r_x2": 385.06448, "r_y2": 324.68307000000004, "r_x3": 358.42596, "r_y3": 324.68307000000004, "coord_origin": "TOPLEFT" }, "text": " diopter ring position for the left", "orig": " diopter ring position for the left", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 250, "label": "text", "bbox": { "l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 190, "rgba": { 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"id": 311, "label": "text", "bbox": { "l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 251, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 380.78412, "r_x1": 466.08835000000005, "r_y1": 380.78412, "r_x2": 466.08835000000005, "r_y2": 378.66302, "r_x3": 449.10074000000003, "r_y3": 378.66302, "coord_origin": "TOPLEFT" }, "text": "Circling Minimums", "orig": "Circling Minimums", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 312, "label": "text", "bbox": { "l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 252, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 385.34148999999996, "r_x1": 449.64444, "r_y1": 385.34148999999996, "r_x2": 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To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. 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"coord_origin": "TOPLEFT" }, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 323, "label": "text", "bbox": { "l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 263, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 501.13077, "r_y0": 413.21521, "r_x1": 505.2477999999999, "r_y1": 413.21521, "r_x2": 505.2477999999999, "r_y2": 411.30624, "r_x3": 501.13077, "r_y3": 411.30624, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 324, "label": "text", "bbox": { "l": 449.10074000000003, 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The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 329, "label": "text", "bbox": { "l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 269, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 436.32492, "r_x1": 502.39545, "r_y1": 436.32492, "r_x2": 502.39545, "r_y2": 434.20383, "r_x3": 449.10074000000003, "r_y3": 434.20383, "coord_origin": "TOPLEFT" }, "text": "dimensions (length and width) are shown for all active runways.", "orig": "dimensions (length and width) are shown for all active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 330, "label": "text", "bbox": { "l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 270, 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E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 349, "label": "text", "bbox": { "l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 289, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 547.82623, "r_y0": 478.28687, "r_x1": 548.45862, "r_y1": 478.28687, "r_x2": 548.45862, "r_y2": 476.16577, "r_x3": 547.82623, "r_y3": 476.16577, "coord_origin": "TOPLEFT" }, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 350, "label": "text", "bbox": { "l": 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449.10074000000003, "r_y3": 483.00183, "coord_origin": "TOPLEFT" }, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 28, "label": "text", "bbox": { "l": 449.10074000000003, "t": 487.5592, "r": 551.80023, "b": 496.51636, "coord_origin": "TOPLEFT" }, "confidence": 0.5027860403060913, "cells": [ { "index": 292, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 489.6803, "r_x1": 549.16168, "r_y1": 489.6803, "r_x2": 549.16168, "r_y2": 487.5592, "r_x3": 449.10074000000003, "r_y3": 487.5592, "coord_origin": "TOPLEFT" }, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 293, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 491.95898, "r_x1": 546.90881, "r_y1": 491.95898, "r_x2": 546.90881, "r_y2": 489.83789, "r_x3": 449.10074000000003, "r_y3": 489.83789, "coord_origin": "TOPLEFT" }, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 294, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 494.23767, "r_x1": 551.80023, "r_y1": 494.23767, "r_x2": 551.80023, "r_y2": 492.11658, "r_x3": 449.10074000000003, "r_y3": 492.11658, "coord_origin": "TOPLEFT" }, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "orig": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 295, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.10074000000003, "r_y0": 496.51636, "r_x1": 505.85068000000007, "r_y1": 496.51636, "r_x2": 505.85068000000007, "r_y2": 494.39526, "r_x3": 449.10074000000003, "r_y3": 494.39526, "coord_origin": "TOPLEFT" }, "text": "the landing surface. Circling only approaches will not show a TDZE.", "orig": "the landing surface. Circling only approaches will not show a TDZE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 352, "label": "text", "bbox": { "l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 296, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.80661000000003, "r_y0": 519.01764, "r_x1": 502.08792, "r_y1": 519.01764, "r_x2": 502.08792, "r_y2": 515.9437, "r_x3": 498.80661000000003, "r_y3": 515.9437, "coord_origin": "TOPLEFT" }, "text": "114", "orig": "114", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 353, "label": "text", "bbox": { "l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 297, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 444.56319999999994, "r_y0": 471.87128, 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", "orig": "electricity needs of Australian households. 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your vehicle", "orig": "chapter 2, you and your vehicle", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 403, "label": "text", "bbox": { "l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 347, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 524.37036, "r_y0": 265.92719, "r_x1": 539.89124, "r_y1": 265.92719, "r_x2": 539.89124, "r_y2": 263.88922, "r_x3": 524.37036, "r_y3": 263.88922, "coord_origin": "TOPLEFT" }, "text": ", you learned about ", "orig": ", you learned about ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 404, "label": "text", "bbox": { "l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 348, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 405, "label": "text", "bbox": { "l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 349, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 270.01331000000005, "r_x1": 544.01343, "r_y1": 270.01331000000005, "r_x2": 544.01343, "r_y2": 267.97533999999996, "r_x3": 498.15335, "r_y3": 267.97533999999996, "coord_origin": "TOPLEFT" }, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 406, "label": "text", "bbox": { "l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 350, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 272.05634, "r_x1": 544.11987, "r_y1": 272.05634, "r_x2": 544.11987, "r_y2": 270.01831000000004, "r_x3": 498.15335, "r_y3": 270.01831000000004, "coord_origin": "TOPLEFT" }, "text": "signs, signals and road markings that keep traffi c organized ", "orig": "signs, signals and road markings that keep traffi c organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 407, "label": "text", "bbox": { "l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 351, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 498.15335, "r_y0": 274.09937, "r_x1": 515.41071, "r_y1": 274.09937, "r_x2": 515.41071, "r_y2": 272.06140000000005, "r_x3": 498.15335, "r_y3": 272.06140000000005, "coord_origin": "TOPLEFT" }, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 163.19317999999998, "r_x1": 294.04349, "r_y1": 163.19317999999998, "r_x2": 294.04349, "r_y2": 154.81853999999998, "r_x3": 53.79800000000001, "r_y3": 154.81853999999998, "coord_origin": "TOPLEFT" }, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 174.15215999999998, "r_x1": 294.04718, "r_y1": 174.15215999999998, "r_x2": 294.04718, "r_y2": 165.77752999999996, "r_x3": 53.79800000000001, "r_y3": 165.77752999999996, "coord_origin": "TOPLEFT" }, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 185.11114999999995, "r_x1": 294.04919, "r_y1": 185.11114999999995, "r_x2": 294.04919, "r_y2": 176.73650999999995, "r_x3": 53.79800000000001, "r_y3": 176.73650999999995, "coord_origin": "TOPLEFT" }, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 196.07019000000003, "r_x1": 294.27573, "r_y1": 196.07019000000003, "r_x2": 294.27573, "r_y2": 187.69556, "r_x3": 53.79800000000001, "r_y3": 187.69556, "coord_origin": "TOPLEFT" }, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 207.02917000000002, "r_x1": 295.42569, "r_y1": 207.02917000000002, "r_x2": 295.42569, "r_y2": 198.65454, "r_x3": 53.79800000000001, "r_y3": 198.65454, "coord_origin": "TOPLEFT" }, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 217.98816, "r_x1": 294.37256, "r_y1": 217.98816, "r_x2": 294.37256, "r_y2": 209.61352999999997, "r_x3": 53.528999, "r_y3": 209.61352999999997, "coord_origin": "TOPLEFT" }, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 228.94714, "r_x1": 275.48334, "r_y1": 228.94714, "r_x2": 275.48334, "r_y2": 220.57250999999997, "r_x3": 53.79800000000001, "r_y3": 220.57250999999997, "coord_origin": "TOPLEFT" }, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 239.90619000000004, "r_x1": 295.564, "r_y1": 239.90619000000004, "r_x2": 295.564, "r_y2": 231.53156, "r_x3": 63.76100199999999, "r_y3": 231.53156, "coord_origin": "TOPLEFT" }, "text": "A key problem in the process of document conversion is to under-", "orig": "A key problem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 250.86517000000003, "r_x1": 294.04868, "r_y1": 250.86517000000003, "r_x2": 294.04868, "r_y2": 242.49054, "r_x3": 53.79800000000001, "r_y3": 242.49054, "coord_origin": "TOPLEFT" }, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 261.82318, "r_x1": 294.04532, "r_y1": 261.82318, "r_x2": 294.04532, "r_y2": 253.44854999999995, "r_x3": 53.79800000000001, "r_y3": 253.44854999999995, "coord_origin": "TOPLEFT" }, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 272.78216999999995, "r_x1": 295.55618, "r_y1": 272.78216999999995, "r_x2": 295.55618, "r_y2": 264.40752999999995, "r_x3": 53.79800000000001, "r_y3": 264.40752999999995, "coord_origin": "TOPLEFT" }, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 283.74118, "r_x1": 294.04059, "r_y1": 283.74118, "r_x2": 294.04059, "r_y2": 275.36658, "r_x3": 53.79800000000001, "r_y3": 275.36658, "coord_origin": "TOPLEFT" }, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 294.70016, "r_x1": 295.55783, "r_y1": 294.70016, "r_x2": 295.55783, "r_y2": 286.32552999999996, "r_x3": 53.79800000000001, "r_y3": 286.32552999999996, "coord_origin": "TOPLEFT" }, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 305.65918000000005, "r_x1": 294.04538, "r_y1": 305.65918000000005, "r_x2": 294.04538, "r_y2": 297.28455, "r_x3": 53.79800000000001, "r_y3": 297.28455, "coord_origin": "TOPLEFT" }, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 316.61816, "r_x1": 294.043, "r_y1": 316.61816, "r_x2": 294.043, "r_y2": 308.24353, "r_x3": 53.79800000000001, "r_y3": 308.24353, "coord_origin": "TOPLEFT" }, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 327.57718, "r_x1": 295.56412, "r_y1": 327.57718, "r_x2": 295.56412, "r_y2": 319.20255, "r_x3": 53.79800000000001, "r_y3": 319.20255, "coord_origin": "TOPLEFT" }, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 338.53616, "r_x1": 294.04532, "r_y1": 338.53616, "r_x2": 294.04532, "r_y2": 330.16153, "r_x3": 53.79800000000001, "r_y3": 330.16153, "coord_origin": "TOPLEFT" }, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 349.49518, "r_x1": 294.04538, "r_y1": 349.49518, "r_x2": 294.04538, "r_y2": 341.12054, "r_x3": 53.79800000000001, "r_y3": 341.12054, "coord_origin": "TOPLEFT" }, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 360.45416000000006, "r_x1": 294.04712, "r_y1": 360.45416000000006, "r_x2": 294.04712, "r_y2": 352.07953, "r_x3": 53.79800000000001, "r_y3": 352.07953, "coord_origin": "TOPLEFT" }, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 371.41318, "r_x1": 294.04538, "r_y1": 371.41318, "r_x2": 294.04538, "r_y2": 363.03853999999995, "r_x3": 53.79800000000001, "r_y3": 363.03853999999995, "coord_origin": "TOPLEFT" }, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 382.37119, "r_x1": 295.55643, "r_y1": 382.37119, "r_x2": 295.55643, "r_y2": 373.99655, "r_x3": 53.79800000000001, "r_y3": 373.99655, "coord_origin": "TOPLEFT" }, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 393.33017, "r_x1": 246.75909, "r_y1": 393.33017, "r_x2": 246.75909, "r_y2": 384.95554, "r_x3": 53.79800000000001, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "itories (PubMed and arXiv), which provide XML or L", "orig": "itories (PubMed and arXiv), which provide XML or L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 243.53101000000004, "r_y0": 391.82455, "r_x1": 248.58553000000003, "r_y1": 391.82455, "r_x2": 248.58553000000003, "r_y2": 385.03183000000007, "r_x3": 243.53101000000004, "r_y3": 385.03183000000007, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 247.24099999999999, "r_y0": 393.33017, "r_x1": 252.58859, "r_y1": 393.33017, "r_x2": 252.58859, "r_y2": 384.95554, "r_x3": 247.24099999999999, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 251.09398999999996, "r_y0": 395.25418, "r_x1": 256.08829, "r_y1": 395.25418, "r_x2": 256.08829, "r_y2": 386.87954999999994, "r_x3": 251.09398999999996, "r_y3": 386.87954999999994, "coord_origin": "TOPLEFT" }, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 254.967, "r_y0": 393.33017, "r_x1": 261.33725, "r_y1": 393.33017, "r_x2": 261.33725, "r_y2": 384.95554, "r_x3": 254.967, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "X", "orig": "X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 263.75021, "r_y0": 393.33017, "r_x1": 295.42773, "r_y1": 393.33017, "r_x2": 295.42773, "r_y2": 384.95554, "r_x3": 263.75021, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 404.28918, "r_x1": 294.21713, "r_y1": 404.28918, "r_x2": 294.21713, "r_y2": 395.91455, "r_x3": 53.528999, "r_y3": 395.91455, "coord_origin": "TOPLEFT" }, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 415.24817, "r_x1": 294.27386, "r_y1": 415.24817, "r_x2": 294.27386, "r_y2": 406.87354, "r_x3": 53.79800000000001, "r_y3": 406.87354, "coord_origin": "TOPLEFT" }, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 426.20717999999994, "r_x1": 295.03488, "r_y1": 426.20717999999994, "r_x2": 295.03488, "r_y2": 417.8325500000001, "r_x3": 53.79800000000001, "r_y3": 417.8325500000001, "coord_origin": "TOPLEFT" }, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 437.16617, "r_x1": 294.04691, "r_y1": 437.16617, "r_x2": 294.04691, "r_y2": 428.79153, "r_x3": 53.79800000000001, "r_y3": 428.79153, "coord_origin": "TOPLEFT" }, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 448.12518, "r_x1": 294.04865, "r_y1": 448.12518, "r_x2": 294.04865, "r_y2": 439.75055, "r_x3": 53.57400100000001, "r_y3": 439.75055, "coord_origin": "TOPLEFT" }, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 459.08417, "r_x1": 294.21643, "r_y1": 459.08417, "r_x2": 294.21643, "r_y2": 450.7095299999999, "r_x3": 53.79800000000001, "r_y3": 450.7095299999999, "coord_origin": "TOPLEFT" }, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 470.04318, "r_x1": 295.42181, "r_y1": 470.04318, "r_x2": 295.42181, "r_y2": 461.66855, "r_x3": 53.79800000000001, "r_y3": 461.66855, "coord_origin": "TOPLEFT" }, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 481.00217, "r_x1": 125.52795, "r_y1": 481.00217, "r_x2": 125.52795, "r_y2": 472.62753, "r_x3": 53.79800000000001, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 128.608, "r_y0": 481.01114, "r_x1": 153.7679, "r_y1": 481.01114, "r_x2": 153.7679, "r_y2": 472.67239, "r_x3": 128.608, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.15199, "r_y0": 481.00217, "r_x1": 165.16365, "r_y1": 481.00217, "r_x2": 165.16365, "r_y2": 472.62753, "r_x3": 157.15199, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.248, "r_y0": 481.01114, "r_x1": 201.49272, "r_y1": 481.01114, "r_x2": 201.49272, "r_y2": 472.67239, "r_x3": 168.248, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.78799, "r_y0": 481.00217, "r_x1": 294.21494, "r_y1": 481.00217, "r_x2": 294.21494, "r_y2": 472.62753, "r_x3": 204.78799, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 491.96017, "r_x1": 294.04715, "r_y1": 491.96017, "r_x2": 294.04715, "r_y2": 483.58554, "r_x3": 53.79800000000001, "r_y3": 483.58554, "coord_origin": "TOPLEFT" }, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 502.91916, "r_x1": 89.080788, "r_y1": 502.91916, "r_x2": 89.080788, "r_y2": 494.54453, "r_x3": 53.79800000000001, "r_y3": 494.54453, "coord_origin": "TOPLEFT" }, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 513.87817, "r_x1": 295.56396, "r_y1": 513.87817, "r_x2": 295.56396, "r_y2": 505.50354, "r_x3": 63.76100199999999, "r_y3": 505.50354, "coord_origin": "TOPLEFT" }, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 524.83716, "r_x1": 294.21271, "r_y1": 524.83716, "r_x2": 294.21271, "r_y2": 516.46252, "r_x3": 53.79800000000001, "r_y3": 516.46252, "coord_origin": "TOPLEFT" }, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.591999, "r_y0": 535.7961700000001, "r_x1": 61.92275599999999, "r_y1": 535.7961700000001, "r_x2": 61.92275599999999, "r_y2": 527.42154, "r_x3": 53.591999, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.162201, "r_y0": 535.7961700000001, "r_x1": 294.04626, "r_y1": 535.7961700000001, "r_x2": 294.04626, "r_y2": 527.42154, "r_x3": 64.162201, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "distinct class labels on 80863 unique document pages, of which", "orig": "distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 546.75517, "r_x1": 294.21228, "r_y1": 546.75517, "r_x2": 294.21228, "r_y2": 538.38055, "r_x3": 53.79800000000001, "r_y3": 538.38055, "coord_origin": "TOPLEFT" }, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 557.71417, "r_x1": 294.04709, "r_y1": 557.71417, "r_x2": 294.04709, "r_y2": 549.33955, "r_x3": 53.79800000000001, "r_y3": 549.33955, "coord_origin": "TOPLEFT" }, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 568.67317, "r_x1": 134.28951, "r_y1": 568.67317, "r_x2": 134.28951, "r_y2": 560.29855, "r_x3": 53.79800000000001, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.29201, "r_y0": 565.0235299999999, "r_x1": 137.67381, "r_y1": 565.0235299999999, "r_x2": 137.67381, "r_y2": 558.23083, "r_x3": 134.29201, "r_y3": 558.23083, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 140.418, "r_y0": 568.67317, "r_x1": 294.047, "r_y1": 568.67317, "r_x2": 294.047, "r_y2": 560.29855, "r_x3": 140.418, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 579.63217, "r_x1": 295.10538, "r_y1": 579.63217, "r_x2": 295.10538, "r_y2": 571.25755, "r_x3": 53.79800000000001, "r_y3": 571.25755, "coord_origin": "TOPLEFT" }, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 592.95618, "r_x1": 74.221352, "r_y1": 592.95618, "r_x2": 74.221352, "r_y2": 584.58156, "r_x3": 64.708, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 592.96515, "r_x1": 146.39589, "r_y1": 592.96515, "r_x2": 146.39589, "r_y2": 584.62639, "r_x3": 78.207001, "r_y3": 584.62639, "coord_origin": "TOPLEFT" }, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 146.41701, "r_y0": 592.95618, "r_x1": 295.03036, "r_y1": 592.95618, "r_x2": 295.03036, "r_y2": 584.58156, "r_x3": 146.41701, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": ": In contrast to PubLayNet and DocBank,", "orig": ": In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.875, "r_y0": 603.91518, "r_x1": 295.56165, "r_y1": 603.91518, "r_x2": 295.56165, "r_y2": 595.54056, "r_x3": 77.875, "r_y3": 595.54056, "coord_origin": "TOPLEFT" }, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 614.87418, "r_x1": 200.6432, "r_y1": 614.87418, "r_x2": 200.6432, "r_y2": 606.49956, "r_x3": 78.207001, "r_y3": 606.49956, "coord_origin": "TOPLEFT" }, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 625.8331800000001, "r_x1": 74.221352, "r_y1": 625.8331800000001, "r_x2": 74.221352, "r_y2": 617.45856, "r_x3": 64.708, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 625.84215, "r_x1": 167.91745, "r_y1": 625.84215, "r_x2": 167.91745, "r_y2": 617.50339, "r_x3": 78.207001, "r_y3": 617.50339, "coord_origin": "TOPLEFT" }, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.33501, "r_y0": 625.8331800000001, "r_x1": 294.26254, "r_y1": 625.8331800000001, "r_x2": 294.26254, "r_y2": 617.45856, "r_x3": 168.33501, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": ": We include diverse and complex", "orig": ": We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 636.79117, "r_x1": 245.45726000000002, "r_y1": 636.79117, "r_x2": 245.45726000000002, "r_y2": 628.41655, "r_x3": 78.207001, "r_y3": 628.41655, "coord_origin": "TOPLEFT" }, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 647.75017, "r_x1": 74.221352, "r_y1": 647.75017, "r_x2": 74.221352, "r_y2": 639.37555, "r_x3": 64.708, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 647.75914, "r_x1": 143.51663, "r_y1": 647.75914, "r_x2": 143.51663, "r_y2": 639.42038, "r_x3": 78.207001, "r_y3": 639.42038, "coord_origin": "TOPLEFT" }, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.02, "r_y0": 647.75017, "r_x1": 294.04648, "r_y1": 647.75017, "r_x2": 294.04648, "r_y2": 639.37555, "r_x3": 144.02, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": ": We define 11 class labels to distinguish", "orig": ": We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 658.70917, "r_x1": 294.68381, "r_y1": 658.70917, "r_x2": 294.68381, "r_y2": 650.33455, "r_x3": 78.207001, "r_y3": 650.33455, "coord_origin": "TOPLEFT" }, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 669.66817, "r_x1": 276.33752, "r_y1": 669.66817, "r_x2": 276.33752, "r_y2": 661.29355, "r_x3": 78.207001, "r_y3": 661.29355, "coord_origin": "TOPLEFT" }, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 680.62717, "r_x1": 74.221352, "r_y1": 680.62717, "r_x2": 74.221352, "r_y2": 672.25256, "r_x3": 64.708, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 680.63614, "r_x1": 163.78357, "r_y1": 680.63614, "r_x2": 163.78357, "r_y2": 672.29739, "r_x3": 78.207001, "r_y3": 672.29739, "coord_origin": "TOPLEFT" }, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 163.994, "r_y0": 680.62717, "r_x1": 295.56439, "r_y1": 680.62717, "r_x2": 295.56439, "r_y2": 672.25256, "r_x3": 163.994, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": ": A fraction of the pages in the Do-", "orig": ": A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 691.58617, "r_x1": 295.42719, "r_y1": 691.58617, "r_x2": 295.42719, "r_y2": 683.21156, "r_x3": 78.207001, "r_y3": 683.21156, "coord_origin": "TOPLEFT" }, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.672001, "r_y0": 708.739891, "r_x1": 216.02750000000003, "r_y1": 708.739891, "r_x2": 216.02750000000003, "r_y2": 702.226364, "r_x3": 53.672001, "r_y3": 702.226364, "coord_origin": "TOPLEFT" }, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.095, "r_y0": 95.73816, "r_x1": 558.43201, "r_y1": 95.73816, "r_x2": 558.43201, "r_y2": 87.36352999999997, "r_x3": 342.095, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 106.69713999999999, "r_x1": 445.83629999999994, "r_y1": 106.69713999999999, "r_x2": 445.83629999999994, "r_y2": 98.32250999999997, "r_x3": 342.36401, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.86502, "r_y0": 117.65612999999996, "r_x1": 338.37836, "r_y1": 117.65612999999996, "r_x2": 338.37836, "r_y2": 109.28148999999996, "r_x3": 328.86502, "r_y3": 109.28148999999996, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 117.66516000000001, "r_x1": 487.25296, "r_y1": 117.66516000000001, "r_x2": 487.25296, "r_y2": 109.32641999999998, "r_x3": 342.36401, "r_y3": 109.32641999999998, "coord_origin": "TOPLEFT" }, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.75900000000007, "r_y0": 117.65618999999992, "r_x1": 558.20294, "r_y1": 117.65618999999992, "r_x2": 558.20294, "r_y2": 109.28156000000001, "r_x3": 487.75900000000007, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": ": Like DocBank, we", "orig": ": Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 128.61517000000003, "r_x1": 559.72101, "r_y1": 128.61517000000003, "r_x2": 559.72101, "r_y2": 120.24054000000001, "r_x3": 342.36401, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 139.57317999999998, "r_x1": 558.20117, "r_y1": 139.57317999999998, "r_x2": 558.20117, "r_y2": 131.19854999999995, "r_x3": 342.36401, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 150.53216999999995, "r_x1": 558.20087, "r_y1": 150.53216999999995, "r_x2": 558.20087, "r_y2": 142.15752999999995, "r_x3": 342.36401, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 161.49114999999995, "r_x1": 438.0624399999999, "r_y1": 161.49114999999995, "r_x2": 438.0624399999999, "r_y2": 153.11652000000004, "r_x3": 342.36401, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 176.35015999999996, "r_x1": 559.19031, "r_y1": 176.35015999999996, "r_x2": 559.19031, "r_y2": 167.97551999999996, "r_x3": 327.918, "r_y3": 167.97551999999996, "coord_origin": "TOPLEFT" }, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 187.30913999999996, "r_x1": 558.20422, "r_y1": 187.30913999999996, "r_x2": 558.20422, "r_y2": 178.93451000000005, "r_x3": 317.62299, "r_y3": 178.93451000000005, "coord_origin": "TOPLEFT" }, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 198.26819, "r_x1": 558.19763, "r_y1": 198.26819, "r_x2": 558.19763, "r_y2": 189.89355, "r_x3": 317.95499, "r_y3": 189.89355, "coord_origin": "TOPLEFT" }, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 209.22717, "r_x1": 558.20612, "r_y1": 209.22717, "r_x2": 558.20612, "r_y2": 200.85253999999998, "r_x3": 317.95499, "r_y3": 200.85253999999998, "coord_origin": "TOPLEFT" }, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 220.18615999999997, "r_x1": 434.94861, "r_y1": 220.18615999999997, "r_x2": 434.94861, "r_y2": 211.81151999999997, "r_x3": 317.95499, "r_y3": 211.81151999999997, "coord_origin": "TOPLEFT" }, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 231.14520000000005, "r_x1": 558.19836, "r_y1": 231.14520000000005, "r_x2": 558.19836, "r_y2": 222.77057000000002, "r_x3": 327.918, "r_y3": 222.77057000000002, "coord_origin": "TOPLEFT" }, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.73099, "r_y0": 242.10419000000002, "r_x1": 558.1991, "r_y1": 242.10419000000002, "r_x2": 558.1991, "r_y2": 233.72955000000002, "r_x3": 317.73099, "r_y3": 233.72955000000002, "coord_origin": "TOPLEFT" }, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 253.06317, "r_x1": 558.20416, "r_y1": 253.06317, "r_x2": 558.20416, "r_y2": 244.68854, "r_x3": 317.95499, "r_y3": 244.68854, "coord_origin": "TOPLEFT" }, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 264.02216, "r_x1": 558.20563, "r_y1": 264.02216, "r_x2": 558.20563, "r_y2": 255.64752, "r_x3": 317.95499, "r_y3": 255.64752, "coord_origin": "TOPLEFT" }, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 274.98015999999996, "r_x1": 558.19861, "r_y1": 274.98015999999996, "r_x2": 558.19861, "r_y2": 266.60553000000004, "r_x3": 317.95499, "r_y3": 266.60553000000004, "coord_origin": "TOPLEFT" }, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 285.93918, "r_x1": 558.20416, "r_y1": 285.93918, "r_x2": 558.20416, "r_y2": 277.56458, "r_x3": 317.95499, "r_y3": 277.56458, "coord_origin": "TOPLEFT" }, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 296.89816, "r_x1": 558.20239, "r_y1": 296.89816, "r_x2": 558.20239, "r_y2": 288.52353, "r_x3": 317.95499, "r_y3": 288.52353, "coord_origin": "TOPLEFT" }, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 307.85718, "r_x1": 559.58197, "r_y1": 307.85718, "r_x2": 559.58197, "r_y2": 299.48254, "r_x3": 317.95499, "r_y3": 299.48254, "coord_origin": "TOPLEFT" }, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.51797, "r_x1": 323.10388, "r_y1": 331.51797, "r_x2": 323.10388, "r_y2": 321.20889, "r_x3": 317.95499, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.12115, "r_y0": 331.51797, "r_x1": 421.74411, "r_y1": 331.51797, "r_x2": 421.74411, "r_y2": 321.20889, "r_x3": 333.12115, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.52499, "r_y0": 354.70717999999994, "r_x1": 559.71301, "r_y1": 354.70717999999994, "r_x2": 559.71301, "r_y2": 346.3325500000001, "r_x3": 317.52499, "r_y3": 346.3325500000001, "coord_origin": "TOPLEFT" }, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 365.66617, "r_x1": 558.20276, "r_y1": 365.66617, "r_x2": 558.20276, "r_y2": 357.29153, "r_x3": 317.95499, "r_y3": 357.29153, "coord_origin": "TOPLEFT" }, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 376.62518, "r_x1": 559.71564, "r_y1": 376.62518, "r_x2": 559.71564, "r_y2": 368.25055, "r_x3": 317.62299, "r_y3": 368.25055, "coord_origin": "TOPLEFT" }, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 387.58417, "r_x1": 558.43365, "r_y1": 387.58417, "r_x2": 558.43365, "r_y2": 379.2095299999999, "r_x3": 317.95499, "r_y3": 379.2095299999999, "coord_origin": "TOPLEFT" }, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 398.54318, "r_x1": 559.18658, "r_y1": 398.54318, "r_x2": 559.18658, "r_y2": 390.16855000000004, "r_x3": 317.95499, "r_y3": 390.16855000000004, "coord_origin": "TOPLEFT" }, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 409.50217, "r_x1": 558.20502, "r_y1": 409.50217, "r_x2": 558.20502, "r_y2": 401.12753, "r_x3": 317.95499, "r_y3": 401.12753, "coord_origin": "TOPLEFT" }, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 420.46118, "r_x1": 558.20422, "r_y1": 420.46118, "r_x2": 558.20422, "r_y2": 412.08655, "r_x3": 317.95499, "r_y3": 412.08655, "coord_origin": "TOPLEFT" }, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 431.4201699999999, "r_x1": 558.20477, "r_y1": 431.4201699999999, "r_x2": 558.20477, "r_y2": 423.04553, "r_x3": 317.95499, "r_y3": 423.04553, "coord_origin": "TOPLEFT" }, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 442.37817, "r_x1": 558.19952, "r_y1": 442.37817, "r_x2": 558.19952, "r_y2": 434.00354, "r_x3": 317.95499, "r_y3": 434.00354, "coord_origin": "TOPLEFT" }, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 453.33716, "r_x1": 559.71613, "r_y1": 453.33716, "r_x2": 559.71613, "r_y2": 444.96252, "r_x3": 317.95499, "r_y3": 444.96252, "coord_origin": "TOPLEFT" }, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 464.29617, "r_x1": 350.90652, "r_y1": 464.29617, "r_x2": 350.90652, "r_y2": 455.92154, "r_x3": 317.95499, "r_y3": 455.92154, "coord_origin": "TOPLEFT" }, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 475.25516, "r_x1": 558.19824, "r_y1": 475.25516, "r_x2": 558.19824, "r_y2": 466.88052, "r_x3": 327.918, "r_y3": 466.88052, "coord_origin": "TOPLEFT" }, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 486.21417, "r_x1": 558.20551, "r_y1": 486.21417, "r_x2": 558.20551, "r_y2": 477.83954, "r_x3": 317.95499, "r_y3": 477.83954, "coord_origin": "TOPLEFT" }, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 497.17319, "r_x1": 558.20575, "r_y1": 497.17319, "r_x2": 558.20575, "r_y2": 488.79855, "r_x3": 317.95499, "r_y3": 488.79855, "coord_origin": "TOPLEFT" }, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 508.13217, "r_x1": 559.18646, "r_y1": 508.13217, "r_x2": 559.18646, "r_y2": 499.75754, "r_x3": 317.95499, "r_y3": 499.75754, "coord_origin": "TOPLEFT" }, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 519.09119, "r_x1": 558.2002, "r_y1": 519.09119, "r_x2": 558.2002, "r_y2": 510.71655, "r_x3": 317.95499, "r_y3": 510.71655, "coord_origin": "TOPLEFT" }, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 530.05017, "r_x1": 558.20618, "r_y1": 530.05017, "r_x2": 558.20618, "r_y2": 521.67554, "r_x3": 317.95499, "r_y3": 521.67554, "coord_origin": "TOPLEFT" }, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 541.00917, "r_x1": 558.20239, "r_y1": 541.00917, "r_x2": 558.20239, "r_y2": 532.63455, "r_x3": 317.95499, "r_y3": 532.63455, "coord_origin": "TOPLEFT" }, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.96817, "r_x1": 503.32648, "r_y1": 551.96817, "r_x2": 503.32648, "r_y2": 543.59355, "r_x3": 317.95499, "r_y3": 543.59355, "coord_origin": "TOPLEFT" }, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.629, "r_x1": 322.97391, "r_y1": 575.629, "r_x2": 322.97391, "r_y2": 565.3199, "r_x3": 317.95499, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 332.73831, "r_y0": 575.629, "r_x1": 477.45688, "r_y1": 575.629, "r_x2": 477.45688, "r_y2": 565.3199, "r_x3": 332.73831, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 598.81818, "r_x1": 558.20233, "r_y1": 598.81818, "r_x2": 558.20233, "r_y2": 590.4435599999999, "r_x3": 317.95499, "r_y3": 590.4435599999999, "coord_origin": "TOPLEFT" }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 609.77718, "r_x1": 558.20239, "r_y1": 609.77718, "r_x2": 558.20239, "r_y2": 601.40256, "r_x3": 317.95499, "r_y3": 601.40256, "coord_origin": "TOPLEFT" }, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 620.73517, "r_x1": 559.7132, "r_y1": 620.73517, "r_x2": 559.7132, "r_y2": 612.36055, "r_x3": 317.95499, "r_y3": 612.36055, "coord_origin": "TOPLEFT" }, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 631.69417, "r_x1": 559.71313, "r_y1": 631.69417, "r_x2": 559.71313, "r_y2": 623.3195499999999, "r_x3": 317.95499, "r_y3": 623.3195499999999, "coord_origin": "TOPLEFT" }, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.65317, "r_x1": 539.92047, "r_y1": 642.65317, "r_x2": 539.92047, "r_y2": 634.27855, "r_x3": 317.95499, "r_y3": 634.27855, "coord_origin": "TOPLEFT" }, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 542.15802, "r_y0": 642.66214, "r_x1": 559.09888, "r_y1": 642.66214, "r_x2": 559.09888, "r_y2": 634.32338, "r_x3": 542.15802, "r_y3": 634.32338, "coord_origin": "TOPLEFT" }, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 653.62114, "r_x1": 331.86273, "r_y1": 653.62114, "r_x2": 331.86273, "r_y2": 645.28238, "r_x3": 317.95499, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.86301, "r_y0": 653.61217, "r_x1": 333.83957, "r_y1": 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1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 436.19501, "r_y0": 653.61217, "r_x1": 438.17157, "r_y1": 653.61217, "r_x2": 438.17157, "r_y2": 645.23755, "r_x3": 436.19501, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 440.396, "r_y0": 653.62114, "r_x1": 480.60988999999995, "r_y1": 653.62114, "r_x2": 480.60988999999995, "r_y2": 645.28238, "r_x3": 440.396, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 480.60999, "r_y0": 653.61217, "r_x1": 482.58655000000005, "r_y1": 653.61217, "r_x2": 482.58655000000005, "r_y2": 645.23755, "r_x3": 480.60999, "r_y3": 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"orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 395.68399, "r_y0": 664.58013, "r_x1": 410.23538, "r_y1": 664.58013, "r_x2": 410.23538, "r_y2": 656.2413799999999, "r_x3": 395.68399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 410.23099, "r_y0": 664.5711699999999, "r_x1": 427.5679, "r_y1": 664.5711699999999, "r_x2": 427.5679, "r_y2": 656.19655, "r_x3": 410.23099, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 429.74399, "r_y0": 664.58013, "r_x1": 445.50800000000004, "r_y1": 664.58013, "r_x2": 445.50800000000004, "r_y2": 656.2413799999999, "r_x3": 429.74399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 445.50800000000004, "r_y0": 664.5711699999999, "r_x1": 558.20227, "r_y1": 664.5711699999999, "r_x2": 558.20227, "r_y2": 656.19655, "r_x3": 445.50800000000004, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ". Our reasoning for picking this", "orig": ". Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.53017, "r_x1": 472.22198, "r_y1": 675.53017, "r_x2": 472.22198, "r_y2": 667.15556, "r_x3": 317.95499, "r_y3": 667.15556, "coord_origin": "TOPLEFT" }, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 686.4891700000001, "r_x1": 558.19843, "r_y1": 686.4891700000001, "r_x2": 558.19843, "r_y2": 678.11456, "r_x3": 327.918, "r_y3": 678.11456, "coord_origin": "TOPLEFT" }, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.448174, "r_x1": 558.19867, "r_y1": 697.448174, "r_x2": 558.19867, "r_y2": 689.07355, "r_x3": 317.95499, "r_y3": 689.07355, "coord_origin": "TOPLEFT" }, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.4071730000001, "r_x1": 558.2041, "r_y1": 708.4071730000001, "r_x2": 558.2041, "r_y2": 700.032555, "r_x3": 317.95499, "r_y3": 700.032555, "coord_origin": "TOPLEFT" }, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "has_chars": false, "has_words": false, "has_lines": true, "image": null, "lines": [] }, "predictions": { "layout": { "clusters": [ { "id": 19, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.7682238221168518, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 14, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.9367801547050476, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 96.16900999999996, "r_x1": 59.427395, "r_y1": 96.16900999999996, "r_x2": 59.427395, "r_y2": 85.85986000000003, "r_x3": 53.79800000000001, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.379532, "r_y0": 96.16900999999996, "r_x1": 156.52899, "r_y1": 96.16900999999996, "r_x2": 156.52899, "r_y2": 85.85986000000003, "r_x3": 70.379532, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "text", "bbox": { "l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT" }, "confidence": 0.9802607893943787, "cells": [ { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 119.35815000000002, "r_x1": 303.01697, "r_y1": 119.35815000000002, "r_x2": 303.01697, "r_y2": 110.98352, "r_x3": 53.79800000000001, "r_y3": 110.98352, "coord_origin": "TOPLEFT" }, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 130.31719999999996, "r_x1": 295.55695, "r_y1": 130.31719999999996, "r_x2": 295.55695, "r_y2": 121.94256999999993, "r_x3": 53.528999, "r_y3": 121.94256999999993, "coord_origin": "TOPLEFT" }, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 141.27617999999995, "r_x1": 294.04642, "r_y1": 141.27617999999995, "r_x2": 294.04642, "r_y2": 132.90155000000004, "r_x3": 53.79800000000001, "r_y3": 132.90155000000004, "coord_origin": "TOPLEFT" }, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 152.23419, "r_x1": 294.04733, "r_y1": 152.23419, "r_x2": 294.04733, "r_y2": 143.85956, "r_x3": 53.79800000000001, "r_y3": 143.85956, "coord_origin": "TOPLEFT" }, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 163.19317999999998, "r_x1": 294.04349, "r_y1": 163.19317999999998, "r_x2": 294.04349, "r_y2": 154.81853999999998, "r_x3": 53.79800000000001, "r_y3": 154.81853999999998, "coord_origin": "TOPLEFT" }, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 174.15215999999998, "r_x1": 294.04718, "r_y1": 174.15215999999998, "r_x2": 294.04718, "r_y2": 165.77752999999996, "r_x3": 53.79800000000001, "r_y3": 165.77752999999996, "coord_origin": "TOPLEFT" }, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 185.11114999999995, "r_x1": 294.04919, "r_y1": 185.11114999999995, "r_x2": 294.04919, "r_y2": 176.73650999999995, "r_x3": 53.79800000000001, "r_y3": 176.73650999999995, "coord_origin": "TOPLEFT" }, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 196.07019000000003, "r_x1": 294.27573, "r_y1": 196.07019000000003, "r_x2": 294.27573, "r_y2": 187.69556, "r_x3": 53.79800000000001, "r_y3": 187.69556, "coord_origin": "TOPLEFT" }, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 207.02917000000002, "r_x1": 295.42569, "r_y1": 207.02917000000002, "r_x2": 295.42569, "r_y2": 198.65454, "r_x3": 53.79800000000001, "r_y3": 198.65454, "coord_origin": "TOPLEFT" }, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 217.98816, "r_x1": 294.37256, "r_y1": 217.98816, "r_x2": 294.37256, "r_y2": 209.61352999999997, "r_x3": 53.528999, "r_y3": 209.61352999999997, "coord_origin": "TOPLEFT" }, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 228.94714, "r_x1": 275.48334, "r_y1": 228.94714, "r_x2": 275.48334, "r_y2": 220.57250999999997, "r_x3": 53.79800000000001, "r_y3": 220.57250999999997, "coord_origin": "TOPLEFT" }, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 7, "label": "text", "bbox": { "l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT" }, "confidence": 0.9793015718460083, "cells": [ { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 239.90619000000004, "r_x1": 295.564, "r_y1": 239.90619000000004, "r_x2": 295.564, "r_y2": 231.53156, "r_x3": 63.76100199999999, "r_y3": 231.53156, "coord_origin": "TOPLEFT" }, "text": "A key problem in the process of document conversion is to under-", "orig": "A key problem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 250.86517000000003, "r_x1": 294.04868, "r_y1": 250.86517000000003, "r_x2": 294.04868, "r_y2": 242.49054, "r_x3": 53.79800000000001, "r_y3": 242.49054, "coord_origin": "TOPLEFT" }, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 261.82318, "r_x1": 294.04532, "r_y1": 261.82318, "r_x2": 294.04532, "r_y2": 253.44854999999995, "r_x3": 53.79800000000001, "r_y3": 253.44854999999995, "coord_origin": "TOPLEFT" }, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 272.78216999999995, "r_x1": 295.55618, "r_y1": 272.78216999999995, "r_x2": 295.55618, "r_y2": 264.40752999999995, "r_x3": 53.79800000000001, "r_y3": 264.40752999999995, "coord_origin": "TOPLEFT" }, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 283.74118, "r_x1": 294.04059, "r_y1": 283.74118, "r_x2": 294.04059, "r_y2": 275.36658, "r_x3": 53.79800000000001, "r_y3": 275.36658, "coord_origin": "TOPLEFT" }, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 294.70016, "r_x1": 295.55783, "r_y1": 294.70016, "r_x2": 295.55783, "r_y2": 286.32552999999996, "r_x3": 53.79800000000001, "r_y3": 286.32552999999996, "coord_origin": "TOPLEFT" }, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 305.65918000000005, "r_x1": 294.04538, "r_y1": 305.65918000000005, "r_x2": 294.04538, "r_y2": 297.28455, "r_x3": 53.79800000000001, "r_y3": 297.28455, "coord_origin": "TOPLEFT" }, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 316.61816, "r_x1": 294.043, "r_y1": 316.61816, "r_x2": 294.043, "r_y2": 308.24353, "r_x3": 53.79800000000001, "r_y3": 308.24353, "coord_origin": "TOPLEFT" }, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 327.57718, "r_x1": 295.56412, "r_y1": 327.57718, "r_x2": 295.56412, "r_y2": 319.20255, "r_x3": 53.79800000000001, "r_y3": 319.20255, "coord_origin": "TOPLEFT" }, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 338.53616, "r_x1": 294.04532, "r_y1": 338.53616, "r_x2": 294.04532, "r_y2": 330.16153, "r_x3": 53.79800000000001, "r_y3": 330.16153, "coord_origin": "TOPLEFT" }, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 349.49518, "r_x1": 294.04538, "r_y1": 349.49518, "r_x2": 294.04538, "r_y2": 341.12054, "r_x3": 53.79800000000001, "r_y3": 341.12054, "coord_origin": "TOPLEFT" }, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 360.45416000000006, "r_x1": 294.04712, "r_y1": 360.45416000000006, "r_x2": 294.04712, "r_y2": 352.07953, "r_x3": 53.79800000000001, "r_y3": 352.07953, "coord_origin": "TOPLEFT" }, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 371.41318, "r_x1": 294.04538, "r_y1": 371.41318, "r_x2": 294.04538, "r_y2": 363.03853999999995, "r_x3": 53.79800000000001, "r_y3": 363.03853999999995, "coord_origin": "TOPLEFT" }, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 382.37119, "r_x1": 295.55643, "r_y1": 382.37119, "r_x2": 295.55643, "r_y2": 373.99655, "r_x3": 53.79800000000001, "r_y3": 373.99655, "coord_origin": "TOPLEFT" }, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 393.33017, "r_x1": 246.75909, "r_y1": 393.33017, "r_x2": 246.75909, "r_y2": 384.95554, "r_x3": 53.79800000000001, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "itories (PubMed and arXiv), which provide XML or L", "orig": "itories (PubMed and arXiv), which provide XML or L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 243.53101000000004, "r_y0": 391.82455, "r_x1": 248.58553000000003, "r_y1": 391.82455, "r_x2": 248.58553000000003, "r_y2": 385.03183000000007, "r_x3": 243.53101000000004, "r_y3": 385.03183000000007, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 247.24099999999999, "r_y0": 393.33017, "r_x1": 252.58859, "r_y1": 393.33017, "r_x2": 252.58859, "r_y2": 384.95554, "r_x3": 247.24099999999999, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 251.09398999999996, "r_y0": 395.25418, "r_x1": 256.08829, "r_y1": 395.25418, "r_x2": 256.08829, "r_y2": 386.87954999999994, "r_x3": 251.09398999999996, "r_y3": 386.87954999999994, "coord_origin": "TOPLEFT" }, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 254.967, "r_y0": 393.33017, "r_x1": 261.33725, "r_y1": 393.33017, "r_x2": 261.33725, "r_y2": 384.95554, "r_x3": 254.967, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "X", "orig": "X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 263.75021, "r_y0": 393.33017, "r_x1": 295.42773, "r_y1": 393.33017, "r_x2": 295.42773, "r_y2": 384.95554, "r_x3": 263.75021, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 404.28918, "r_x1": 294.21713, "r_y1": 404.28918, "r_x2": 294.21713, "r_y2": 395.91455, "r_x3": 53.528999, "r_y3": 395.91455, "coord_origin": "TOPLEFT" }, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 415.24817, "r_x1": 294.27386, "r_y1": 415.24817, "r_x2": 294.27386, "r_y2": 406.87354, "r_x3": 53.79800000000001, "r_y3": 406.87354, "coord_origin": "TOPLEFT" }, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 426.20717999999994, "r_x1": 295.03488, "r_y1": 426.20717999999994, "r_x2": 295.03488, "r_y2": 417.8325500000001, "r_x3": 53.79800000000001, "r_y3": 417.8325500000001, "coord_origin": "TOPLEFT" }, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 437.16617, "r_x1": 294.04691, "r_y1": 437.16617, "r_x2": 294.04691, "r_y2": 428.79153, "r_x3": 53.79800000000001, "r_y3": 428.79153, "coord_origin": "TOPLEFT" }, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 448.12518, "r_x1": 294.04865, "r_y1": 448.12518, "r_x2": 294.04865, "r_y2": 439.75055, "r_x3": 53.57400100000001, "r_y3": 439.75055, "coord_origin": "TOPLEFT" }, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 459.08417, "r_x1": 294.21643, "r_y1": 459.08417, "r_x2": 294.21643, "r_y2": 450.7095299999999, "r_x3": 53.79800000000001, "r_y3": 450.7095299999999, "coord_origin": "TOPLEFT" }, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 470.04318, "r_x1": 295.42181, "r_y1": 470.04318, "r_x2": 295.42181, "r_y2": 461.66855, "r_x3": 53.79800000000001, "r_y3": 461.66855, "coord_origin": "TOPLEFT" }, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 481.00217, "r_x1": 125.52795, "r_y1": 481.00217, "r_x2": 125.52795, "r_y2": 472.62753, "r_x3": 53.79800000000001, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 128.608, "r_y0": 481.01114, "r_x1": 153.7679, "r_y1": 481.01114, "r_x2": 153.7679, "r_y2": 472.67239, "r_x3": 128.608, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.15199, "r_y0": 481.00217, "r_x1": 165.16365, "r_y1": 481.00217, "r_x2": 165.16365, "r_y2": 472.62753, "r_x3": 157.15199, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.248, "r_y0": 481.01114, "r_x1": 201.49272, "r_y1": 481.01114, "r_x2": 201.49272, "r_y2": 472.67239, "r_x3": 168.248, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.78799, "r_y0": 481.00217, "r_x1": 294.21494, "r_y1": 481.00217, "r_x2": 294.21494, "r_y2": 472.62753, "r_x3": 204.78799, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 491.96017, "r_x1": 294.04715, "r_y1": 491.96017, "r_x2": 294.04715, "r_y2": 483.58554, "r_x3": 53.79800000000001, "r_y3": 483.58554, "coord_origin": "TOPLEFT" }, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 502.91916, "r_x1": 89.080788, "r_y1": 502.91916, "r_x2": 89.080788, "r_y2": 494.54453, "r_x3": 53.79800000000001, "r_y3": 494.54453, "coord_origin": "TOPLEFT" }, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 6, "label": "text", "bbox": { "l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT" }, "confidence": 0.9795253276824951, "cells": [ { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 513.87817, "r_x1": 295.56396, "r_y1": 513.87817, "r_x2": 295.56396, "r_y2": 505.50354, "r_x3": 63.76100199999999, "r_y3": 505.50354, "coord_origin": "TOPLEFT" }, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 524.83716, "r_x1": 294.21271, "r_y1": 524.83716, "r_x2": 294.21271, "r_y2": 516.46252, "r_x3": 53.79800000000001, "r_y3": 516.46252, "coord_origin": "TOPLEFT" }, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.591999, "r_y0": 535.7961700000001, "r_x1": 61.92275599999999, "r_y1": 535.7961700000001, "r_x2": 61.92275599999999, "r_y2": 527.42154, "r_x3": 53.591999, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.162201, "r_y0": 535.7961700000001, "r_x1": 294.04626, "r_y1": 535.7961700000001, "r_x2": 294.04626, "r_y2": 527.42154, "r_x3": 64.162201, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "distinct class labels on 80863 unique document pages, of which", "orig": "distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 546.75517, "r_x1": 294.21228, "r_y1": 546.75517, "r_x2": 294.21228, "r_y2": 538.38055, "r_x3": 53.79800000000001, "r_y3": 538.38055, "coord_origin": "TOPLEFT" }, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 557.71417, "r_x1": 294.04709, "r_y1": 557.71417, "r_x2": 294.04709, "r_y2": 549.33955, "r_x3": 53.79800000000001, "r_y3": 549.33955, "coord_origin": "TOPLEFT" }, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 568.67317, "r_x1": 134.28951, "r_y1": 568.67317, "r_x2": 134.28951, "r_y2": 560.29855, "r_x3": 53.79800000000001, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.29201, "r_y0": 565.0235299999999, "r_x1": 137.67381, "r_y1": 565.0235299999999, "r_x2": 137.67381, "r_y2": 558.23083, "r_x3": 134.29201, "r_y3": 558.23083, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 140.418, "r_y0": 568.67317, "r_x1": 294.047, "r_y1": 568.67317, "r_x2": 294.047, "r_y2": 560.29855, "r_x3": 140.418, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 579.63217, "r_x1": 295.10538, "r_y1": 579.63217, "r_x2": 295.10538, "r_y2": 571.25755, "r_x3": 53.79800000000001, "r_y3": 571.25755, "coord_origin": "TOPLEFT" }, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT" }, "confidence": 0.9687220454216003, "cells": [ { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 592.95618, "r_x1": 74.221352, "r_y1": 592.95618, "r_x2": 74.221352, "r_y2": 584.58156, "r_x3": 64.708, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 592.96515, "r_x1": 146.39589, "r_y1": 592.96515, "r_x2": 146.39589, "r_y2": 584.62639, "r_x3": 78.207001, "r_y3": 584.62639, "coord_origin": "TOPLEFT" }, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 146.41701, "r_y0": 592.95618, "r_x1": 295.03036, "r_y1": 592.95618, "r_x2": 295.03036, "r_y2": 584.58156, "r_x3": 146.41701, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": ": In contrast to PubLayNet and DocBank,", "orig": ": In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.875, "r_y0": 603.91518, "r_x1": 295.56165, "r_y1": 603.91518, "r_x2": 295.56165, "r_y2": 595.54056, "r_x3": 77.875, "r_y3": 595.54056, "coord_origin": "TOPLEFT" }, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 614.87418, "r_x1": 200.6432, "r_y1": 614.87418, "r_x2": 200.6432, "r_y2": 606.49956, "r_x3": 78.207001, "r_y3": 606.49956, "coord_origin": "TOPLEFT" }, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT" }, "confidence": 0.9594229459762573, "cells": [ { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 625.8331800000001, "r_x1": 74.221352, "r_y1": 625.8331800000001, "r_x2": 74.221352, "r_y2": 617.45856, "r_x3": 64.708, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 625.84215, "r_x1": 167.91745, "r_y1": 625.84215, "r_x2": 167.91745, "r_y2": 617.50339, "r_x3": 78.207001, "r_y3": 617.50339, "coord_origin": "TOPLEFT" }, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.33501, "r_y0": 625.8331800000001, "r_x1": 294.26254, "r_y1": 625.8331800000001, "r_x2": 294.26254, "r_y2": 617.45856, "r_x3": 168.33501, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": ": We include diverse and complex", "orig": ": We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 636.79117, "r_x1": 245.45726000000002, "r_y1": 636.79117, "r_x2": 245.45726000000002, "r_y2": 628.41655, "r_x3": 78.207001, "r_y3": 628.41655, "coord_origin": "TOPLEFT" }, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT" }, "confidence": 0.9615103006362915, "cells": [ { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 647.75017, "r_x1": 74.221352, "r_y1": 647.75017, "r_x2": 74.221352, "r_y2": 639.37555, "r_x3": 64.708, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 647.75914, "r_x1": 143.51663, "r_y1": 647.75914, "r_x2": 143.51663, "r_y2": 639.42038, "r_x3": 78.207001, "r_y3": 639.42038, "coord_origin": "TOPLEFT" }, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.02, "r_y0": 647.75017, "r_x1": 294.04648, "r_y1": 647.75017, "r_x2": 294.04648, "r_y2": 639.37555, "r_x3": 144.02, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": ": We define 11 class labels to distinguish", "orig": ": We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 658.70917, "r_x1": 294.68381, "r_y1": 658.70917, "r_x2": 294.68381, "r_y2": 650.33455, "r_x3": 78.207001, "r_y3": 650.33455, "coord_origin": "TOPLEFT" }, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 669.66817, "r_x1": 276.33752, "r_y1": 669.66817, "r_x2": 276.33752, "r_y2": 661.29355, "r_x3": 78.207001, "r_y3": 661.29355, "coord_origin": "TOPLEFT" }, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 13, "label": "list_item", "bbox": { "l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT" }, "confidence": 0.9447500109672546, "cells": [ { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 680.62717, "r_x1": 74.221352, "r_y1": 680.62717, "r_x2": 74.221352, "r_y2": 672.25256, "r_x3": 64.708, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 680.63614, "r_x1": 163.78357, "r_y1": 680.63614, "r_x2": 163.78357, "r_y2": 672.29739, "r_x3": 78.207001, "r_y3": 672.29739, "coord_origin": "TOPLEFT" }, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 163.994, "r_y0": 680.62717, "r_x1": 295.56439, "r_y1": 680.62717, "r_x2": 295.56439, "r_y2": 672.25256, "r_x3": 163.994, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": ": A fraction of the pages in the Do-", "orig": ": A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { 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"$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 18, "label": "text", "bbox": { "l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT" }, "confidence": 0.7997146844863892, "cells": [ { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.095, "r_y0": 95.73816, "r_x1": 558.43201, "r_y1": 95.73816, "r_x2": 558.43201, "r_y2": 87.36352999999997, "r_x3": 342.095, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 106.69713999999999, "r_x1": 445.83629999999994, "r_y1": 106.69713999999999, "r_x2": 445.83629999999994, "r_y2": 98.32250999999997, "r_x3": 342.36401, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 12, "label": "list_item", "bbox": { "l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9563499093055725, "cells": [ { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.86502, "r_y0": 117.65612999999996, "r_x1": 338.37836, "r_y1": 117.65612999999996, "r_x2": 338.37836, "r_y2": 109.28148999999996, "r_x3": 328.86502, "r_y3": 109.28148999999996, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 117.66516000000001, "r_x1": 487.25296, "r_y1": 117.66516000000001, "r_x2": 487.25296, "r_y2": 109.32641999999998, "r_x3": 342.36401, "r_y3": 109.32641999999998, "coord_origin": "TOPLEFT" }, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.75900000000007, "r_y0": 117.65618999999992, "r_x1": 558.20294, "r_y1": 117.65618999999992, "r_x2": 558.20294, "r_y2": 109.28156000000001, "r_x3": 487.75900000000007, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": ": Like DocBank, we", "orig": ": Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 128.61517000000003, "r_x1": 559.72101, "r_y1": 128.61517000000003, "r_x2": 559.72101, "r_y2": 120.24054000000001, "r_x3": 342.36401, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 139.57317999999998, "r_x1": 558.20117, "r_y1": 139.57317999999998, "r_x2": 558.20117, "r_y2": 131.19854999999995, "r_x3": 342.36401, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 150.53216999999995, "r_x1": 558.20087, "r_y1": 150.53216999999995, "r_x2": 558.20087, "r_y2": 142.15752999999995, "r_x3": 342.36401, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 161.49114999999995, "r_x1": 438.0624399999999, "r_y1": 161.49114999999995, "r_x2": 438.0624399999999, "r_y2": 153.11652000000004, "r_x3": 342.36401, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 4, "label": "text", "bbox": { "l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9813903570175171, "cells": [ { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 176.35015999999996, "r_x1": 559.19031, "r_y1": 176.35015999999996, "r_x2": 559.19031, "r_y2": 167.97551999999996, "r_x3": 327.918, "r_y3": 167.97551999999996, "coord_origin": "TOPLEFT" }, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 187.30913999999996, "r_x1": 558.20422, "r_y1": 187.30913999999996, "r_x2": 558.20422, "r_y2": 178.93451000000005, "r_x3": 317.62299, "r_y3": 178.93451000000005, "coord_origin": "TOPLEFT" }, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 198.26819, "r_x1": 558.19763, "r_y1": 198.26819, "r_x2": 558.19763, "r_y2": 189.89355, "r_x3": 317.95499, "r_y3": 189.89355, "coord_origin": "TOPLEFT" }, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 209.22717, "r_x1": 558.20612, "r_y1": 209.22717, "r_x2": 558.20612, "r_y2": 200.85253999999998, "r_x3": 317.95499, "r_y3": 200.85253999999998, "coord_origin": "TOPLEFT" }, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 220.18615999999997, "r_x1": 434.94861, "r_y1": 220.18615999999997, "r_x2": 434.94861, "r_y2": 211.81151999999997, "r_x3": 317.95499, "r_y3": 211.81151999999997, "coord_origin": "TOPLEFT" }, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT" }, "confidence": 0.9844217300415039, "cells": [ { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 231.14520000000005, "r_x1": 558.19836, "r_y1": 231.14520000000005, "r_x2": 558.19836, "r_y2": 222.77057000000002, "r_x3": 327.918, "r_y3": 222.77057000000002, "coord_origin": "TOPLEFT" }, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.73099, "r_y0": 242.10419000000002, "r_x1": 558.1991, "r_y1": 242.10419000000002, "r_x2": 558.1991, "r_y2": 233.72955000000002, "r_x3": 317.73099, "r_y3": 233.72955000000002, "coord_origin": "TOPLEFT" }, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 253.06317, "r_x1": 558.20416, "r_y1": 253.06317, "r_x2": 558.20416, "r_y2": 244.68854, "r_x3": 317.95499, "r_y3": 244.68854, "coord_origin": "TOPLEFT" }, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 264.02216, "r_x1": 558.20563, "r_y1": 264.02216, "r_x2": 558.20563, "r_y2": 255.64752, "r_x3": 317.95499, "r_y3": 255.64752, "coord_origin": "TOPLEFT" }, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 274.98015999999996, "r_x1": 558.19861, "r_y1": 274.98015999999996, "r_x2": 558.19861, "r_y2": 266.60553000000004, "r_x3": 317.95499, "r_y3": 266.60553000000004, "coord_origin": "TOPLEFT" }, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 285.93918, "r_x1": 558.20416, "r_y1": 285.93918, "r_x2": 558.20416, "r_y2": 277.56458, "r_x3": 317.95499, "r_y3": 277.56458, "coord_origin": "TOPLEFT" }, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 296.89816, "r_x1": 558.20239, "r_y1": 296.89816, "r_x2": 558.20239, "r_y2": 288.52353, "r_x3": 317.95499, "r_y3": 288.52353, "coord_origin": "TOPLEFT" }, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 307.85718, "r_x1": 559.58197, "r_y1": 307.85718, "r_x2": 559.58197, "r_y2": 299.48254, "r_x3": 317.95499, "r_y3": 299.48254, "coord_origin": "TOPLEFT" }, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 15, "label": "section_header", "bbox": { "l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT" }, "confidence": 0.9347459077835083, "cells": [ { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.51797, "r_x1": 323.10388, "r_y1": 331.51797, "r_x2": 323.10388, "r_y2": 321.20889, "r_x3": 317.95499, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.12115, "r_y0": 331.51797, "r_x1": 421.74411, "r_y1": 331.51797, "r_x2": 421.74411, "r_y2": 321.20889, "r_x3": 333.12115, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT" }, "confidence": 0.9860377907752991, "cells": [ { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.52499, "r_y0": 354.70717999999994, "r_x1": 559.71301, "r_y1": 354.70717999999994, "r_x2": 559.71301, "r_y2": 346.3325500000001, "r_x3": 317.52499, "r_y3": 346.3325500000001, "coord_origin": "TOPLEFT" }, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 365.66617, "r_x1": 558.20276, "r_y1": 365.66617, "r_x2": 558.20276, "r_y2": 357.29153, "r_x3": 317.95499, "r_y3": 357.29153, "coord_origin": "TOPLEFT" }, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 376.62518, "r_x1": 559.71564, "r_y1": 376.62518, "r_x2": 559.71564, "r_y2": 368.25055, "r_x3": 317.62299, "r_y3": 368.25055, "coord_origin": "TOPLEFT" }, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 387.58417, "r_x1": 558.43365, "r_y1": 387.58417, "r_x2": 558.43365, "r_y2": 379.2095299999999, "r_x3": 317.95499, "r_y3": 379.2095299999999, "coord_origin": "TOPLEFT" }, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 398.54318, "r_x1": 559.18658, "r_y1": 398.54318, "r_x2": 559.18658, "r_y2": 390.16855000000004, "r_x3": 317.95499, "r_y3": 390.16855000000004, "coord_origin": "TOPLEFT" }, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 409.50217, "r_x1": 558.20502, "r_y1": 409.50217, "r_x2": 558.20502, "r_y2": 401.12753, "r_x3": 317.95499, "r_y3": 401.12753, "coord_origin": "TOPLEFT" }, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 420.46118, "r_x1": 558.20422, "r_y1": 420.46118, "r_x2": 558.20422, "r_y2": 412.08655, "r_x3": 317.95499, "r_y3": 412.08655, "coord_origin": "TOPLEFT" }, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 431.4201699999999, "r_x1": 558.20477, "r_y1": 431.4201699999999, "r_x2": 558.20477, "r_y2": 423.04553, "r_x3": 317.95499, "r_y3": 423.04553, "coord_origin": "TOPLEFT" }, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 442.37817, "r_x1": 558.19952, "r_y1": 442.37817, "r_x2": 558.19952, "r_y2": 434.00354, "r_x3": 317.95499, "r_y3": 434.00354, "coord_origin": "TOPLEFT" }, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 453.33716, "r_x1": 559.71613, "r_y1": 453.33716, "r_x2": 559.71613, "r_y2": 444.96252, "r_x3": 317.95499, "r_y3": 444.96252, "coord_origin": "TOPLEFT" }, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 464.29617, "r_x1": 350.90652, "r_y1": 464.29617, "r_x2": 350.90652, "r_y2": 455.92154, "r_x3": 317.95499, "r_y3": 455.92154, "coord_origin": "TOPLEFT" }, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT" }, "confidence": 0.9861046671867371, "cells": [ { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 475.25516, "r_x1": 558.19824, "r_y1": 475.25516, "r_x2": 558.19824, "r_y2": 466.88052, "r_x3": 327.918, "r_y3": 466.88052, "coord_origin": "TOPLEFT" }, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 486.21417, "r_x1": 558.20551, "r_y1": 486.21417, "r_x2": 558.20551, "r_y2": 477.83954, "r_x3": 317.95499, "r_y3": 477.83954, "coord_origin": "TOPLEFT" }, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 497.17319, "r_x1": 558.20575, "r_y1": 497.17319, "r_x2": 558.20575, "r_y2": 488.79855, "r_x3": 317.95499, "r_y3": 488.79855, "coord_origin": "TOPLEFT" }, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 508.13217, "r_x1": 559.18646, "r_y1": 508.13217, "r_x2": 559.18646, "r_y2": 499.75754, "r_x3": 317.95499, "r_y3": 499.75754, "coord_origin": "TOPLEFT" }, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 519.09119, "r_x1": 558.2002, "r_y1": 519.09119, "r_x2": 558.2002, "r_y2": 510.71655, "r_x3": 317.95499, "r_y3": 510.71655, "coord_origin": "TOPLEFT" }, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 530.05017, "r_x1": 558.20618, "r_y1": 530.05017, "r_x2": 558.20618, "r_y2": 521.67554, "r_x3": 317.95499, "r_y3": 521.67554, "coord_origin": "TOPLEFT" }, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 541.00917, "r_x1": 558.20239, "r_y1": 541.00917, "r_x2": 558.20239, "r_y2": 532.63455, "r_x3": 317.95499, "r_y3": 532.63455, "coord_origin": "TOPLEFT" }, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.96817, "r_x1": 503.32648, "r_y1": 551.96817, "r_x2": 503.32648, "r_y2": 543.59355, "r_x3": 317.95499, "r_y3": 543.59355, "coord_origin": "TOPLEFT" }, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 16, "label": "section_header", "bbox": { "l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT" }, "confidence": 0.9331073760986328, "cells": [ { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.629, "r_x1": 322.97391, "r_y1": 575.629, "r_x2": 322.97391, "r_y2": 565.3199, "r_x3": 317.95499, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 332.73831, "r_y0": 575.629, "r_x1": 477.45688, "r_y1": 575.629, "r_x2": 477.45688, "r_y2": 565.3199, "r_x3": 332.73831, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT" }, "confidence": 0.9852981567382812, "cells": [ { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 598.81818, "r_x1": 558.20233, "r_y1": 598.81818, "r_x2": 558.20233, "r_y2": 590.4435599999999, "r_x3": 317.95499, "r_y3": 590.4435599999999, "coord_origin": "TOPLEFT" }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 609.77718, "r_x1": 558.20239, "r_y1": 609.77718, "r_x2": 558.20239, "r_y2": 601.40256, "r_x3": 317.95499, "r_y3": 601.40256, "coord_origin": "TOPLEFT" }, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 620.73517, "r_x1": 559.7132, "r_y1": 620.73517, "r_x2": 559.7132, "r_y2": 612.36055, "r_x3": 317.95499, "r_y3": 612.36055, "coord_origin": "TOPLEFT" }, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 631.69417, "r_x1": 559.71313, "r_y1": 631.69417, "r_x2": 559.71313, "r_y2": 623.3195499999999, "r_x3": 317.95499, "r_y3": 623.3195499999999, "coord_origin": "TOPLEFT" }, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.65317, "r_x1": 539.92047, "r_y1": 642.65317, "r_x2": 539.92047, "r_y2": 634.27855, "r_x3": 317.95499, "r_y3": 634.27855, "coord_origin": "TOPLEFT" }, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 542.15802, "r_y0": 642.66214, "r_x1": 559.09888, "r_y1": 642.66214, "r_x2": 559.09888, "r_y2": 634.32338, "r_x3": 542.15802, "r_y3": 634.32338, "coord_origin": "TOPLEFT" }, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 653.62114, "r_x1": 331.86273, "r_y1": 653.62114, "r_x2": 331.86273, "r_y2": 645.28238, "r_x3": 317.95499, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.86301, "r_y0": 653.61217, "r_x1": 333.83957, "r_y1": 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1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 436.19501, "r_y0": 653.61217, "r_x1": 438.17157, "r_y1": 653.61217, "r_x2": 438.17157, "r_y2": 645.23755, "r_x3": 436.19501, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 440.396, "r_y0": 653.62114, "r_x1": 480.60988999999995, "r_y1": 653.62114, "r_x2": 480.60988999999995, "r_y2": 645.28238, "r_x3": 440.396, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 480.60999, "r_y0": 653.61217, "r_x1": 482.58655000000005, "r_y1": 653.61217, "r_x2": 482.58655000000005, "r_y2": 645.23755, "r_x3": 480.60999, "r_y3": 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"r_x2": 445.50800000000004, "r_y2": 656.2413799999999, "r_x3": 429.74399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 445.50800000000004, "r_y0": 664.5711699999999, "r_x1": 558.20227, "r_y1": 664.5711699999999, "r_x2": 558.20227, "r_y2": 656.19655, "r_x3": 445.50800000000004, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ". Our reasoning for picking this", "orig": ". Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.53017, "r_x1": 472.22198, "r_y1": 675.53017, "r_x2": 472.22198, "r_y2": 667.15556, "r_x3": 317.95499, "r_y3": 667.15556, "coord_origin": "TOPLEFT" }, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT" }, "confidence": 0.9749757051467896, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 686.4891700000001, "r_x1": 558.19843, "r_y1": 686.4891700000001, "r_x2": 558.19843, "r_y2": 678.11456, "r_x3": 327.918, "r_y3": 678.11456, "coord_origin": "TOPLEFT" }, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.448174, "r_x1": 558.19867, "r_y1": 697.448174, "r_x2": 558.19867, "r_y2": 689.07355, "r_x3": 317.95499, "r_y3": 689.07355, "coord_origin": "TOPLEFT" }, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.4071730000001, "r_x1": 558.2041, "r_y1": 708.4071730000001, "r_x2": 558.2041, "r_y2": 700.032555, "r_x3": 317.95499, "r_y3": 700.032555, "coord_origin": "TOPLEFT" }, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] } ] }, "tablestructure": { "table_map": {} }, "figures_classification": null, "equations_prediction": null, "vlm_response": null }, "assembled": { "elements": [ { "label": "page_header", "id": 19, "page_no": 1, "cluster": { "id": 19, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.7682238221168518, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" }, { "label": "section_header", "id": 14, "page_no": 1, "cluster": { "id": 14, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.9367801547050476, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 96.16900999999996, "r_x1": 59.427395, "r_y1": 96.16900999999996, "r_x2": 59.427395, "r_y2": 85.85986000000003, "r_x3": 53.79800000000001, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.379532, "r_y0": 96.16900999999996, "r_x1": 156.52899, "r_y1": 96.16900999999996, "r_x2": 156.52899, "r_y2": 85.85986000000003, "r_x3": 70.379532, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "1 INTRODUCTION" }, { "label": "text", "id": 5, "page_no": 1, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT" }, "confidence": 0.9802607893943787, "cells": [ { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 119.35815000000002, "r_x1": 303.01697, "r_y1": 119.35815000000002, "r_x2": 303.01697, "r_y2": 110.98352, "r_x3": 53.79800000000001, "r_y3": 110.98352, "coord_origin": "TOPLEFT" }, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 130.31719999999996, "r_x1": 295.55695, "r_y1": 130.31719999999996, "r_x2": 295.55695, "r_y2": 121.94256999999993, "r_x3": 53.528999, "r_y3": 121.94256999999993, "coord_origin": "TOPLEFT" }, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 141.27617999999995, "r_x1": 294.04642, "r_y1": 141.27617999999995, "r_x2": 294.04642, "r_y2": 132.90155000000004, "r_x3": 53.79800000000001, "r_y3": 132.90155000000004, "coord_origin": "TOPLEFT" }, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 152.23419, "r_x1": 294.04733, "r_y1": 152.23419, "r_x2": 294.04733, "r_y2": 143.85956, "r_x3": 53.79800000000001, "r_y3": 143.85956, "coord_origin": "TOPLEFT" }, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 163.19317999999998, "r_x1": 294.04349, "r_y1": 163.19317999999998, "r_x2": 294.04349, "r_y2": 154.81853999999998, "r_x3": 53.79800000000001, "r_y3": 154.81853999999998, "coord_origin": "TOPLEFT" }, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 174.15215999999998, "r_x1": 294.04718, "r_y1": 174.15215999999998, "r_x2": 294.04718, "r_y2": 165.77752999999996, "r_x3": 53.79800000000001, "r_y3": 165.77752999999996, "coord_origin": "TOPLEFT" }, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 185.11114999999995, "r_x1": 294.04919, "r_y1": 185.11114999999995, "r_x2": 294.04919, "r_y2": 176.73650999999995, "r_x3": 53.79800000000001, "r_y3": 176.73650999999995, "coord_origin": "TOPLEFT" }, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 196.07019000000003, "r_x1": 294.27573, "r_y1": 196.07019000000003, "r_x2": 294.27573, "r_y2": 187.69556, "r_x3": 53.79800000000001, "r_y3": 187.69556, "coord_origin": "TOPLEFT" }, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 207.02917000000002, "r_x1": 295.42569, "r_y1": 207.02917000000002, "r_x2": 295.42569, "r_y2": 198.65454, "r_x3": 53.79800000000001, "r_y3": 198.65454, "coord_origin": "TOPLEFT" }, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 217.98816, "r_x1": 294.37256, "r_y1": 217.98816, "r_x2": 294.37256, "r_y2": 209.61352999999997, "r_x3": 53.528999, "r_y3": 209.61352999999997, "coord_origin": "TOPLEFT" }, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 228.94714, "r_x1": 275.48334, "r_y1": 228.94714, "r_x2": 275.48334, "r_y2": 220.57250999999997, "r_x3": 53.79800000000001, "r_y3": 220.57250999999997, "coord_origin": "TOPLEFT" }, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1." }, { "label": "text", "id": 7, "page_no": 1, "cluster": { "id": 7, "label": "text", "bbox": { "l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT" }, "confidence": 0.9793015718460083, "cells": [ { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 239.90619000000004, "r_x1": 295.564, "r_y1": 239.90619000000004, "r_x2": 295.564, "r_y2": 231.53156, "r_x3": 63.76100199999999, "r_y3": 231.53156, "coord_origin": "TOPLEFT" }, "text": "A key problem in the process of document conversion is to under-", "orig": "A key problem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 250.86517000000003, "r_x1": 294.04868, "r_y1": 250.86517000000003, "r_x2": 294.04868, "r_y2": 242.49054, "r_x3": 53.79800000000001, "r_y3": 242.49054, "coord_origin": "TOPLEFT" }, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 261.82318, "r_x1": 294.04532, "r_y1": 261.82318, "r_x2": 294.04532, "r_y2": 253.44854999999995, "r_x3": 53.79800000000001, "r_y3": 253.44854999999995, "coord_origin": "TOPLEFT" }, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 272.78216999999995, "r_x1": 295.55618, "r_y1": 272.78216999999995, "r_x2": 295.55618, "r_y2": 264.40752999999995, "r_x3": 53.79800000000001, "r_y3": 264.40752999999995, "coord_origin": "TOPLEFT" }, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 283.74118, "r_x1": 294.04059, "r_y1": 283.74118, "r_x2": 294.04059, "r_y2": 275.36658, "r_x3": 53.79800000000001, "r_y3": 275.36658, "coord_origin": "TOPLEFT" }, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 294.70016, "r_x1": 295.55783, "r_y1": 294.70016, "r_x2": 295.55783, "r_y2": 286.32552999999996, "r_x3": 53.79800000000001, "r_y3": 286.32552999999996, "coord_origin": "TOPLEFT" }, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 305.65918000000005, "r_x1": 294.04538, "r_y1": 305.65918000000005, "r_x2": 294.04538, "r_y2": 297.28455, "r_x3": 53.79800000000001, "r_y3": 297.28455, "coord_origin": "TOPLEFT" }, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 316.61816, "r_x1": 294.043, "r_y1": 316.61816, "r_x2": 294.043, "r_y2": 308.24353, "r_x3": 53.79800000000001, "r_y3": 308.24353, "coord_origin": "TOPLEFT" }, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 327.57718, "r_x1": 295.56412, "r_y1": 327.57718, "r_x2": 295.56412, "r_y2": 319.20255, "r_x3": 53.79800000000001, "r_y3": 319.20255, "coord_origin": "TOPLEFT" }, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 338.53616, "r_x1": 294.04532, "r_y1": 338.53616, "r_x2": 294.04532, "r_y2": 330.16153, "r_x3": 53.79800000000001, "r_y3": 330.16153, "coord_origin": "TOPLEFT" }, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 349.49518, "r_x1": 294.04538, "r_y1": 349.49518, "r_x2": 294.04538, "r_y2": 341.12054, "r_x3": 53.79800000000001, "r_y3": 341.12054, "coord_origin": "TOPLEFT" }, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 360.45416000000006, "r_x1": 294.04712, "r_y1": 360.45416000000006, "r_x2": 294.04712, "r_y2": 352.07953, "r_x3": 53.79800000000001, "r_y3": 352.07953, "coord_origin": "TOPLEFT" }, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 371.41318, "r_x1": 294.04538, "r_y1": 371.41318, "r_x2": 294.04538, "r_y2": 363.03853999999995, "r_x3": 53.79800000000001, "r_y3": 363.03853999999995, "coord_origin": "TOPLEFT" }, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 382.37119, "r_x1": 295.55643, "r_y1": 382.37119, "r_x2": 295.55643, "r_y2": 373.99655, "r_x3": 53.79800000000001, "r_y3": 373.99655, "coord_origin": "TOPLEFT" }, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 393.33017, "r_x1": 246.75909, "r_y1": 393.33017, "r_x2": 246.75909, "r_y2": 384.95554, "r_x3": 53.79800000000001, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "itories (PubMed and arXiv), which provide XML or L", "orig": "itories (PubMed and arXiv), which provide XML or L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 243.53101000000004, "r_y0": 391.82455, "r_x1": 248.58553000000003, "r_y1": 391.82455, "r_x2": 248.58553000000003, "r_y2": 385.03183000000007, "r_x3": 243.53101000000004, "r_y3": 385.03183000000007, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 247.24099999999999, "r_y0": 393.33017, "r_x1": 252.58859, "r_y1": 393.33017, "r_x2": 252.58859, "r_y2": 384.95554, "r_x3": 247.24099999999999, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 251.09398999999996, "r_y0": 395.25418, "r_x1": 256.08829, "r_y1": 395.25418, "r_x2": 256.08829, "r_y2": 386.87954999999994, "r_x3": 251.09398999999996, "r_y3": 386.87954999999994, "coord_origin": "TOPLEFT" }, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 254.967, "r_y0": 393.33017, "r_x1": 261.33725, "r_y1": 393.33017, "r_x2": 261.33725, "r_y2": 384.95554, "r_x3": 254.967, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "X", "orig": "X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 263.75021, "r_y0": 393.33017, "r_x1": 295.42773, "r_y1": 393.33017, "r_x2": 295.42773, "r_y2": 384.95554, "r_x3": 263.75021, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 404.28918, "r_x1": 294.21713, "r_y1": 404.28918, "r_x2": 294.21713, "r_y2": 395.91455, "r_x3": 53.528999, "r_y3": 395.91455, "coord_origin": "TOPLEFT" }, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 415.24817, "r_x1": 294.27386, "r_y1": 415.24817, "r_x2": 294.27386, "r_y2": 406.87354, "r_x3": 53.79800000000001, "r_y3": 406.87354, "coord_origin": "TOPLEFT" }, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 426.20717999999994, "r_x1": 295.03488, "r_y1": 426.20717999999994, "r_x2": 295.03488, "r_y2": 417.8325500000001, "r_x3": 53.79800000000001, "r_y3": 417.8325500000001, "coord_origin": "TOPLEFT" }, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 437.16617, "r_x1": 294.04691, "r_y1": 437.16617, "r_x2": 294.04691, "r_y2": 428.79153, "r_x3": 53.79800000000001, "r_y3": 428.79153, "coord_origin": "TOPLEFT" }, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 448.12518, "r_x1": 294.04865, "r_y1": 448.12518, "r_x2": 294.04865, "r_y2": 439.75055, "r_x3": 53.57400100000001, "r_y3": 439.75055, "coord_origin": "TOPLEFT" }, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 459.08417, "r_x1": 294.21643, "r_y1": 459.08417, "r_x2": 294.21643, "r_y2": 450.7095299999999, "r_x3": 53.79800000000001, "r_y3": 450.7095299999999, "coord_origin": "TOPLEFT" }, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 470.04318, "r_x1": 295.42181, "r_y1": 470.04318, "r_x2": 295.42181, "r_y2": 461.66855, "r_x3": 53.79800000000001, "r_y3": 461.66855, "coord_origin": "TOPLEFT" }, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 481.00217, "r_x1": 125.52795, "r_y1": 481.00217, "r_x2": 125.52795, "r_y2": 472.62753, "r_x3": 53.79800000000001, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 128.608, "r_y0": 481.01114, "r_x1": 153.7679, "r_y1": 481.01114, "r_x2": 153.7679, "r_y2": 472.67239, "r_x3": 128.608, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.15199, "r_y0": 481.00217, "r_x1": 165.16365, "r_y1": 481.00217, "r_x2": 165.16365, "r_y2": 472.62753, "r_x3": 157.15199, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.248, "r_y0": 481.01114, "r_x1": 201.49272, "r_y1": 481.01114, "r_x2": 201.49272, "r_y2": 472.67239, "r_x3": 168.248, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.78799, "r_y0": 481.00217, "r_x1": 294.21494, "r_y1": 481.00217, "r_x2": 294.21494, "r_y2": 472.62753, "r_x3": 204.78799, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 491.96017, "r_x1": 294.04715, "r_y1": 491.96017, "r_x2": 294.04715, "r_y2": 483.58554, "r_x3": 53.79800000000001, "r_y3": 483.58554, "coord_origin": "TOPLEFT" }, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 502.91916, "r_x1": 89.080788, "r_y1": 502.91916, "r_x2": 89.080788, "r_y2": 494.54453, "r_x3": 53.79800000000001, "r_y3": 494.54453, "coord_origin": "TOPLEFT" }, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5." }, { "label": "text", "id": 6, "page_no": 1, "cluster": { "id": 6, "label": "text", "bbox": { "l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT" }, "confidence": 0.9795253276824951, "cells": [ { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 513.87817, "r_x1": 295.56396, "r_y1": 513.87817, "r_x2": 295.56396, "r_y2": 505.50354, "r_x3": 63.76100199999999, "r_y3": 505.50354, "coord_origin": "TOPLEFT" }, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 524.83716, "r_x1": 294.21271, "r_y1": 524.83716, "r_x2": 294.21271, "r_y2": 516.46252, "r_x3": 53.79800000000001, "r_y3": 516.46252, "coord_origin": "TOPLEFT" }, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.591999, "r_y0": 535.7961700000001, "r_x1": 61.92275599999999, "r_y1": 535.7961700000001, "r_x2": 61.92275599999999, "r_y2": 527.42154, "r_x3": 53.591999, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.162201, "r_y0": 535.7961700000001, "r_x1": 294.04626, "r_y1": 535.7961700000001, "r_x2": 294.04626, "r_y2": 527.42154, "r_x3": 64.162201, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "distinct class labels on 80863 unique document pages, of which", "orig": "distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 546.75517, "r_x1": 294.21228, "r_y1": 546.75517, "r_x2": 294.21228, "r_y2": 538.38055, "r_x3": 53.79800000000001, "r_y3": 538.38055, "coord_origin": "TOPLEFT" }, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 557.71417, "r_x1": 294.04709, "r_y1": 557.71417, "r_x2": 294.04709, "r_y2": 549.33955, "r_x3": 53.79800000000001, "r_y3": 549.33955, "coord_origin": "TOPLEFT" }, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 568.67317, "r_x1": 134.28951, "r_y1": 568.67317, "r_x2": 134.28951, "r_y2": 560.29855, "r_x3": 53.79800000000001, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.29201, "r_y0": 565.0235299999999, "r_x1": 137.67381, "r_y1": 565.0235299999999, "r_x2": 137.67381, "r_y2": 558.23083, "r_x3": 134.29201, "r_y3": 558.23083, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 140.418, "r_y0": 568.67317, "r_x1": 294.047, "r_y1": 568.67317, "r_x2": 294.047, "r_y2": 560.29855, "r_x3": 140.418, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 579.63217, "r_x1": 295.10538, "r_y1": 579.63217, "r_x2": 295.10538, "r_y2": 571.25755, "r_x3": 53.79800000000001, "r_y3": 571.25755, "coord_origin": "TOPLEFT" }, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:" }, { "label": "list_item", "id": 9, "page_no": 1, "cluster": { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT" }, "confidence": 0.9687220454216003, "cells": [ { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 592.95618, "r_x1": 74.221352, "r_y1": 592.95618, "r_x2": 74.221352, "r_y2": 584.58156, "r_x3": 64.708, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 592.96515, "r_x1": 146.39589, "r_y1": 592.96515, "r_x2": 146.39589, "r_y2": 584.62639, "r_x3": 78.207001, "r_y3": 584.62639, "coord_origin": "TOPLEFT" }, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 146.41701, "r_y0": 592.95618, "r_x1": 295.03036, "r_y1": 592.95618, "r_x2": 295.03036, "r_y2": 584.58156, "r_x3": 146.41701, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": ": In contrast to PubLayNet and DocBank,", "orig": ": In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.875, "r_y0": 603.91518, "r_x1": 295.56165, "r_y1": 603.91518, "r_x2": 295.56165, "r_y2": 595.54056, "r_x3": 77.875, "r_y3": 595.54056, "coord_origin": "TOPLEFT" }, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 614.87418, "r_x1": 200.6432, "r_y1": 614.87418, "r_x2": 200.6432, "r_y2": 606.49956, "r_x3": 78.207001, "r_y3": 606.49956, "coord_origin": "TOPLEFT" }, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set." }, { "label": "list_item", "id": 11, "page_no": 1, "cluster": { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT" }, "confidence": 0.9594229459762573, "cells": [ { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 625.8331800000001, "r_x1": 74.221352, "r_y1": 625.8331800000001, "r_x2": 74.221352, "r_y2": 617.45856, "r_x3": 64.708, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 625.84215, "r_x1": 167.91745, "r_y1": 625.84215, "r_x2": 167.91745, "r_y2": 617.50339, "r_x3": 78.207001, "r_y3": 617.50339, "coord_origin": "TOPLEFT" }, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.33501, "r_y0": 625.8331800000001, "r_x1": 294.26254, "r_y1": 625.8331800000001, "r_x2": 294.26254, "r_y2": 617.45856, "r_x3": 168.33501, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": ": We include diverse and complex", "orig": ": We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 636.79117, "r_x1": 245.45726000000002, "r_y1": 636.79117, "r_x2": 245.45726000000002, "r_y2": 628.41655, "r_x3": 78.207001, "r_y3": 628.41655, "coord_origin": "TOPLEFT" }, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources." }, { "label": "list_item", "id": 10, "page_no": 1, "cluster": { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT" }, "confidence": 0.9615103006362915, "cells": [ { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 647.75017, "r_x1": 74.221352, "r_y1": 647.75017, "r_x2": 74.221352, "r_y2": 639.37555, "r_x3": 64.708, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 647.75914, "r_x1": 143.51663, "r_y1": 647.75914, "r_x2": 143.51663, "r_y2": 639.42038, "r_x3": 78.207001, "r_y3": 639.42038, "coord_origin": "TOPLEFT" }, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.02, "r_y0": 647.75017, "r_x1": 294.04648, "r_y1": 647.75017, "r_x2": 294.04648, "r_y2": 639.37555, "r_x3": 144.02, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": ": We define 11 class labels to distinguish", "orig": ": We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 658.70917, "r_x1": 294.68381, "r_y1": 658.70917, "r_x2": 294.68381, "r_y2": 650.33455, "r_x3": 78.207001, "r_y3": 650.33455, "coord_origin": "TOPLEFT" }, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 669.66817, "r_x1": 276.33752, "r_y1": 669.66817, "r_x2": 276.33752, "r_y2": 661.29355, "r_x3": 78.207001, "r_y3": 661.29355, "coord_origin": "TOPLEFT" }, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours." }, { "label": "list_item", "id": 13, "page_no": 1, "cluster": { "id": 13, "label": "list_item", "bbox": { "l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT" }, "confidence": 0.9447500109672546, "cells": [ { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 680.62717, "r_x1": 74.221352, "r_y1": 680.62717, "r_x2": 74.221352, "r_y2": 672.25256, "r_x3": 64.708, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 680.63614, "r_x1": 163.78357, "r_y1": 680.63614, "r_x2": 163.78357, "r_y2": 672.29739, "r_x3": 78.207001, "r_y3": 672.29739, "coord_origin": "TOPLEFT" }, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 163.994, "r_y0": 680.62717, "r_x1": 295.56439, "r_y1": 680.62717, "r_x2": 295.56439, "r_y2": 672.25256, "r_x3": 163.994, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": ": A fraction of the pages in the Do-", "orig": ": A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 691.58617, "r_x1": 295.42719, "r_y1": 691.58617, "r_x2": 295.42719, "r_y2": 683.21156, "r_x3": 78.207001, "r_y3": 683.21156, "coord_origin": "TOPLEFT" }, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation." }, { "label": "footnote", "id": 17, "page_no": 1, "cluster": { "id": 17, "label": "footnote", "bbox": { "l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT" }, "confidence": 0.8584771752357483, "cells": [ { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.672001, "r_y0": 708.739891, "r_x1": 216.02750000000003, "r_y1": 708.739891, "r_x2": 216.02750000000003, "r_y2": 702.226364, "r_x3": 53.672001, "r_y3": 702.226364, "coord_origin": "TOPLEFT" }, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet" }, { "label": "text", "id": 18, "page_no": 1, "cluster": { "id": 18, "label": "text", "bbox": { "l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT" }, "confidence": 0.7997146844863892, "cells": [ { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.095, "r_y0": 95.73816, "r_x1": 558.43201, "r_y1": 95.73816, "r_x2": 558.43201, "r_y2": 87.36352999999997, "r_x3": 342.095, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 106.69713999999999, "r_x1": 445.83629999999994, "r_y1": 106.69713999999999, "r_x2": 445.83629999999994, "r_y2": 98.32250999999997, "r_x3": 342.36401, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "This enables experimentation with annotation uncertainty and quality control analysis." }, { "label": "list_item", "id": 12, "page_no": 1, "cluster": { "id": 12, "label": "list_item", "bbox": { "l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9563499093055725, "cells": [ { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.86502, "r_y0": 117.65612999999996, "r_x1": 338.37836, "r_y1": 117.65612999999996, "r_x2": 338.37836, "r_y2": 109.28148999999996, "r_x3": 328.86502, "r_y3": 109.28148999999996, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 117.66516000000001, "r_x1": 487.25296, "r_y1": 117.66516000000001, "r_x2": 487.25296, "r_y2": 109.32641999999998, "r_x3": 342.36401, "r_y3": 109.32641999999998, "coord_origin": "TOPLEFT" }, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.75900000000007, "r_y0": 117.65618999999992, "r_x1": 558.20294, "r_y1": 117.65618999999992, "r_x2": 558.20294, "r_y2": 109.28156000000001, "r_x3": 487.75900000000007, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": ": Like DocBank, we", "orig": ": Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 128.61517000000003, "r_x1": 559.72101, "r_y1": 128.61517000000003, "r_x2": 559.72101, "r_y2": 120.24054000000001, "r_x3": 342.36401, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 139.57317999999998, "r_x1": 558.20117, "r_y1": 139.57317999999998, "r_x2": 558.20117, "r_y2": 131.19854999999995, "r_x3": 342.36401, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 150.53216999999995, "r_x1": 558.20087, "r_y1": 150.53216999999995, "r_x2": 558.20087, "r_y2": 142.15752999999995, "r_x3": 342.36401, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 161.49114999999995, "r_x1": 438.0624399999999, "r_y1": 161.49114999999995, "r_x2": 438.0624399999999, "r_y2": 153.11652000000004, "r_x3": 342.36401, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores." }, { "label": "text", "id": 4, "page_no": 1, "cluster": { "id": 4, "label": "text", "bbox": { "l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9813903570175171, "cells": [ { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 176.35015999999996, "r_x1": 559.19031, "r_y1": 176.35015999999996, "r_x2": 559.19031, "r_y2": 167.97551999999996, "r_x3": 327.918, "r_y3": 167.97551999999996, "coord_origin": "TOPLEFT" }, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 187.30913999999996, "r_x1": 558.20422, "r_y1": 187.30913999999996, "r_x2": 558.20422, "r_y2": 178.93451000000005, "r_x3": 317.62299, "r_y3": 178.93451000000005, "coord_origin": "TOPLEFT" }, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 198.26819, "r_x1": 558.19763, "r_y1": 198.26819, "r_x2": 558.19763, "r_y2": 189.89355, "r_x3": 317.95499, "r_y3": 189.89355, "coord_origin": "TOPLEFT" }, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 209.22717, "r_x1": 558.20612, "r_y1": 209.22717, "r_x2": 558.20612, "r_y2": 200.85253999999998, "r_x3": 317.95499, "r_y3": 200.85253999999998, "coord_origin": "TOPLEFT" }, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 220.18615999999997, "r_x1": 434.94861, "r_y1": 220.18615999999997, "r_x2": 434.94861, "r_y2": 211.81151999999997, "r_x3": 317.95499, "r_y3": 211.81151999999997, "coord_origin": "TOPLEFT" }, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns." }, { "label": "text", "id": 3, "page_no": 1, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT" }, "confidence": 0.9844217300415039, "cells": [ { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 231.14520000000005, "r_x1": 558.19836, "r_y1": 231.14520000000005, "r_x2": 558.19836, "r_y2": 222.77057000000002, "r_x3": 327.918, "r_y3": 222.77057000000002, "coord_origin": "TOPLEFT" }, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.73099, "r_y0": 242.10419000000002, "r_x1": 558.1991, "r_y1": 242.10419000000002, "r_x2": 558.1991, "r_y2": 233.72955000000002, "r_x3": 317.73099, "r_y3": 233.72955000000002, "coord_origin": "TOPLEFT" }, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 253.06317, "r_x1": 558.20416, "r_y1": 253.06317, "r_x2": 558.20416, "r_y2": 244.68854, "r_x3": 317.95499, "r_y3": 244.68854, "coord_origin": "TOPLEFT" }, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 264.02216, "r_x1": 558.20563, "r_y1": 264.02216, "r_x2": 558.20563, "r_y2": 255.64752, "r_x3": 317.95499, "r_y3": 255.64752, "coord_origin": "TOPLEFT" }, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 274.98015999999996, "r_x1": 558.19861, "r_y1": 274.98015999999996, "r_x2": 558.19861, "r_y2": 266.60553000000004, "r_x3": 317.95499, "r_y3": 266.60553000000004, "coord_origin": "TOPLEFT" }, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 285.93918, "r_x1": 558.20416, "r_y1": 285.93918, "r_x2": 558.20416, "r_y2": 277.56458, "r_x3": 317.95499, "r_y3": 277.56458, "coord_origin": "TOPLEFT" }, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 296.89816, "r_x1": 558.20239, "r_y1": 296.89816, "r_x2": 558.20239, "r_y2": 288.52353, "r_x3": 317.95499, "r_y3": 288.52353, "coord_origin": "TOPLEFT" }, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 307.85718, "r_x1": 559.58197, "r_y1": 307.85718, "r_x2": 559.58197, "r_y2": 299.48254, "r_x3": 317.95499, "r_y3": 299.48254, "coord_origin": "TOPLEFT" }, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery." }, { "label": "section_header", "id": 15, "page_no": 1, "cluster": { "id": 15, "label": "section_header", "bbox": { "l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT" }, "confidence": 0.9347459077835083, "cells": [ { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.51797, "r_x1": 323.10388, "r_y1": 331.51797, "r_x2": 323.10388, "r_y2": 321.20889, "r_x3": 317.95499, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.12115, "r_y0": 331.51797, "r_x1": 421.74411, "r_y1": 331.51797, "r_x2": 421.74411, "r_y2": 321.20889, "r_x3": 333.12115, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "2 RELATED WORK" }, { "label": "text", "id": 1, "page_no": 1, "cluster": { "id": 1, "label": "text", "bbox": { "l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT" }, "confidence": 0.9860377907752991, "cells": [ { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.52499, "r_y0": 354.70717999999994, "r_x1": 559.71301, "r_y1": 354.70717999999994, "r_x2": 559.71301, "r_y2": 346.3325500000001, "r_x3": 317.52499, "r_y3": 346.3325500000001, "coord_origin": "TOPLEFT" }, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 365.66617, "r_x1": 558.20276, "r_y1": 365.66617, "r_x2": 558.20276, "r_y2": 357.29153, "r_x3": 317.95499, "r_y3": 357.29153, "coord_origin": "TOPLEFT" }, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 376.62518, "r_x1": 559.71564, "r_y1": 376.62518, "r_x2": 559.71564, "r_y2": 368.25055, "r_x3": 317.62299, "r_y3": 368.25055, "coord_origin": "TOPLEFT" }, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 387.58417, "r_x1": 558.43365, "r_y1": 387.58417, "r_x2": 558.43365, "r_y2": 379.2095299999999, "r_x3": 317.95499, "r_y3": 379.2095299999999, "coord_origin": "TOPLEFT" }, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 398.54318, "r_x1": 559.18658, "r_y1": 398.54318, "r_x2": 559.18658, "r_y2": 390.16855000000004, "r_x3": 317.95499, "r_y3": 390.16855000000004, "coord_origin": "TOPLEFT" }, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 409.50217, "r_x1": 558.20502, "r_y1": 409.50217, "r_x2": 558.20502, "r_y2": 401.12753, "r_x3": 317.95499, "r_y3": 401.12753, "coord_origin": "TOPLEFT" }, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 420.46118, "r_x1": 558.20422, "r_y1": 420.46118, "r_x2": 558.20422, "r_y2": 412.08655, "r_x3": 317.95499, "r_y3": 412.08655, "coord_origin": "TOPLEFT" }, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 431.4201699999999, "r_x1": 558.20477, "r_y1": 431.4201699999999, "r_x2": 558.20477, "r_y2": 423.04553, "r_x3": 317.95499, "r_y3": 423.04553, "coord_origin": "TOPLEFT" }, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 442.37817, "r_x1": 558.19952, "r_y1": 442.37817, "r_x2": 558.19952, "r_y2": 434.00354, "r_x3": 317.95499, "r_y3": 434.00354, "coord_origin": "TOPLEFT" }, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 453.33716, "r_x1": 559.71613, "r_y1": 453.33716, "r_x2": 559.71613, "r_y2": 444.96252, "r_x3": 317.95499, "r_y3": 444.96252, "coord_origin": "TOPLEFT" }, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 464.29617, "r_x1": 350.90652, "r_y1": 464.29617, "r_x2": 350.90652, "r_y2": 455.92154, "r_x3": 317.95499, "r_y3": 455.92154, "coord_origin": "TOPLEFT" }, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]." }, { "label": "text", "id": 0, "page_no": 1, "cluster": { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT" }, "confidence": 0.9861046671867371, "cells": [ { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 475.25516, "r_x1": 558.19824, "r_y1": 475.25516, "r_x2": 558.19824, "r_y2": 466.88052, "r_x3": 327.918, "r_y3": 466.88052, "coord_origin": "TOPLEFT" }, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 486.21417, "r_x1": 558.20551, "r_y1": 486.21417, "r_x2": 558.20551, "r_y2": 477.83954, "r_x3": 317.95499, "r_y3": 477.83954, "coord_origin": "TOPLEFT" }, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 497.17319, "r_x1": 558.20575, "r_y1": 497.17319, "r_x2": 558.20575, "r_y2": 488.79855, "r_x3": 317.95499, "r_y3": 488.79855, "coord_origin": "TOPLEFT" }, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 508.13217, "r_x1": 559.18646, "r_y1": 508.13217, "r_x2": 559.18646, "r_y2": 499.75754, "r_x3": 317.95499, "r_y3": 499.75754, "coord_origin": "TOPLEFT" }, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 519.09119, "r_x1": 558.2002, "r_y1": 519.09119, "r_x2": 558.2002, "r_y2": 510.71655, "r_x3": 317.95499, "r_y3": 510.71655, "coord_origin": "TOPLEFT" }, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 530.05017, "r_x1": 558.20618, "r_y1": 530.05017, "r_x2": 558.20618, "r_y2": 521.67554, "r_x3": 317.95499, "r_y3": 521.67554, "coord_origin": "TOPLEFT" }, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 541.00917, "r_x1": 558.20239, "r_y1": 541.00917, "r_x2": 558.20239, "r_y2": 532.63455, "r_x3": 317.95499, "r_y3": 532.63455, "coord_origin": "TOPLEFT" }, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.96817, "r_x1": 503.32648, "r_y1": 551.96817, "r_x2": 503.32648, "r_y2": 543.59355, "r_x3": 317.95499, "r_y3": 543.59355, "coord_origin": "TOPLEFT" }, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish." }, { "label": "section_header", "id": 16, "page_no": 1, "cluster": { "id": 16, "label": "section_header", "bbox": { "l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT" }, "confidence": 0.9331073760986328, "cells": [ { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.629, "r_x1": 322.97391, "r_y1": 575.629, "r_x2": 322.97391, "r_y2": 565.3199, "r_x3": 317.95499, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 332.73831, "r_y0": 575.629, "r_x1": 477.45688, "r_y1": 575.629, "r_x2": 477.45688, "r_y2": 565.3199, "r_x3": 332.73831, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "3 THE DOCLAYNET DATASET" }, { "label": "text", "id": 2, "page_no": 1, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT" }, "confidence": 0.9852981567382812, "cells": [ { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 598.81818, "r_x1": 558.20233, "r_y1": 598.81818, "r_x2": 558.20233, "r_y2": 590.4435599999999, "r_x3": 317.95499, "r_y3": 590.4435599999999, "coord_origin": "TOPLEFT" }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 609.77718, "r_x1": 558.20239, "r_y1": 609.77718, "r_x2": 558.20239, "r_y2": 601.40256, "r_x3": 317.95499, "r_y3": 601.40256, "coord_origin": "TOPLEFT" }, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 620.73517, "r_x1": 559.7132, "r_y1": 620.73517, "r_x2": 559.7132, "r_y2": 612.36055, "r_x3": 317.95499, "r_y3": 612.36055, "coord_origin": "TOPLEFT" }, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 631.69417, "r_x1": 559.71313, "r_y1": 631.69417, "r_x2": 559.71313, "r_y2": 623.3195499999999, "r_x3": 317.95499, "r_y3": 623.3195499999999, "coord_origin": "TOPLEFT" }, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.65317, "r_x1": 539.92047, "r_y1": 642.65317, "r_x2": 539.92047, "r_y2": 634.27855, "r_x3": 317.95499, "r_y3": 634.27855, "coord_origin": "TOPLEFT" }, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 542.15802, "r_y0": 642.66214, "r_x1": 559.09888, "r_y1": 642.66214, "r_x2": 559.09888, "r_y2": 634.32338, "r_x3": 542.15802, "r_y3": 634.32338, "coord_origin": "TOPLEFT" }, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 653.62114, "r_x1": 331.86273, "r_y1": 653.62114, "r_x2": 331.86273, "r_y2": 645.28238, "r_x3": 317.95499, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.86301, "r_y0": 653.61217, "r_x1": 333.83957, "r_y1": 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"r_x2": 445.50800000000004, "r_y2": 656.2413799999999, "r_x3": 429.74399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 445.50800000000004, "r_y0": 664.5711699999999, "r_x1": 558.20227, "r_y1": 664.5711699999999, "r_x2": 558.20227, "r_y2": 656.19655, "r_x3": 445.50800000000004, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ". Our reasoning for picking this", "orig": ". Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.53017, "r_x1": 472.22198, "r_y1": 675.53017, "r_x2": 472.22198, "r_y2": 667.15556, "r_x3": 317.95499, "r_y3": 667.15556, "coord_origin": "TOPLEFT" }, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4." }, { "label": "text", "id": 8, "page_no": 1, "cluster": { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT" }, "confidence": 0.9749757051467896, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 686.4891700000001, "r_x1": 558.19843, "r_y1": 686.4891700000001, "r_x2": 558.19843, "r_y2": 678.11456, "r_x3": 327.918, "r_y3": 678.11456, "coord_origin": "TOPLEFT" }, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.448174, "r_x1": 558.19867, "r_y1": 697.448174, "r_x2": 558.19867, "r_y2": 689.07355, "r_x3": 317.95499, "r_y3": 689.07355, "coord_origin": "TOPLEFT" }, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.4071730000001, "r_x1": 558.2041, "r_y1": 708.4071730000001, "r_x2": 558.2041, "r_y2": 700.032555, "r_x3": 317.95499, "r_y3": 700.032555, "coord_origin": "TOPLEFT" }, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents" } ], "body": [ { "label": "section_header", "id": 14, "page_no": 1, "cluster": { "id": 14, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.9367801547050476, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 96.16900999999996, "r_x1": 59.427395, "r_y1": 96.16900999999996, "r_x2": 59.427395, "r_y2": 85.85986000000003, "r_x3": 53.79800000000001, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.379532, "r_y0": 96.16900999999996, "r_x1": 156.52899, "r_y1": 96.16900999999996, "r_x2": 156.52899, "r_y2": 85.85986000000003, "r_x3": 70.379532, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "1 INTRODUCTION" }, { "label": "text", "id": 5, "page_no": 1, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT" }, "confidence": 0.9802607893943787, "cells": [ { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 119.35815000000002, "r_x1": 303.01697, "r_y1": 119.35815000000002, "r_x2": 303.01697, "r_y2": 110.98352, "r_x3": 53.79800000000001, "r_y3": 110.98352, "coord_origin": "TOPLEFT" }, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 130.31719999999996, "r_x1": 295.55695, "r_y1": 130.31719999999996, "r_x2": 295.55695, "r_y2": 121.94256999999993, "r_x3": 53.528999, "r_y3": 121.94256999999993, "coord_origin": "TOPLEFT" }, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 141.27617999999995, "r_x1": 294.04642, "r_y1": 141.27617999999995, "r_x2": 294.04642, "r_y2": 132.90155000000004, "r_x3": 53.79800000000001, "r_y3": 132.90155000000004, "coord_origin": "TOPLEFT" }, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 152.23419, "r_x1": 294.04733, "r_y1": 152.23419, "r_x2": 294.04733, "r_y2": 143.85956, "r_x3": 53.79800000000001, "r_y3": 143.85956, "coord_origin": "TOPLEFT" }, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 163.19317999999998, "r_x1": 294.04349, "r_y1": 163.19317999999998, "r_x2": 294.04349, "r_y2": 154.81853999999998, "r_x3": 53.79800000000001, "r_y3": 154.81853999999998, "coord_origin": "TOPLEFT" }, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 174.15215999999998, "r_x1": 294.04718, "r_y1": 174.15215999999998, "r_x2": 294.04718, "r_y2": 165.77752999999996, "r_x3": 53.79800000000001, "r_y3": 165.77752999999996, "coord_origin": "TOPLEFT" }, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 185.11114999999995, "r_x1": 294.04919, "r_y1": 185.11114999999995, "r_x2": 294.04919, "r_y2": 176.73650999999995, "r_x3": 53.79800000000001, "r_y3": 176.73650999999995, "coord_origin": "TOPLEFT" }, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 196.07019000000003, "r_x1": 294.27573, "r_y1": 196.07019000000003, "r_x2": 294.27573, "r_y2": 187.69556, "r_x3": 53.79800000000001, "r_y3": 187.69556, "coord_origin": "TOPLEFT" }, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 207.02917000000002, "r_x1": 295.42569, "r_y1": 207.02917000000002, "r_x2": 295.42569, "r_y2": 198.65454, "r_x3": 53.79800000000001, "r_y3": 198.65454, "coord_origin": "TOPLEFT" }, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 217.98816, "r_x1": 294.37256, "r_y1": 217.98816, "r_x2": 294.37256, "r_y2": 209.61352999999997, "r_x3": 53.528999, "r_y3": 209.61352999999997, "coord_origin": "TOPLEFT" }, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 228.94714, "r_x1": 275.48334, "r_y1": 228.94714, "r_x2": 275.48334, "r_y2": 220.57250999999997, "r_x3": 53.79800000000001, "r_y3": 220.57250999999997, "coord_origin": "TOPLEFT" }, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1." }, { "label": "text", "id": 7, "page_no": 1, "cluster": { "id": 7, "label": "text", "bbox": { "l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT" }, "confidence": 0.9793015718460083, "cells": [ { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 239.90619000000004, "r_x1": 295.564, "r_y1": 239.90619000000004, "r_x2": 295.564, "r_y2": 231.53156, "r_x3": 63.76100199999999, "r_y3": 231.53156, "coord_origin": "TOPLEFT" }, "text": "A key problem in the process of document conversion is to under-", "orig": "A key problem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 250.86517000000003, "r_x1": 294.04868, "r_y1": 250.86517000000003, "r_x2": 294.04868, "r_y2": 242.49054, "r_x3": 53.79800000000001, "r_y3": 242.49054, "coord_origin": "TOPLEFT" }, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 261.82318, "r_x1": 294.04532, "r_y1": 261.82318, "r_x2": 294.04532, "r_y2": 253.44854999999995, "r_x3": 53.79800000000001, "r_y3": 253.44854999999995, "coord_origin": "TOPLEFT" }, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 272.78216999999995, "r_x1": 295.55618, "r_y1": 272.78216999999995, "r_x2": 295.55618, "r_y2": 264.40752999999995, "r_x3": 53.79800000000001, "r_y3": 264.40752999999995, "coord_origin": "TOPLEFT" }, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 283.74118, "r_x1": 294.04059, "r_y1": 283.74118, "r_x2": 294.04059, "r_y2": 275.36658, "r_x3": 53.79800000000001, "r_y3": 275.36658, "coord_origin": "TOPLEFT" }, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 294.70016, "r_x1": 295.55783, "r_y1": 294.70016, "r_x2": 295.55783, "r_y2": 286.32552999999996, "r_x3": 53.79800000000001, "r_y3": 286.32552999999996, "coord_origin": "TOPLEFT" }, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 305.65918000000005, "r_x1": 294.04538, "r_y1": 305.65918000000005, "r_x2": 294.04538, "r_y2": 297.28455, "r_x3": 53.79800000000001, "r_y3": 297.28455, "coord_origin": "TOPLEFT" }, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 316.61816, "r_x1": 294.043, "r_y1": 316.61816, "r_x2": 294.043, "r_y2": 308.24353, "r_x3": 53.79800000000001, "r_y3": 308.24353, "coord_origin": "TOPLEFT" }, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 327.57718, "r_x1": 295.56412, "r_y1": 327.57718, "r_x2": 295.56412, "r_y2": 319.20255, "r_x3": 53.79800000000001, "r_y3": 319.20255, "coord_origin": "TOPLEFT" }, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 338.53616, "r_x1": 294.04532, "r_y1": 338.53616, "r_x2": 294.04532, "r_y2": 330.16153, "r_x3": 53.79800000000001, "r_y3": 330.16153, "coord_origin": "TOPLEFT" }, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 349.49518, "r_x1": 294.04538, "r_y1": 349.49518, "r_x2": 294.04538, "r_y2": 341.12054, "r_x3": 53.79800000000001, "r_y3": 341.12054, "coord_origin": "TOPLEFT" }, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 360.45416000000006, "r_x1": 294.04712, "r_y1": 360.45416000000006, "r_x2": 294.04712, "r_y2": 352.07953, "r_x3": 53.79800000000001, "r_y3": 352.07953, "coord_origin": "TOPLEFT" }, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 371.41318, "r_x1": 294.04538, "r_y1": 371.41318, "r_x2": 294.04538, "r_y2": 363.03853999999995, "r_x3": 53.79800000000001, "r_y3": 363.03853999999995, "coord_origin": "TOPLEFT" }, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 382.37119, "r_x1": 295.55643, "r_y1": 382.37119, "r_x2": 295.55643, "r_y2": 373.99655, "r_x3": 53.79800000000001, "r_y3": 373.99655, "coord_origin": "TOPLEFT" }, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 393.33017, "r_x1": 246.75909, "r_y1": 393.33017, "r_x2": 246.75909, "r_y2": 384.95554, "r_x3": 53.79800000000001, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "itories (PubMed and arXiv), which provide XML or L", "orig": "itories (PubMed and arXiv), which provide XML or L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 243.53101000000004, "r_y0": 391.82455, "r_x1": 248.58553000000003, "r_y1": 391.82455, "r_x2": 248.58553000000003, "r_y2": 385.03183000000007, "r_x3": 243.53101000000004, "r_y3": 385.03183000000007, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 247.24099999999999, "r_y0": 393.33017, "r_x1": 252.58859, "r_y1": 393.33017, "r_x2": 252.58859, "r_y2": 384.95554, "r_x3": 247.24099999999999, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 251.09398999999996, "r_y0": 395.25418, "r_x1": 256.08829, "r_y1": 395.25418, "r_x2": 256.08829, "r_y2": 386.87954999999994, "r_x3": 251.09398999999996, "r_y3": 386.87954999999994, "coord_origin": "TOPLEFT" }, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 254.967, "r_y0": 393.33017, "r_x1": 261.33725, "r_y1": 393.33017, "r_x2": 261.33725, "r_y2": 384.95554, "r_x3": 254.967, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "X", "orig": "X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 263.75021, "r_y0": 393.33017, "r_x1": 295.42773, "r_y1": 393.33017, "r_x2": 295.42773, "r_y2": 384.95554, "r_x3": 263.75021, "r_y3": 384.95554, "coord_origin": "TOPLEFT" }, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 404.28918, "r_x1": 294.21713, "r_y1": 404.28918, "r_x2": 294.21713, "r_y2": 395.91455, "r_x3": 53.528999, "r_y3": 395.91455, "coord_origin": "TOPLEFT" }, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 415.24817, "r_x1": 294.27386, "r_y1": 415.24817, "r_x2": 294.27386, "r_y2": 406.87354, "r_x3": 53.79800000000001, "r_y3": 406.87354, "coord_origin": "TOPLEFT" }, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 426.20717999999994, "r_x1": 295.03488, "r_y1": 426.20717999999994, "r_x2": 295.03488, "r_y2": 417.8325500000001, "r_x3": 53.79800000000001, "r_y3": 417.8325500000001, "coord_origin": "TOPLEFT" }, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 437.16617, "r_x1": 294.04691, "r_y1": 437.16617, "r_x2": 294.04691, "r_y2": 428.79153, "r_x3": 53.79800000000001, "r_y3": 428.79153, "coord_origin": "TOPLEFT" }, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 448.12518, "r_x1": 294.04865, "r_y1": 448.12518, "r_x2": 294.04865, "r_y2": 439.75055, "r_x3": 53.57400100000001, "r_y3": 439.75055, "coord_origin": "TOPLEFT" }, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 459.08417, "r_x1": 294.21643, "r_y1": 459.08417, "r_x2": 294.21643, "r_y2": 450.7095299999999, "r_x3": 53.79800000000001, "r_y3": 450.7095299999999, "coord_origin": "TOPLEFT" }, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 470.04318, "r_x1": 295.42181, "r_y1": 470.04318, "r_x2": 295.42181, "r_y2": 461.66855, "r_x3": 53.79800000000001, "r_y3": 461.66855, "coord_origin": "TOPLEFT" }, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 481.00217, "r_x1": 125.52795, "r_y1": 481.00217, "r_x2": 125.52795, "r_y2": 472.62753, "r_x3": 53.79800000000001, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 128.608, "r_y0": 481.01114, "r_x1": 153.7679, "r_y1": 481.01114, "r_x2": 153.7679, "r_y2": 472.67239, "r_x3": 128.608, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.15199, "r_y0": 481.00217, "r_x1": 165.16365, "r_y1": 481.00217, "r_x2": 165.16365, "r_y2": 472.62753, "r_x3": 157.15199, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.248, "r_y0": 481.01114, "r_x1": 201.49272, "r_y1": 481.01114, "r_x2": 201.49272, "r_y2": 472.67239, "r_x3": 168.248, "r_y3": 472.67239, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.78799, "r_y0": 481.00217, "r_x1": 294.21494, "r_y1": 481.00217, "r_x2": 294.21494, "r_y2": 472.62753, "r_x3": 204.78799, "r_y3": 472.62753, "coord_origin": "TOPLEFT" }, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 491.96017, "r_x1": 294.04715, "r_y1": 491.96017, "r_x2": 294.04715, "r_y2": 483.58554, "r_x3": 53.79800000000001, "r_y3": 483.58554, "coord_origin": "TOPLEFT" }, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 502.91916, "r_x1": 89.080788, "r_y1": 502.91916, "r_x2": 89.080788, "r_y2": 494.54453, "r_x3": 53.79800000000001, "r_y3": 494.54453, "coord_origin": "TOPLEFT" }, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5." }, { "label": "text", "id": 6, "page_no": 1, "cluster": { "id": 6, "label": "text", "bbox": { "l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT" }, "confidence": 0.9795253276824951, "cells": [ { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 513.87817, "r_x1": 295.56396, "r_y1": 513.87817, "r_x2": 295.56396, "r_y2": 505.50354, "r_x3": 63.76100199999999, "r_y3": 505.50354, "coord_origin": "TOPLEFT" }, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 524.83716, "r_x1": 294.21271, "r_y1": 524.83716, "r_x2": 294.21271, "r_y2": 516.46252, "r_x3": 53.79800000000001, "r_y3": 516.46252, "coord_origin": "TOPLEFT" }, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.591999, "r_y0": 535.7961700000001, "r_x1": 61.92275599999999, "r_y1": 535.7961700000001, "r_x2": 61.92275599999999, "r_y2": 527.42154, "r_x3": 53.591999, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.162201, "r_y0": 535.7961700000001, "r_x1": 294.04626, "r_y1": 535.7961700000001, "r_x2": 294.04626, "r_y2": 527.42154, "r_x3": 64.162201, "r_y3": 527.42154, "coord_origin": "TOPLEFT" }, "text": "distinct class labels on 80863 unique document pages, of which", "orig": "distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 546.75517, "r_x1": 294.21228, "r_y1": 546.75517, "r_x2": 294.21228, "r_y2": 538.38055, "r_x3": 53.79800000000001, "r_y3": 538.38055, "coord_origin": "TOPLEFT" }, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 557.71417, "r_x1": 294.04709, "r_y1": 557.71417, "r_x2": 294.04709, "r_y2": 549.33955, "r_x3": 53.79800000000001, "r_y3": 549.33955, "coord_origin": "TOPLEFT" }, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 568.67317, "r_x1": 134.28951, "r_y1": 568.67317, "r_x2": 134.28951, "r_y2": 560.29855, "r_x3": 53.79800000000001, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.29201, "r_y0": 565.0235299999999, "r_x1": 137.67381, "r_y1": 565.0235299999999, "r_x2": 137.67381, "r_y2": 558.23083, "r_x3": 134.29201, "r_y3": 558.23083, "coord_origin": "TOPLEFT" }, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 140.418, "r_y0": 568.67317, "r_x1": 294.047, "r_y1": 568.67317, "r_x2": 294.047, "r_y2": 560.29855, "r_x3": 140.418, "r_y3": 560.29855, "coord_origin": "TOPLEFT" }, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 579.63217, "r_x1": 295.10538, "r_y1": 579.63217, "r_x2": 295.10538, "r_y2": 571.25755, "r_x3": 53.79800000000001, "r_y3": 571.25755, "coord_origin": "TOPLEFT" }, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:" }, { "label": "list_item", "id": 9, "page_no": 1, "cluster": { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT" }, "confidence": 0.9687220454216003, "cells": [ { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 592.95618, "r_x1": 74.221352, "r_y1": 592.95618, "r_x2": 74.221352, "r_y2": 584.58156, "r_x3": 64.708, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 592.96515, "r_x1": 146.39589, "r_y1": 592.96515, "r_x2": 146.39589, "r_y2": 584.62639, "r_x3": 78.207001, "r_y3": 584.62639, "coord_origin": "TOPLEFT" }, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 146.41701, "r_y0": 592.95618, "r_x1": 295.03036, "r_y1": 592.95618, "r_x2": 295.03036, "r_y2": 584.58156, "r_x3": 146.41701, "r_y3": 584.58156, "coord_origin": "TOPLEFT" }, "text": ": In contrast to PubLayNet and DocBank,", "orig": ": In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.875, "r_y0": 603.91518, "r_x1": 295.56165, "r_y1": 603.91518, "r_x2": 295.56165, "r_y2": 595.54056, "r_x3": 77.875, "r_y3": 595.54056, "coord_origin": "TOPLEFT" }, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 614.87418, "r_x1": 200.6432, "r_y1": 614.87418, "r_x2": 200.6432, "r_y2": 606.49956, "r_x3": 78.207001, "r_y3": 606.49956, "coord_origin": "TOPLEFT" }, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set." }, { "label": "list_item", "id": 11, "page_no": 1, "cluster": { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT" }, "confidence": 0.9594229459762573, "cells": [ { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 625.8331800000001, "r_x1": 74.221352, "r_y1": 625.8331800000001, "r_x2": 74.221352, "r_y2": 617.45856, "r_x3": 64.708, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 625.84215, "r_x1": 167.91745, "r_y1": 625.84215, "r_x2": 167.91745, "r_y2": 617.50339, "r_x3": 78.207001, "r_y3": 617.50339, "coord_origin": "TOPLEFT" }, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 168.33501, "r_y0": 625.8331800000001, "r_x1": 294.26254, "r_y1": 625.8331800000001, "r_x2": 294.26254, "r_y2": 617.45856, "r_x3": 168.33501, "r_y3": 617.45856, "coord_origin": "TOPLEFT" }, "text": ": We include diverse and complex", "orig": ": We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 636.79117, "r_x1": 245.45726000000002, "r_y1": 636.79117, "r_x2": 245.45726000000002, "r_y2": 628.41655, "r_x3": 78.207001, "r_y3": 628.41655, "coord_origin": "TOPLEFT" }, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources." }, { "label": "list_item", "id": 10, "page_no": 1, "cluster": { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT" }, "confidence": 0.9615103006362915, "cells": [ { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 647.75017, "r_x1": 74.221352, "r_y1": 647.75017, "r_x2": 74.221352, "r_y2": 639.37555, "r_x3": 64.708, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 647.75914, "r_x1": 143.51663, "r_y1": 647.75914, "r_x2": 143.51663, "r_y2": 639.42038, "r_x3": 78.207001, "r_y3": 639.42038, "coord_origin": "TOPLEFT" }, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.02, "r_y0": 647.75017, "r_x1": 294.04648, "r_y1": 647.75017, "r_x2": 294.04648, "r_y2": 639.37555, "r_x3": 144.02, "r_y3": 639.37555, "coord_origin": "TOPLEFT" }, "text": ": We define 11 class labels to distinguish", "orig": ": We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 658.70917, "r_x1": 294.68381, "r_y1": 658.70917, "r_x2": 294.68381, "r_y2": 650.33455, "r_x3": 78.207001, "r_y3": 650.33455, "coord_origin": "TOPLEFT" }, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 669.66817, "r_x1": 276.33752, "r_y1": 669.66817, "r_x2": 276.33752, "r_y2": 661.29355, "r_x3": 78.207001, "r_y3": 661.29355, "coord_origin": "TOPLEFT" }, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours." }, { "label": "list_item", "id": 13, "page_no": 1, "cluster": { "id": 13, "label": "list_item", "bbox": { "l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT" }, "confidence": 0.9447500109672546, "cells": [ { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 680.62717, "r_x1": 74.221352, "r_y1": 680.62717, "r_x2": 74.221352, "r_y2": 672.25256, "r_x3": 64.708, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 680.63614, "r_x1": 163.78357, "r_y1": 680.63614, "r_x2": 163.78357, "r_y2": 672.29739, "r_x3": 78.207001, "r_y3": 672.29739, "coord_origin": "TOPLEFT" }, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 163.994, "r_y0": 680.62717, "r_x1": 295.56439, "r_y1": 680.62717, "r_x2": 295.56439, "r_y2": 672.25256, "r_x3": 163.994, "r_y3": 672.25256, "coord_origin": "TOPLEFT" }, "text": ": A fraction of the pages in the Do-", "orig": ": A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 691.58617, "r_x1": 295.42719, "r_y1": 691.58617, "r_x2": 295.42719, "r_y2": 683.21156, "r_x3": 78.207001, "r_y3": 683.21156, "coord_origin": "TOPLEFT" }, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation." }, { "label": "footnote", "id": 17, "page_no": 1, "cluster": { "id": 17, "label": "footnote", "bbox": { "l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT" }, "confidence": 0.8584771752357483, "cells": [ { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.672001, "r_y0": 708.739891, "r_x1": 216.02750000000003, "r_y1": 708.739891, "r_x2": 216.02750000000003, "r_y2": 702.226364, "r_x3": 53.672001, "r_y3": 702.226364, "coord_origin": "TOPLEFT" }, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet" }, { "label": "text", "id": 18, "page_no": 1, "cluster": { "id": 18, "label": "text", "bbox": { "l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT" }, "confidence": 0.7997146844863892, "cells": [ { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.095, "r_y0": 95.73816, "r_x1": 558.43201, "r_y1": 95.73816, "r_x2": 558.43201, "r_y2": 87.36352999999997, "r_x3": 342.095, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 106.69713999999999, "r_x1": 445.83629999999994, "r_y1": 106.69713999999999, "r_x2": 445.83629999999994, "r_y2": 98.32250999999997, "r_x3": 342.36401, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "This enables experimentation with annotation uncertainty and quality control analysis." }, { "label": "list_item", "id": 12, "page_no": 1, "cluster": { "id": 12, "label": "list_item", "bbox": { "l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9563499093055725, "cells": [ { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.86502, "r_y0": 117.65612999999996, "r_x1": 338.37836, "r_y1": 117.65612999999996, "r_x2": 338.37836, "r_y2": 109.28148999999996, "r_x3": 328.86502, "r_y3": 109.28148999999996, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 117.66516000000001, "r_x1": 487.25296, "r_y1": 117.66516000000001, "r_x2": 487.25296, "r_y2": 109.32641999999998, "r_x3": 342.36401, "r_y3": 109.32641999999998, "coord_origin": "TOPLEFT" }, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.75900000000007, "r_y0": 117.65618999999992, "r_x1": 558.20294, "r_y1": 117.65618999999992, "r_x2": 558.20294, "r_y2": 109.28156000000001, "r_x3": 487.75900000000007, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": ": Like DocBank, we", "orig": ": Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 128.61517000000003, "r_x1": 559.72101, "r_y1": 128.61517000000003, "r_x2": 559.72101, "r_y2": 120.24054000000001, "r_x3": 342.36401, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 139.57317999999998, "r_x1": 558.20117, "r_y1": 139.57317999999998, "r_x2": 558.20117, "r_y2": 131.19854999999995, "r_x3": 342.36401, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 150.53216999999995, "r_x1": 558.20087, "r_y1": 150.53216999999995, "r_x2": 558.20087, "r_y2": 142.15752999999995, "r_x3": 342.36401, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 342.36401, "r_y0": 161.49114999999995, "r_x1": 438.0624399999999, "r_y1": 161.49114999999995, "r_x2": 438.0624399999999, "r_y2": 153.11652000000004, "r_x3": 342.36401, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores." }, { "label": "text", "id": 4, "page_no": 1, "cluster": { "id": 4, "label": "text", "bbox": { "l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9813903570175171, "cells": [ { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 176.35015999999996, "r_x1": 559.19031, "r_y1": 176.35015999999996, "r_x2": 559.19031, "r_y2": 167.97551999999996, "r_x3": 327.918, "r_y3": 167.97551999999996, "coord_origin": "TOPLEFT" }, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 187.30913999999996, "r_x1": 558.20422, "r_y1": 187.30913999999996, "r_x2": 558.20422, "r_y2": 178.93451000000005, "r_x3": 317.62299, "r_y3": 178.93451000000005, "coord_origin": "TOPLEFT" }, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 198.26819, "r_x1": 558.19763, "r_y1": 198.26819, "r_x2": 558.19763, "r_y2": 189.89355, "r_x3": 317.95499, "r_y3": 189.89355, "coord_origin": "TOPLEFT" }, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 209.22717, "r_x1": 558.20612, "r_y1": 209.22717, "r_x2": 558.20612, "r_y2": 200.85253999999998, "r_x3": 317.95499, "r_y3": 200.85253999999998, "coord_origin": "TOPLEFT" }, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 220.18615999999997, "r_x1": 434.94861, "r_y1": 220.18615999999997, "r_x2": 434.94861, "r_y2": 211.81151999999997, "r_x3": 317.95499, "r_y3": 211.81151999999997, "coord_origin": "TOPLEFT" }, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns." }, { "label": "text", "id": 3, "page_no": 1, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT" }, "confidence": 0.9844217300415039, "cells": [ { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 231.14520000000005, "r_x1": 558.19836, "r_y1": 231.14520000000005, "r_x2": 558.19836, "r_y2": 222.77057000000002, "r_x3": 327.918, "r_y3": 222.77057000000002, "coord_origin": "TOPLEFT" }, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.73099, "r_y0": 242.10419000000002, "r_x1": 558.1991, "r_y1": 242.10419000000002, "r_x2": 558.1991, "r_y2": 233.72955000000002, "r_x3": 317.73099, "r_y3": 233.72955000000002, "coord_origin": "TOPLEFT" }, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 253.06317, "r_x1": 558.20416, "r_y1": 253.06317, "r_x2": 558.20416, "r_y2": 244.68854, "r_x3": 317.95499, "r_y3": 244.68854, "coord_origin": "TOPLEFT" }, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 264.02216, "r_x1": 558.20563, "r_y1": 264.02216, "r_x2": 558.20563, "r_y2": 255.64752, "r_x3": 317.95499, "r_y3": 255.64752, "coord_origin": "TOPLEFT" }, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 274.98015999999996, "r_x1": 558.19861, "r_y1": 274.98015999999996, "r_x2": 558.19861, "r_y2": 266.60553000000004, "r_x3": 317.95499, "r_y3": 266.60553000000004, "coord_origin": "TOPLEFT" }, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 285.93918, "r_x1": 558.20416, "r_y1": 285.93918, "r_x2": 558.20416, "r_y2": 277.56458, "r_x3": 317.95499, "r_y3": 277.56458, "coord_origin": "TOPLEFT" }, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 296.89816, "r_x1": 558.20239, "r_y1": 296.89816, "r_x2": 558.20239, "r_y2": 288.52353, "r_x3": 317.95499, "r_y3": 288.52353, "coord_origin": "TOPLEFT" }, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 307.85718, "r_x1": 559.58197, "r_y1": 307.85718, "r_x2": 559.58197, "r_y2": 299.48254, "r_x3": 317.95499, "r_y3": 299.48254, "coord_origin": "TOPLEFT" }, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery." }, { "label": "section_header", "id": 15, "page_no": 1, "cluster": { "id": 15, "label": "section_header", "bbox": { "l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT" }, "confidence": 0.9347459077835083, "cells": [ { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.51797, "r_x1": 323.10388, "r_y1": 331.51797, "r_x2": 323.10388, "r_y2": 321.20889, "r_x3": 317.95499, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.12115, "r_y0": 331.51797, "r_x1": 421.74411, "r_y1": 331.51797, "r_x2": 421.74411, "r_y2": 321.20889, "r_x3": 333.12115, "r_y3": 321.20889, "coord_origin": "TOPLEFT" }, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "2 RELATED WORK" }, { "label": "text", "id": 1, "page_no": 1, "cluster": { "id": 1, "label": "text", "bbox": { "l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT" }, "confidence": 0.9860377907752991, "cells": [ { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.52499, "r_y0": 354.70717999999994, "r_x1": 559.71301, "r_y1": 354.70717999999994, "r_x2": 559.71301, "r_y2": 346.3325500000001, "r_x3": 317.52499, "r_y3": 346.3325500000001, "coord_origin": "TOPLEFT" }, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 365.66617, "r_x1": 558.20276, "r_y1": 365.66617, "r_x2": 558.20276, "r_y2": 357.29153, "r_x3": 317.95499, "r_y3": 357.29153, "coord_origin": "TOPLEFT" }, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 376.62518, "r_x1": 559.71564, "r_y1": 376.62518, "r_x2": 559.71564, "r_y2": 368.25055, "r_x3": 317.62299, "r_y3": 368.25055, "coord_origin": "TOPLEFT" }, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 387.58417, "r_x1": 558.43365, "r_y1": 387.58417, "r_x2": 558.43365, "r_y2": 379.2095299999999, "r_x3": 317.95499, "r_y3": 379.2095299999999, "coord_origin": "TOPLEFT" }, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 398.54318, "r_x1": 559.18658, "r_y1": 398.54318, "r_x2": 559.18658, "r_y2": 390.16855000000004, "r_x3": 317.95499, "r_y3": 390.16855000000004, "coord_origin": "TOPLEFT" }, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 409.50217, "r_x1": 558.20502, "r_y1": 409.50217, "r_x2": 558.20502, "r_y2": 401.12753, "r_x3": 317.95499, "r_y3": 401.12753, "coord_origin": "TOPLEFT" }, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 420.46118, "r_x1": 558.20422, "r_y1": 420.46118, "r_x2": 558.20422, "r_y2": 412.08655, "r_x3": 317.95499, "r_y3": 412.08655, "coord_origin": "TOPLEFT" }, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 431.4201699999999, "r_x1": 558.20477, "r_y1": 431.4201699999999, "r_x2": 558.20477, "r_y2": 423.04553, "r_x3": 317.95499, "r_y3": 423.04553, "coord_origin": "TOPLEFT" }, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 442.37817, "r_x1": 558.19952, "r_y1": 442.37817, "r_x2": 558.19952, "r_y2": 434.00354, "r_x3": 317.95499, "r_y3": 434.00354, "coord_origin": "TOPLEFT" }, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 453.33716, "r_x1": 559.71613, "r_y1": 453.33716, "r_x2": 559.71613, "r_y2": 444.96252, "r_x3": 317.95499, "r_y3": 444.96252, "coord_origin": "TOPLEFT" }, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 464.29617, "r_x1": 350.90652, "r_y1": 464.29617, "r_x2": 350.90652, "r_y2": 455.92154, "r_x3": 317.95499, "r_y3": 455.92154, "coord_origin": "TOPLEFT" }, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]." }, { "label": "text", "id": 0, "page_no": 1, "cluster": { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT" }, "confidence": 0.9861046671867371, "cells": [ { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 475.25516, "r_x1": 558.19824, "r_y1": 475.25516, "r_x2": 558.19824, "r_y2": 466.88052, "r_x3": 327.918, "r_y3": 466.88052, "coord_origin": "TOPLEFT" }, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 486.21417, "r_x1": 558.20551, "r_y1": 486.21417, "r_x2": 558.20551, "r_y2": 477.83954, "r_x3": 317.95499, "r_y3": 477.83954, "coord_origin": "TOPLEFT" }, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 497.17319, "r_x1": 558.20575, "r_y1": 497.17319, "r_x2": 558.20575, "r_y2": 488.79855, "r_x3": 317.95499, "r_y3": 488.79855, "coord_origin": "TOPLEFT" }, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 508.13217, "r_x1": 559.18646, "r_y1": 508.13217, "r_x2": 559.18646, "r_y2": 499.75754, "r_x3": 317.95499, "r_y3": 499.75754, "coord_origin": "TOPLEFT" }, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 519.09119, "r_x1": 558.2002, "r_y1": 519.09119, "r_x2": 558.2002, "r_y2": 510.71655, "r_x3": 317.95499, "r_y3": 510.71655, "coord_origin": "TOPLEFT" }, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 530.05017, "r_x1": 558.20618, "r_y1": 530.05017, "r_x2": 558.20618, "r_y2": 521.67554, "r_x3": 317.95499, "r_y3": 521.67554, "coord_origin": "TOPLEFT" }, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 541.00917, "r_x1": 558.20239, "r_y1": 541.00917, "r_x2": 558.20239, "r_y2": 532.63455, "r_x3": 317.95499, "r_y3": 532.63455, "coord_origin": "TOPLEFT" }, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.96817, "r_x1": 503.32648, "r_y1": 551.96817, "r_x2": 503.32648, "r_y2": 543.59355, "r_x3": 317.95499, "r_y3": 543.59355, "coord_origin": "TOPLEFT" }, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish." }, { "label": "section_header", "id": 16, "page_no": 1, "cluster": { "id": 16, "label": "section_header", "bbox": { "l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT" }, "confidence": 0.9331073760986328, "cells": [ { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.629, "r_x1": 322.97391, "r_y1": 575.629, "r_x2": 322.97391, "r_y2": 565.3199, "r_x3": 317.95499, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 332.73831, "r_y0": 575.629, "r_x1": 477.45688, "r_y1": 575.629, "r_x2": 477.45688, "r_y2": 565.3199, "r_x3": 332.73831, "r_y3": 565.3199, "coord_origin": "TOPLEFT" }, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "3 THE DOCLAYNET DATASET" }, { "label": "text", "id": 2, "page_no": 1, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT" }, "confidence": 0.9852981567382812, "cells": [ { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 598.81818, "r_x1": 558.20233, "r_y1": 598.81818, "r_x2": 558.20233, "r_y2": 590.4435599999999, "r_x3": 317.95499, "r_y3": 590.4435599999999, "coord_origin": "TOPLEFT" }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 609.77718, "r_x1": 558.20239, "r_y1": 609.77718, "r_x2": 558.20239, "r_y2": 601.40256, "r_x3": 317.95499, "r_y3": 601.40256, "coord_origin": "TOPLEFT" }, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 620.73517, "r_x1": 559.7132, "r_y1": 620.73517, "r_x2": 559.7132, "r_y2": 612.36055, "r_x3": 317.95499, "r_y3": 612.36055, "coord_origin": "TOPLEFT" }, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 631.69417, "r_x1": 559.71313, "r_y1": 631.69417, "r_x2": 559.71313, "r_y2": 623.3195499999999, "r_x3": 317.95499, "r_y3": 623.3195499999999, "coord_origin": "TOPLEFT" }, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.65317, "r_x1": 539.92047, "r_y1": 642.65317, "r_x2": 539.92047, "r_y2": 634.27855, "r_x3": 317.95499, "r_y3": 634.27855, "coord_origin": "TOPLEFT" }, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 542.15802, "r_y0": 642.66214, "r_x1": 559.09888, "r_y1": 642.66214, "r_x2": 559.09888, "r_y2": 634.32338, "r_x3": 542.15802, "r_y3": 634.32338, "coord_origin": "TOPLEFT" }, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 653.62114, "r_x1": 331.86273, "r_y1": 653.62114, "r_x2": 331.86273, "r_y2": 645.28238, "r_x3": 317.95499, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.86301, "r_y0": 653.61217, "r_x1": 333.83957, "r_y1": 653.61217, "r_x2": 333.83957, "r_y2": 645.23755, "r_x3": 331.86301, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 336.064, "r_y0": 653.62114, "r_x1": 366.05368, "r_y1": 653.62114, "r_x2": 366.05368, "r_y2": 645.28238, "r_x3": 336.064, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Footnote", "orig": "Footnote", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 366.05301, "r_y0": 653.61217, "r_x1": 368.02957, "r_y1": 653.61217, "r_x2": 368.02957, "r_y2": 645.23755, "r_x3": 366.05301, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 370.254, "r_y0": 653.62114, "r_x1": 400.05502, "r_y1": 653.62114, "r_x2": 400.05502, "r_y2": 645.28238, "r_x3": 370.254, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 400.05499, "r_y0": 653.61217, "r_x1": 402.03156, "r_y1": 653.61217, "r_x2": 402.03156, "r_y2": 645.23755, "r_x3": 400.05499, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 404.25601, "r_y0": 653.62114, "r_x1": 436.19531, "r_y1": 653.62114, "r_x2": 436.19531, "r_y2": 645.28238, "r_x3": 404.25601, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 436.19501, "r_y0": 653.61217, "r_x1": 438.17157, "r_y1": 653.61217, "r_x2": 438.17157, "r_y2": 645.23755, "r_x3": 436.19501, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 440.396, "r_y0": 653.62114, "r_x1": 480.60988999999995, "r_y1": 653.62114, "r_x2": 480.60988999999995, "r_y2": 645.28238, "r_x3": 440.396, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 138, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 480.60999, "r_y0": 653.61217, "r_x1": 482.58655000000005, "r_y1": 653.61217, "r_x2": 482.58655000000005, "r_y2": 645.23755, "r_x3": 480.60999, "r_y3": 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653.62114, "r_x2": 557.211, "r_y2": 645.28238, "r_x3": 532.57599, "r_y3": 645.28238, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 142, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 557.211, "r_y0": 653.61217, "r_x1": 559.18756, "r_y1": 653.61217, "r_x2": 559.18756, "r_y2": 645.23755, "r_x3": 557.211, "r_y3": 645.23755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 664.58013, "r_x1": 368.78821, "r_y1": 664.58013, "r_x2": 368.78821, "r_y2": 656.2413799999999, "r_x3": 317.95499, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { 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"orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 147, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 395.68399, "r_y0": 664.58013, "r_x1": 410.23538, "r_y1": 664.58013, "r_x2": 410.23538, "r_y2": 656.2413799999999, "r_x3": 395.68399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 148, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 410.23099, "r_y0": 664.5711699999999, "r_x1": 427.5679, "r_y1": 664.5711699999999, "r_x2": 427.5679, "r_y2": 656.19655, "r_x3": 410.23099, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 149, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 429.74399, "r_y0": 664.58013, "r_x1": 445.50800000000004, "r_y1": 664.58013, "r_x2": 445.50800000000004, "r_y2": 656.2413799999999, "r_x3": 429.74399, "r_y3": 656.2413799999999, "coord_origin": "TOPLEFT" }, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 150, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 445.50800000000004, "r_y0": 664.5711699999999, "r_x1": 558.20227, "r_y1": 664.5711699999999, "r_x2": 558.20227, "r_y2": 656.19655, "r_x3": 445.50800000000004, "r_y3": 656.19655, "coord_origin": "TOPLEFT" }, "text": ". Our reasoning for picking this", "orig": ". Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 151, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.53017, "r_x1": 472.22198, "r_y1": 675.53017, "r_x2": 472.22198, "r_y2": 667.15556, "r_x3": 317.95499, "r_y3": 667.15556, "coord_origin": "TOPLEFT" }, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4." }, { "label": "text", "id": 8, "page_no": 1, "cluster": { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT" }, "confidence": 0.9749757051467896, "cells": [ { "index": 152, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 686.4891700000001, "r_x1": 558.19843, "r_y1": 686.4891700000001, "r_x2": 558.19843, "r_y2": 678.11456, "r_x3": 327.918, "r_y3": 678.11456, "coord_origin": "TOPLEFT" }, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.448174, "r_x1": 558.19867, "r_y1": 697.448174, "r_x2": 558.19867, "r_y2": 689.07355, "r_x3": 317.95499, "r_y3": 689.07355, "coord_origin": "TOPLEFT" }, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.4071730000001, "r_x1": 558.2041, "r_y1": 708.4071730000001, "r_x2": 558.2041, "r_y2": 700.032555, "r_x3": 317.95499, "r_y3": 700.032555, "coord_origin": "TOPLEFT" }, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents" } ], "headers": [ { "label": "page_header", "id": 19, "page_no": 1, "cluster": { "id": 19, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.7682238221168518, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" } ] } }, { "page_no": 2, "size": { "width": 612.0, "height": 792.0 }, "parsed_page": { "dimension": { "angle": 0.0, "rect": { "r_x0": 0.0, "r_y0": 0.0, "r_x1": 612.0, "r_y1": 0.0, "r_x2": 612.0, "r_y2": 792.0, "r_x3": 0.0, "r_y3": 792.0, "coord_origin": "BOTTOMLEFT" }, "boundary_type": "crop_box", "art_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "bleed_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "crop_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "media_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "trim_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" } }, "bitmap_resources": [], "char_cells": [], "word_cells": [], "textline_cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 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"orig": "Scientific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 184.40349, "r_y0": 127.20911000000001, "r_x1": 199.66519, "r_y1": 127.20911000000001, "r_x2": 199.66519, "r_y2": 118.68206999999995, "r_x3": 184.40349, "r_y3": 118.68206999999995, "coord_origin": "TOPLEFT" }, "text": "17%", "orig": "17%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 88.288223, "r_y0": 122.88176999999985, "r_x1": 118.80401, "r_y1": 122.88176999999985, "r_x2": 118.80401, "r_y2": 114.35473999999988, "r_x3": 88.288223, "r_y3": 114.35473999999988, "coord_origin": "TOPLEFT" }, "text": "Financial", "orig": "Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 136.24422, "r_y0": 138.77112, 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"from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 139.88339, "r_y0": 221.02739999999994, "r_x1": 157.68491, "r_y1": 221.02739999999994, "r_x2": 157.68491, "r_y2": 212.50036999999998, "r_x3": 139.88339, "r_y3": 212.50036999999998, "coord_origin": "TOPLEFT" }, "text": "Laws", "orig": "Laws", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.43983, "r_y0": 192.30511, "r_x1": 172.70154, "r_y1": 192.30511, "r_x2": 172.70154, "r_y2": 183.77808000000005, "r_x3": 157.43983, "r_y3": 183.77808000000005, "coord_origin": "TOPLEFT" }, "text": "16%", "orig": "16%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 225.47252, "r_y0": 197.82361000000003, "r_x1": 254.29510000000002, "r_y1": 197.82361000000003, "r_x2": 254.29510000000002, "r_y2": 189.29656999999997, "r_x3": 225.47252, "r_y3": 189.29656999999997, "coord_origin": "TOPLEFT" }, "text": "Manuals", "orig": "Manuals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 194.40683, "r_y0": 179.6485, "r_x1": 209.66853, "r_y1": 179.6485, "r_x2": 209.66853, "r_y2": 171.12145999999996, "r_x3": 194.40683, "r_y3": 171.12145999999996, "coord_origin": "TOPLEFT" }, "text": "21%", "orig": "21%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 244.58826, "r_x1": 294.04373, "r_y1": 244.58826, "r_x2": 294.04373, "r_y2": 236.11499000000003, "r_x3": 53.79800000000001, "r_y3": 236.11499000000003, "coord_origin": "TOPLEFT" }, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 255.54724, "r_x1": 96.756027, "r_y1": 255.54724, "r_x2": 96.756027, "r_y2": 247.07397000000003, "r_x3": 53.79800000000001, "r_y3": 247.07397000000003, "coord_origin": "TOPLEFT" }, "text": "categories.", "orig": "categories.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 290.17815999999993, "r_x1": 294.04605, "r_y1": 290.17815999999993, "r_x2": 294.04605, "r_y2": 281.8035300000001, "r_x3": 53.79800000000001, "r_y3": 281.8035300000001, "coord_origin": "TOPLEFT" }, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 301.13718, "r_x1": 294.04868, "r_y1": 301.13718, "r_x2": 294.04868, "r_y2": 292.76254, "r_x3": 53.79800000000001, "r_y3": 292.76254, "coord_origin": "TOPLEFT" }, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 312.09616, "r_x1": 98.881348, "r_y1": 312.09616, "r_x2": 98.881348, "r_y2": 303.72153, "r_x3": 53.79800000000001, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 99.070999, "r_y0": 310.08768, "r_x1": 104.77363, "r_y1": 310.08768, "r_x2": 104.77363, "r_y2": 306.07971, "r_x3": 99.070999, "r_y3": 306.07971, "coord_origin": "TOPLEFT" }, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 107.46399999999998, "r_y0": 312.09616, "r_x1": 115.83373999999999, "r_y1": 312.09616, "r_x2": 115.83373999999999, "r_y2": 303.72153, "r_x3": 107.46399999999998, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 118.08366, "r_y0": 312.09616, "r_x1": 294.26233, "r_y1": 312.09616, "r_x2": 294.26233, "r_y2": 303.72153, "r_x3": 118.08366, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "pages) with technical content, dense in complex", "orig": "pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 323.05518, "r_x1": 294.04715, "r_y1": 323.05518, "r_x2": 294.04715, "r_y2": 314.68054, "r_x3": 53.79800000000001, "r_y3": 314.68054, "coord_origin": "TOPLEFT" }, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 334.01318, "r_x1": 294.27383, "r_y1": 334.01318, "r_x2": 294.27383, "r_y2": 325.63855, "r_x3": 53.79800000000001, "r_y3": 325.63855, "coord_origin": "TOPLEFT" }, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 344.97217, "r_x1": 294.0416, "r_y1": 344.97217, "r_x2": 294.0416, "r_y2": 336.59753, "r_x3": 53.79800000000001, "r_y3": 336.59753, "coord_origin": "TOPLEFT" }, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 355.9311799999999, "r_x1": 294.04712, "r_y1": 355.9311799999999, "r_x2": 294.04712, "r_y2": 347.5565500000001, "r_x3": 53.79800000000001, "r_y3": 347.5565500000001, "coord_origin": "TOPLEFT" }, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 366.89017, "r_x1": 251.73131, "r_y1": 366.89017, "r_x2": 251.73131, "r_y2": 358.51553, "r_x3": 53.79800000000001, "r_y3": 358.51553, "coord_origin": "TOPLEFT" }, "text": "documents or photographs showing \u201ctext in the wild\".", "orig": "documents or photographs showing \u201ctext in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 377.84918, "r_x1": 295.55945, "r_y1": 377.84918, "r_x2": 295.55945, "r_y2": 369.47455, "r_x3": 63.76100199999999, "r_y3": 369.47455, "coord_origin": "TOPLEFT" }, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 388.80816999999996, "r_x1": 105.90533, "r_y1": 388.80816999999996, "r_x2": 105.90533, "r_y2": 380.43353, "r_x3": 53.79800000000001, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": "gories, namely", "orig": "gories, namely", "text_direction": "left_to_right", "confidence": 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Each document cate-", "orig": ". Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.72617, "r_x1": 294.04535, "r_y1": 410.72617, "r_x2": 294.04535, "r_y2": 402.3515300000001, "r_x3": 53.79800000000001, "r_y3": 402.3515300000001, "coord_origin": "TOPLEFT" }, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.68518000000006, "r_x1": 132.19516, "r_y1": 421.68518000000006, "r_x2": 132.19516, "r_y2": 413.31055, "r_x3": 53.79800000000001, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.528, "r_y0": 421.69415, "r_x1": 167.77272, "r_y1": 421.69415, "r_x2": 167.77272, "r_y2": 413.35541, "r_x3": 134.528, "r_y3": 413.35541, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 170.314, "r_y0": 421.68518000000006, "r_x1": 252.36031, "r_y1": 421.68518000000006, "r_x2": 252.36031, "r_y2": 413.31055, "r_x3": 170.314, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "format annual reports", "orig": "format annual reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 252.356, "r_y0": 418.03555, "r_x1": 255.73781, "r_y1": 418.03555, "r_x2": 255.73781, "r_y2": 411.24283, "r_x3": 252.356, "r_y3": 411.24283, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 258.56601, "r_y0": 421.68518000000006, "r_x1": 295.56046, "r_y1": 421.68518000000006, "r_x2": 295.56046, "r_y2": 413.31055, "r_x3": 258.56601, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.6441699999999, "r_x1": 294.04376, "r_y1": 432.6441699999999, "r_x2": 294.04376, "r_y2": 424.26953, "r_x3": 53.79800000000001, "r_y3": 424.26953, "coord_origin": "TOPLEFT" }, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.60217, "r_x1": 197.59023, "r_y1": 443.60217, "r_x2": 197.59023, "r_y2": 435.22754000000003, "r_x3": 53.79800000000001, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 197.591, "r_y0": 443.61115, "r_x1": 258.02774, "r_y1": 443.61115, "r_x2": 258.02774, "r_y2": 435.27240000000006, "r_x3": 197.591, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 260.48901, "r_y0": 443.60217, "r_x1": 273.78104, "r_y1": 443.60217, "r_x2": 273.78104, "r_y2": 435.22754000000003, "r_x3": 260.48901, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 276.03201, "r_y0": 443.61115, "r_x1": 294.94113, "r_y1": 443.61115, "r_x2": 294.94113, "r_y2": 435.27240000000006, "r_x3": 276.03201, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 454.57013, "r_x1": 68.085777, "r_y1": 454.57013, "r_x2": 68.085777, "r_y2": 446.23138, "r_x3": 53.79800000000001, "r_y3": 446.23138, "coord_origin": "TOPLEFT" }, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 68.296997, "r_y0": 454.56116, "r_x1": 294.04565, "r_y1": 454.56116, "r_x2": 294.04565, "r_y2": 446.18652, "r_x3": 68.296997, "r_y3": 446.18652, "coord_origin": "TOPLEFT" }, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.52017, "r_x1": 294.04163, "r_y1": 465.52017, "r_x2": 294.04163, "r_y2": 457.14554, "r_x3": 53.79800000000001, "r_y3": 457.14554, "coord_origin": "TOPLEFT" }, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 476.47919, "r_x1": 294.04889, "r_y1": 476.47919, "r_x2": 294.04889, "r_y2": 468.10455, "r_x3": 53.57400100000001, "r_y3": 468.10455, "coord_origin": "TOPLEFT" }, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 487.43817, "r_x1": 294.04715, "r_y1": 487.43817, "r_x2": 294.04715, "r_y2": 479.06354, "r_x3": 53.79800000000001, "r_y3": 479.06354, "coord_origin": "TOPLEFT" }, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 498.39719, "r_x1": 294.21643, "r_y1": 498.39719, "r_x2": 294.21643, "r_y2": 490.02255, "r_x3": 53.79800000000001, "r_y3": 490.02255, "coord_origin": "TOPLEFT" }, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 509.35617, "r_x1": 112.2948, "r_y1": 509.35617, "r_x2": 112.2948, "r_y2": 500.98154, "r_x3": 53.79800000000001, "r_y3": 500.98154, "coord_origin": "TOPLEFT" }, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 520.31519, "r_x1": 295.55954, "r_y1": 520.31519, "r_x2": 295.55954, "r_y2": 511.94055, "r_x3": 63.76100199999999, "r_y3": 511.94055, "coord_origin": "TOPLEFT" }, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 531.27417, "r_x1": 294.04718, "r_y1": 531.27417, "r_x2": 294.04718, "r_y2": 522.89954, "r_x3": 53.79800000000001, "r_y3": 522.89954, "coord_origin": "TOPLEFT" }, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 542.23317, "r_x1": 295.56155, "r_y1": 542.23317, "r_x2": 295.56155, "r_y2": 533.8585499999999, "r_x3": 53.528999, "r_y3": 533.8585499999999, "coord_origin": "TOPLEFT" }, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 553.19217, "r_x1": 294.04144, "r_y1": 553.19217, "r_x2": 294.04144, "r_y2": 544.81755, "r_x3": 53.79800000000001, "r_y3": 544.81755, "coord_origin": "TOPLEFT" }, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 564.15018, "r_x1": 294.04709, "r_y1": 564.15018, "r_x2": 294.04709, "r_y2": 555.77556, "r_x3": 53.79800000000001, "r_y3": 555.77556, "coord_origin": "TOPLEFT" }, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 575.10918, "r_x1": 294.04163, "r_y1": 575.10918, "r_x2": 294.04163, "r_y2": 566.73456, "r_x3": 53.79800000000001, "r_y3": 566.73456, "coord_origin": "TOPLEFT" }, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 586.06818, "r_x1": 295.5567, "r_y1": 586.06818, "r_x2": 295.5567, "r_y2": 577.6935599999999, "r_x3": 53.79800000000001, "r_y3": 577.6935599999999, "coord_origin": "TOPLEFT" }, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 597.02718, "r_x1": 294.04541, "r_y1": 597.02718, "r_x2": 294.04541, "r_y2": 588.65256, "r_x3": 53.79800000000001, "r_y3": 588.65256, "coord_origin": "TOPLEFT" }, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 607.98618, "r_x1": 164.39928, "r_y1": 607.98618, "r_x2": 164.39928, "r_y2": 599.61156, "r_x3": 53.466999, "r_y3": 599.61156, "coord_origin": "TOPLEFT" }, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 618.9451799999999, "r_x1": 295.56396, "r_y1": 618.9451799999999, "r_x2": 295.56396, "r_y2": 610.57056, "r_x3": 63.76100199999999, "r_y3": 610.57056, "coord_origin": "TOPLEFT" }, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 629.90417, "r_x1": 294.04532, "r_y1": 629.90417, "r_x2": 294.04532, "r_y2": 621.52956, "r_x3": 53.79800000000001, "r_y3": 621.52956, "coord_origin": "TOPLEFT" }, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 640.86317, "r_x1": 294.04538, "r_y1": 640.86317, "r_x2": 294.04538, "r_y2": 632.48856, "r_x3": 53.79800000000001, "r_y3": 632.48856, "coord_origin": "TOPLEFT" }, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 651.82217, "r_x1": 294.04315, "r_y1": 651.82217, "r_x2": 294.04315, "r_y2": 643.4475600000001, "r_x3": 53.79800000000001, "r_y3": 643.4475600000001, "coord_origin": "TOPLEFT" }, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 662.78117, "r_x1": 294.04712, "r_y1": 662.78117, "r_x2": 294.04712, "r_y2": 654.40656, "r_x3": 53.79800000000001, "r_y3": 654.40656, "coord_origin": "TOPLEFT" }, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 673.7391700000001, "r_x1": 294.04333, "r_y1": 673.7391700000001, "r_x2": 294.04333, "r_y2": 665.36456, "r_x3": 53.79800000000001, "r_y3": 665.36456, "coord_origin": "TOPLEFT" }, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 684.69817, "r_x1": 98.916931, "r_y1": 684.69817, "r_x2": 98.916931, "r_y2": 676.32355, "r_x3": 53.79800000000001, "r_y3": 676.32355, "coord_origin": "TOPLEFT" }, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.169891, "r_x1": 195.78998, "r_y1": 708.169891, "r_x2": 195.78998, "r_y2": 701.6563639999999, "r_x3": 53.79800000000001, "r_y3": 701.6563639999999, "coord_origin": "TOPLEFT" }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 95.73816, "r_x1": 558.20093, "r_y1": 95.73816, "r_x2": 558.20093, "r_y2": 87.36352999999997, "r_x3": 327.918, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.69713999999999, "r_x1": 558.20013, "r_y1": 106.69713999999999, "r_x2": 558.20013, "r_y2": 98.32250999999997, "r_x3": 317.95499, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.65618999999992, "r_x1": 558.20056, "r_y1": 117.65618999999992, "r_x2": 558.20056, "r_y2": 109.28156000000001, "r_x3": 317.95499, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 128.61517000000003, "r_x1": 559.19183, "r_y1": 128.61517000000003, "r_x2": 559.19183, "r_y2": 120.24054000000001, "r_x3": 317.95499, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 139.57317999999998, "r_x1": 558.20349, "r_y1": 139.57317999999998, "r_x2": 558.20349, "r_y2": 131.19854999999995, "r_x3": 317.62299, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 150.53216999999995, "r_x1": 558.52936, "r_y1": 150.53216999999995, "r_x2": 558.52936, "r_y2": 142.15752999999995, "r_x3": 317.95499, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 161.49114999999995, "r_x1": 461.6416, "r_y1": 161.49114999999995, "r_x2": 461.6416, "r_y2": 153.11652000000004, "r_x3": 317.95499, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 172.4502, "r_x1": 558.43811, "r_y1": 172.4502, "r_x2": 558.43811, "r_y2": 164.07556, "r_x3": 327.918, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 183.40918, "r_x1": 516.8219, "r_y1": 183.40918, "r_x2": 516.8219, "r_y2": 175.03454999999997, "r_x3": 317.95499, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 519.07501, "r_y0": 183.41814999999997, "r_x1": 558.20135, "r_y1": 183.41814999999997, "r_x2": 558.20135, "r_y2": 175.07941000000005, "r_x3": 519.07501, "r_y3": 175.07941000000005, "coord_origin": "TOPLEFT" }, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 194.36816, "r_x1": 558.20325, "r_y1": 194.36816, "r_x2": 558.20325, "r_y2": 185.99352999999996, "r_x3": 317.95499, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 205.32714999999996, "r_x1": 558.20404, "r_y1": 205.32714999999996, "r_x2": 558.20404, "r_y2": 196.95250999999996, "r_x3": 317.95499, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "file (in JSON format) with the associated page images (in PNG", "orig": "file (in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 216.28619000000003, "r_x1": 364.28769, "r_y1": 216.28619000000003, "r_x2": 364.28769, "r_y2": 207.91156, "r_x3": 317.95499, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 364.28699, "r_y0": 215.55993999999998, "r_x1": 369.98962, "r_y1": 215.55993999999998, "r_x2": 369.98962, "r_y2": 207.85779000000002, "r_x3": 364.28699, "r_y3": 207.85779000000002, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 369.98999, "r_y0": 216.28619000000003, "r_x1": 558.20526, "r_y1": 216.28619000000003, "r_x2": 558.20526, "r_y2": 207.91156, "r_x3": 369.98999, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 227.24518, "r_x1": 558.20251, "r_y1": 227.24518, "r_x2": 558.20251, "r_y2": 218.87054, "r_x3": 317.95499, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 238.20416, "r_x1": 558.203, "r_y1": 238.20416, "r_x2": 558.203, "r_y2": 229.82952999999998, "r_x3": 317.95499, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 249.16216999999995, "r_x1": 558.20404, "r_y1": 249.16216999999995, "r_x2": 558.20404, "r_y2": 240.78754000000004, "r_x3": 317.95499, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 260.12114999999994, "r_x1": 558.20227, "r_y1": 260.12114999999994, "r_x2": 558.20227, "r_y2": 251.74652000000003, "r_x3": 317.95499, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 271.0802, "r_x1": 550.36414, "r_y1": 271.0802, "r_x2": 550.36414, "r_y2": 262.70556999999997, "r_x3": 317.95499, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 282.03918, "r_x1": 559.18488, "r_y1": 282.03918, "r_x2": 559.18488, "r_y2": 273.66454999999996, "r_x3": 327.918, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 292.99817, "r_x1": 559.7132, "r_y1": 292.99817, "r_x2": 559.7132, "r_y2": 284.62354, "r_x3": 317.95499, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 303.95717999999994, "r_x1": 558.20416, "r_y1": 303.95717999999994, "r_x2": 558.20416, "r_y2": 295.5825500000001, "r_x3": 317.95499, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 314.91617, "r_x1": 559.71326, "r_y1": 314.91617, "r_x2": 559.71326, "r_y2": 306.54153, "r_x3": 317.95499, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 325.87518, "r_x1": 558.41443, "r_y1": 325.87518, "r_x2": 558.41443, "r_y2": 317.50055, "r_x3": 317.95499, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 336.83417, "r_x1": 559.71545, "r_y1": 336.83417, "r_x2": 559.71545, "r_y2": 328.4595299999999, "r_x3": 317.95499, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 347.79318, "r_x1": 558.36865, "r_y1": 347.79318, "r_x2": 558.36865, "r_y2": 339.41855000000004, "r_x3": 317.95499, "r_y3": 339.41855000000004, "coord_origin": "TOPLEFT" }, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 358.75217, "r_x1": 558.20062, "r_y1": 358.75217, "r_x2": 558.20062, "r_y2": 350.37753, "r_x3": 317.95499, "r_y3": 350.37753, "coord_origin": "TOPLEFT" }, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 369.71017, "r_x1": 558.20184, "r_y1": 369.71017, "r_x2": 558.20184, "r_y2": 361.33554, "r_x3": 317.95499, "r_y3": 361.33554, "coord_origin": "TOPLEFT" }, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 380.66916, "r_x1": 559.71442, "r_y1": 380.66916, "r_x2": 559.71442, "r_y2": 372.29453, "r_x3": 317.95499, "r_y3": 372.29453, "coord_origin": "TOPLEFT" }, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 391.62817, "r_x1": 559.1864, "r_y1": 391.62817, "r_x2": 559.1864, "r_y2": 383.25354, "r_x3": 317.95499, "r_y3": 383.25354, "coord_origin": "TOPLEFT" }, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 316.94199, "r_y0": 402.58716, "r_x1": 558.20111, "r_y1": 402.58716, "r_x2": 558.20111, "r_y2": 394.21252, "r_x3": 316.94199, "r_y3": 394.21252, "coord_origin": "TOPLEFT" }, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "orig": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 413.54617, "r_x1": 559.7132, "r_y1": 413.54617, "r_x2": 559.7132, "r_y2": 405.17154, "r_x3": 317.95499, "r_y3": 405.17154, "coord_origin": "TOPLEFT" }, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 424.50515999999993, "r_x1": 558.20013, "r_y1": 424.50515999999993, "r_x2": 558.20013, "r_y2": 416.13052, "r_x3": 317.95499, "r_y3": 416.13052, "coord_origin": "TOPLEFT" }, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 435.46417, "r_x1": 464.50613, "r_y1": 435.46417, "r_x2": 464.50613, "r_y2": 427.08953999999994, "r_x3": 317.95499, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.98199000000005, "r_y0": 435.47313999999994, "r_x1": 482.14560000000006, "r_y1": 435.47313999999994, "r_x2": 482.14560000000006, "r_y2": 427.13439999999997, "r_x3": 466.98199000000005, "r_y3": 427.13439999999997, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 485.13199000000003, "r_y0": 435.46417, "r_x1": 558.19727, "r_y1": 435.46417, "r_x2": 558.19727, "r_y2": 427.08953999999994, "r_x3": 485.13199000000003, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 446.4231899999999, "r_x1": 558.20221, "r_y1": 446.4231899999999, "r_x2": 558.20221, "r_y2": 438.04855, "r_x3": 317.95499, "r_y3": 438.04855, "coord_origin": "TOPLEFT" }, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 457.38217, "r_x1": 558.51501, "r_y1": 457.38217, "r_x2": 558.51501, "r_y2": 449.00754, "r_x3": 317.95499, "r_y3": 449.00754, "coord_origin": "TOPLEFT" }, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "orig": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 468.34119, "r_x1": 558.19855, "r_y1": 468.34119, "r_x2": 558.19855, "r_y2": 459.96655000000004, "r_x3": 317.95499, "r_y3": 459.96655000000004, "coord_origin": "TOPLEFT" }, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 479.29916, "r_x1": 559.58215, "r_y1": 479.29916, "r_x2": 559.58215, "r_y2": 470.92453, "r_x3": 317.95499, "r_y3": 470.92453, "coord_origin": "TOPLEFT" }, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "orig": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 490.25818, "r_x1": 558.41559, "r_y1": 490.25818, "r_x2": 558.41559, "r_y2": 481.88354, "r_x3": 317.64099, "r_y3": 481.88354, "coord_origin": "TOPLEFT" }, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 501.21716, "r_x1": 559.72156, "r_y1": 501.21716, "r_x2": 559.72156, "r_y2": 492.84253, "r_x3": 317.95499, "r_y3": 492.84253, "coord_origin": "TOPLEFT" }, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 512.1761799999999, "r_x1": 558.19928, "r_y1": 512.1761799999999, "r_x2": 558.19928, "r_y2": 503.80154, "r_x3": 317.95499, "r_y3": 503.80154, "coord_origin": "TOPLEFT" }, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 523.13516, "r_x1": 558.20581, "r_y1": 523.13516, "r_x2": 558.20581, "r_y2": 514.76053, "r_x3": 317.95499, "r_y3": 514.76053, "coord_origin": "TOPLEFT" }, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 534.09418, "r_x1": 559.71729, "r_y1": 534.09418, "r_x2": 559.71729, "r_y2": 525.71954, "r_x3": 317.95499, "r_y3": 525.71954, "coord_origin": "TOPLEFT" }, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 545.05318, "r_x1": 558.20404, "r_y1": 545.05318, "r_x2": 558.20404, "r_y2": 536.6785600000001, "r_x3": 317.95499, "r_y3": 536.6785600000001, "coord_origin": "TOPLEFT" }, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 556.01218, "r_x1": 558.20074, "r_y1": 556.01218, "r_x2": 558.20074, "r_y2": 547.63756, "r_x3": 317.95499, "r_y3": 547.63756, "coord_origin": "TOPLEFT" }, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.97118, "r_x1": 558.20062, "r_y1": 566.97118, "r_x2": 558.20062, "r_y2": 558.59656, "r_x3": 317.95499, "r_y3": 558.59656, "coord_origin": "TOPLEFT" }, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.9301800000001, "r_x1": 558.20416, "r_y1": 577.9301800000001, "r_x2": 558.20416, "r_y2": 569.55556, "r_x3": 317.95499, "r_y3": 569.55556, "coord_origin": "TOPLEFT" }, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 588.88918, "r_x1": 457.62469, "r_y1": 588.88918, "r_x2": 457.62469, "r_y2": 580.51456, "r_x3": 317.95499, "r_y3": 580.51456, "coord_origin": "TOPLEFT" }, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 617.15901, "r_x1": 323.56226, "r_y1": 617.15901, "r_x2": 323.56226, "r_y2": 606.84991, "r_x3": 317.95499, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47134, "r_y0": 617.15901, "r_x1": 470.21326, "r_y1": 617.15901, "r_x2": 470.21326, "r_y2": 606.84991, "r_x3": 334.47134, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 640.34818, "r_x1": 558.20148, "r_y1": 640.34818, "r_x2": 558.20148, "r_y2": 631.97356, "r_x3": 317.686, "r_y3": 631.97356, "coord_origin": "TOPLEFT" }, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.30717, "r_x1": 559.58521, "r_y1": 651.30717, "r_x2": 559.58521, "r_y2": 642.93256, "r_x3": 317.95499, "r_y3": 642.93256, "coord_origin": "TOPLEFT" }, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.26617, "r_x1": 558.20007, "r_y1": 662.26617, "r_x2": 558.20007, "r_y2": 653.89156, "r_x3": 317.95499, "r_y3": 653.89156, "coord_origin": "TOPLEFT" }, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.22517, "r_x1": 559.71375, "r_y1": 673.22517, "r_x2": 559.71375, "r_y2": 664.85056, "r_x3": 317.95499, "r_y3": 664.85056, "coord_origin": "TOPLEFT" }, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.18417, "r_x1": 558.20233, "r_y1": 684.18417, "r_x2": 558.20233, "r_y2": 675.80956, "r_x3": 317.95499, "r_y3": 675.80956, "coord_origin": "TOPLEFT" }, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 695.143173, "r_x1": 559.71381, "r_y1": 695.143173, "r_x2": 559.71381, "r_y2": 686.76855, "r_x3": 317.95499, "r_y3": 686.76855, "coord_origin": "TOPLEFT" }, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 706.102173, "r_x1": 559.1864, "r_y1": 706.102173, "r_x2": 559.1864, "r_y2": 697.7275539999999, "r_x3": 317.95499, "r_y3": 697.7275539999999, "coord_origin": "TOPLEFT" }, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. 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As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 312.09616, "r_x1": 98.881348, "r_y1": 312.09616, "r_x2": 98.881348, "r_y2": 303.72153, "r_x3": 53.79800000000001, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 99.070999, "r_y0": 310.08768, "r_x1": 104.77363, "r_y1": 310.08768, "r_x2": 104.77363, "r_y2": 306.07971, "r_x3": 99.070999, "r_y3": 306.07971, "coord_origin": "TOPLEFT" }, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 107.46399999999998, "r_y0": 312.09616, "r_x1": 115.83373999999999, "r_y1": 312.09616, "r_x2": 115.83373999999999, "r_y2": 303.72153, "r_x3": 107.46399999999998, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 118.08366, "r_y0": 312.09616, "r_x1": 294.26233, "r_y1": 312.09616, "r_x2": 294.26233, "r_y2": 303.72153, "r_x3": 118.08366, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "pages) with technical content, dense in complex", "orig": "pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 323.05518, "r_x1": 294.04715, "r_y1": 323.05518, "r_x2": 294.04715, "r_y2": 314.68054, "r_x3": 53.79800000000001, "r_y3": 314.68054, "coord_origin": "TOPLEFT" }, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 334.01318, "r_x1": 294.27383, "r_y1": 334.01318, "r_x2": 294.27383, "r_y2": 325.63855, "r_x3": 53.79800000000001, "r_y3": 325.63855, "coord_origin": "TOPLEFT" }, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 344.97217, "r_x1": 294.0416, "r_y1": 344.97217, "r_x2": 294.0416, "r_y2": 336.59753, "r_x3": 53.79800000000001, "r_y3": 336.59753, "coord_origin": "TOPLEFT" }, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 355.9311799999999, "r_x1": 294.04712, "r_y1": 355.9311799999999, "r_x2": 294.04712, "r_y2": 347.5565500000001, "r_x3": 53.79800000000001, "r_y3": 347.5565500000001, "coord_origin": "TOPLEFT" }, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 366.89017, "r_x1": 251.73131, "r_y1": 366.89017, "r_x2": 251.73131, "r_y2": 358.51553, "r_x3": 53.79800000000001, "r_y3": 358.51553, "coord_origin": "TOPLEFT" }, "text": "documents or photographs showing \u201ctext in the wild\".", "orig": "documents or photographs showing \u201ctext in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT" }, "confidence": 0.987700879573822, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 377.84918, "r_x1": 295.55945, "r_y1": 377.84918, "r_x2": 295.55945, "r_y2": 369.47455, "r_x3": 63.76100199999999, "r_y3": 369.47455, "coord_origin": "TOPLEFT" }, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 388.80816999999996, "r_x1": 105.90533, "r_y1": 388.80816999999996, "r_x2": 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"coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 264.54901, "r_y0": 388.80816999999996, "r_x1": 266.48218, "r_y1": 388.80816999999996, "r_x2": 266.48218, "r_y2": 380.43353, "r_x3": 264.54901, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 268.33401, "r_y0": 388.81714, "r_x1": 294.36133, "r_y1": 388.81714, "r_x2": 294.36133, "r_y2": 380.47838999999993, "r_x3": 268.33401, "r_y3": 380.47838999999993, "coord_origin": "TOPLEFT" }, "text": "Laws &", "orig": "Laws &", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 127.17899999999999, "r_y0": 399.76718, "r_x1": 140.60596, "r_y1": 399.76718, "r_x2": 140.60596, "r_y2": 391.39255, "r_x3": 127.17899999999999, "r_y3": 391.39255, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.853, "r_y0": 399.77615, "r_x1": 215.15340000000003, "r_y1": 399.77615, "r_x2": 215.15340000000003, "r_y2": 391.43741000000006, "r_x3": 142.853, "r_y3": 391.43741000000006, "coord_origin": "TOPLEFT" }, "text": "Government Tenders", "orig": "Government Tenders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 215.15601000000004, "r_y0": 399.76718, "r_x1": 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Each document cate-", "orig": ". Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.72617, "r_x1": 294.04535, "r_y1": 410.72617, "r_x2": 294.04535, "r_y2": 402.3515300000001, "r_x3": 53.79800000000001, "r_y3": 402.3515300000001, "coord_origin": "TOPLEFT" }, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.68518000000006, "r_x1": 132.19516, "r_y1": 421.68518000000006, "r_x2": 132.19516, "r_y2": 413.31055, "r_x3": 53.79800000000001, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.528, "r_y0": 421.69415, "r_x1": 167.77272, "r_y1": 421.69415, "r_x2": 167.77272, "r_y2": 413.35541, "r_x3": 134.528, "r_y3": 413.35541, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 170.314, "r_y0": 421.68518000000006, "r_x1": 252.36031, "r_y1": 421.68518000000006, "r_x2": 252.36031, "r_y2": 413.31055, "r_x3": 170.314, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "format annual reports", "orig": "format annual reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 252.356, "r_y0": 418.03555, "r_x1": 255.73781, "r_y1": 418.03555, "r_x2": 255.73781, "r_y2": 411.24283, "r_x3": 252.356, "r_y3": 411.24283, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 258.56601, "r_y0": 421.68518000000006, "r_x1": 295.56046, "r_y1": 421.68518000000006, "r_x2": 295.56046, "r_y2": 413.31055, "r_x3": 258.56601, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.6441699999999, "r_x1": 294.04376, "r_y1": 432.6441699999999, "r_x2": 294.04376, "r_y2": 424.26953, "r_x3": 53.79800000000001, "r_y3": 424.26953, "coord_origin": "TOPLEFT" }, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.60217, "r_x1": 197.59023, "r_y1": 443.60217, "r_x2": 197.59023, "r_y2": 435.22754000000003, "r_x3": 53.79800000000001, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 197.591, "r_y0": 443.61115, "r_x1": 258.02774, "r_y1": 443.61115, "r_x2": 258.02774, "r_y2": 435.27240000000006, "r_x3": 197.591, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 260.48901, "r_y0": 443.60217, "r_x1": 273.78104, "r_y1": 443.60217, "r_x2": 273.78104, "r_y2": 435.22754000000003, "r_x3": 260.48901, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 276.03201, "r_y0": 443.61115, "r_x1": 294.94113, "r_y1": 443.61115, "r_x2": 294.94113, "r_y2": 435.27240000000006, "r_x3": 276.03201, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 454.57013, "r_x1": 68.085777, "r_y1": 454.57013, "r_x2": 68.085777, "r_y2": 446.23138, "r_x3": 53.79800000000001, "r_y3": 446.23138, "coord_origin": "TOPLEFT" }, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 68.296997, "r_y0": 454.56116, "r_x1": 294.04565, "r_y1": 454.56116, "r_x2": 294.04565, "r_y2": 446.18652, "r_x3": 68.296997, "r_y3": 446.18652, "coord_origin": "TOPLEFT" }, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.52017, "r_x1": 294.04163, "r_y1": 465.52017, "r_x2": 294.04163, "r_y2": 457.14554, "r_x3": 53.79800000000001, "r_y3": 457.14554, "coord_origin": "TOPLEFT" }, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 476.47919, "r_x1": 294.04889, "r_y1": 476.47919, "r_x2": 294.04889, "r_y2": 468.10455, "r_x3": 53.57400100000001, "r_y3": 468.10455, "coord_origin": "TOPLEFT" }, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 487.43817, "r_x1": 294.04715, "r_y1": 487.43817, "r_x2": 294.04715, "r_y2": 479.06354, "r_x3": 53.79800000000001, "r_y3": 479.06354, "coord_origin": "TOPLEFT" }, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 498.39719, "r_x1": 294.21643, "r_y1": 498.39719, "r_x2": 294.21643, "r_y2": 490.02255, "r_x3": 53.79800000000001, "r_y3": 490.02255, "coord_origin": "TOPLEFT" }, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 509.35617, "r_x1": 112.2948, "r_y1": 509.35617, "r_x2": 112.2948, "r_y2": 500.98154, "r_x3": 53.79800000000001, "r_y3": 500.98154, "coord_origin": "TOPLEFT" }, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT" }, "confidence": 0.9877581000328064, "cells": [ { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 520.31519, "r_x1": 295.55954, "r_y1": 520.31519, "r_x2": 295.55954, "r_y2": 511.94055, "r_x3": 63.76100199999999, "r_y3": 511.94055, "coord_origin": "TOPLEFT" }, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 531.27417, "r_x1": 294.04718, "r_y1": 531.27417, "r_x2": 294.04718, "r_y2": 522.89954, "r_x3": 53.79800000000001, "r_y3": 522.89954, "coord_origin": "TOPLEFT" }, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 542.23317, "r_x1": 295.56155, "r_y1": 542.23317, "r_x2": 295.56155, "r_y2": 533.8585499999999, "r_x3": 53.528999, "r_y3": 533.8585499999999, "coord_origin": "TOPLEFT" }, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 553.19217, "r_x1": 294.04144, "r_y1": 553.19217, "r_x2": 294.04144, "r_y2": 544.81755, "r_x3": 53.79800000000001, "r_y3": 544.81755, "coord_origin": "TOPLEFT" }, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 564.15018, "r_x1": 294.04709, "r_y1": 564.15018, "r_x2": 294.04709, "r_y2": 555.77556, "r_x3": 53.79800000000001, "r_y3": 555.77556, "coord_origin": "TOPLEFT" }, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 575.10918, "r_x1": 294.04163, "r_y1": 575.10918, "r_x2": 294.04163, "r_y2": 566.73456, "r_x3": 53.79800000000001, "r_y3": 566.73456, "coord_origin": "TOPLEFT" }, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 586.06818, "r_x1": 295.5567, "r_y1": 586.06818, "r_x2": 295.5567, "r_y2": 577.6935599999999, "r_x3": 53.79800000000001, "r_y3": 577.6935599999999, "coord_origin": "TOPLEFT" }, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 597.02718, "r_x1": 294.04541, "r_y1": 597.02718, "r_x2": 294.04541, "r_y2": 588.65256, "r_x3": 53.79800000000001, "r_y3": 588.65256, "coord_origin": "TOPLEFT" }, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 607.98618, "r_x1": 164.39928, "r_y1": 607.98618, "r_x2": 164.39928, "r_y2": 599.61156, "r_x3": 53.466999, "r_y3": 599.61156, "coord_origin": "TOPLEFT" }, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT" }, "confidence": 0.9880735278129578, "cells": [ { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 618.9451799999999, "r_x1": 295.56396, "r_y1": 618.9451799999999, "r_x2": 295.56396, "r_y2": 610.57056, "r_x3": 63.76100199999999, "r_y3": 610.57056, "coord_origin": "TOPLEFT" }, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 629.90417, "r_x1": 294.04532, "r_y1": 629.90417, "r_x2": 294.04532, "r_y2": 621.52956, "r_x3": 53.79800000000001, "r_y3": 621.52956, "coord_origin": "TOPLEFT" }, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 640.86317, "r_x1": 294.04538, "r_y1": 640.86317, "r_x2": 294.04538, "r_y2": 632.48856, "r_x3": 53.79800000000001, "r_y3": 632.48856, "coord_origin": "TOPLEFT" }, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 651.82217, "r_x1": 294.04315, "r_y1": 651.82217, "r_x2": 294.04315, "r_y2": 643.4475600000001, "r_x3": 53.79800000000001, "r_y3": 643.4475600000001, "coord_origin": "TOPLEFT" }, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 662.78117, "r_x1": 294.04712, "r_y1": 662.78117, "r_x2": 294.04712, "r_y2": 654.40656, "r_x3": 53.79800000000001, "r_y3": 654.40656, "coord_origin": "TOPLEFT" }, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 673.7391700000001, "r_x1": 294.04333, "r_y1": 673.7391700000001, "r_x2": 294.04333, "r_y2": 665.36456, "r_x3": 53.79800000000001, "r_y3": 665.36456, "coord_origin": "TOPLEFT" }, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 684.69817, "r_x1": 98.916931, "r_y1": 684.69817, "r_x2": 98.916931, "r_y2": 676.32355, "r_x3": 53.79800000000001, "r_y3": 676.32355, "coord_origin": "TOPLEFT" }, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 12, "label": "footnote", "bbox": { "l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT" }, "confidence": 0.9048352241516113, "cells": [ { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.169891, "r_x1": 195.78998, "r_y1": 708.169891, "r_x2": 195.78998, "r_y2": 701.6563639999999, "r_x3": 53.79800000000001, "r_y3": 701.6563639999999, "coord_origin": "TOPLEFT" }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.986920952796936, "cells": [ { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 95.73816, "r_x1": 558.20093, "r_y1": 95.73816, "r_x2": 558.20093, "r_y2": 87.36352999999997, "r_x3": 327.918, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.69713999999999, "r_x1": 558.20013, "r_y1": 106.69713999999999, "r_x2": 558.20013, "r_y2": 98.32250999999997, "r_x3": 317.95499, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.65618999999992, "r_x1": 558.20056, "r_y1": 117.65618999999992, "r_x2": 558.20056, "r_y2": 109.28156000000001, "r_x3": 317.95499, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 128.61517000000003, "r_x1": 559.19183, "r_y1": 128.61517000000003, "r_x2": 559.19183, "r_y2": 120.24054000000001, "r_x3": 317.95499, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 139.57317999999998, "r_x1": 558.20349, "r_y1": 139.57317999999998, "r_x2": 558.20349, "r_y2": 131.19854999999995, "r_x3": 317.62299, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 150.53216999999995, "r_x1": 558.52936, "r_y1": 150.53216999999995, "r_x2": 558.52936, "r_y2": 142.15752999999995, "r_x3": 317.95499, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 161.49114999999995, "r_x1": 461.6416, "r_y1": 161.49114999999995, "r_x2": 461.6416, "r_y2": 153.11652000000004, "r_x3": 317.95499, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 6, "label": "text", "bbox": { "l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT" }, "confidence": 0.9860605001449585, "cells": [ { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 172.4502, "r_x1": 558.43811, "r_y1": 172.4502, "r_x2": 558.43811, "r_y2": 164.07556, "r_x3": 327.918, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 183.40918, "r_x1": 516.8219, "r_y1": 183.40918, "r_x2": 516.8219, "r_y2": 175.03454999999997, "r_x3": 317.95499, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 519.07501, "r_y0": 183.41814999999997, "r_x1": 558.20135, "r_y1": 183.41814999999997, "r_x2": 558.20135, "r_y2": 175.07941000000005, "r_x3": 519.07501, "r_y3": 175.07941000000005, "coord_origin": "TOPLEFT" }, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 194.36816, "r_x1": 558.20325, "r_y1": 194.36816, "r_x2": 558.20325, "r_y2": 185.99352999999996, "r_x3": 317.95499, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 205.32714999999996, "r_x1": 558.20404, "r_y1": 205.32714999999996, "r_x2": 558.20404, "r_y2": 196.95250999999996, "r_x3": 317.95499, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "file (in JSON format) with the associated page images (in PNG", "orig": "file (in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 216.28619000000003, "r_x1": 364.28769, "r_y1": 216.28619000000003, "r_x2": 364.28769, "r_y2": 207.91156, "r_x3": 317.95499, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 364.28699, "r_y0": 215.55993999999998, "r_x1": 369.98962, "r_y1": 215.55993999999998, "r_x2": 369.98962, "r_y2": 207.85779000000002, "r_x3": 364.28699, "r_y3": 207.85779000000002, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 369.98999, "r_y0": 216.28619000000003, "r_x1": 558.20526, "r_y1": 216.28619000000003, "r_x2": 558.20526, "r_y2": 207.91156, "r_x3": 369.98999, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 227.24518, "r_x1": 558.20251, "r_y1": 227.24518, "r_x2": 558.20251, "r_y2": 218.87054, "r_x3": 317.95499, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 238.20416, "r_x1": 558.203, "r_y1": 238.20416, "r_x2": 558.203, "r_y2": 229.82952999999998, "r_x3": 317.95499, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 249.16216999999995, "r_x1": 558.20404, "r_y1": 249.16216999999995, "r_x2": 558.20404, "r_y2": 240.78754000000004, "r_x3": 317.95499, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 260.12114999999994, "r_x1": 558.20227, "r_y1": 260.12114999999994, "r_x2": 558.20227, "r_y2": 251.74652000000003, "r_x3": 317.95499, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 271.0802, "r_x1": 550.36414, "r_y1": 271.0802, "r_x2": 550.36414, "r_y2": 262.70556999999997, "r_x3": 317.95499, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 7, "label": "text", "bbox": { "l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT" }, "confidence": 0.9853296875953674, "cells": [ { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 282.03918, "r_x1": 559.18488, "r_y1": 282.03918, "r_x2": 559.18488, "r_y2": 273.66454999999996, "r_x3": 327.918, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 292.99817, "r_x1": 559.7132, "r_y1": 292.99817, "r_x2": 559.7132, "r_y2": 284.62354, "r_x3": 317.95499, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 303.95717999999994, "r_x1": 558.20416, "r_y1": 303.95717999999994, "r_x2": 558.20416, "r_y2": 295.5825500000001, "r_x3": 317.95499, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 314.91617, "r_x1": 559.71326, "r_y1": 314.91617, "r_x2": 559.71326, "r_y2": 306.54153, "r_x3": 317.95499, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 325.87518, "r_x1": 558.41443, "r_y1": 325.87518, "r_x2": 558.41443, "r_y2": 317.50055, "r_x3": 317.95499, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 336.83417, "r_x1": 559.71545, "r_y1": 336.83417, "r_x2": 559.71545, "r_y2": 328.4595299999999, "r_x3": 317.95499, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 347.79318, "r_x1": 558.36865, "r_y1": 347.79318, "r_x2": 558.36865, "r_y2": 339.41855000000004, "r_x3": 317.95499, "r_y3": 339.41855000000004, "coord_origin": "TOPLEFT" }, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 358.75217, "r_x1": 558.20062, "r_y1": 358.75217, "r_x2": 558.20062, "r_y2": 350.37753, "r_x3": 317.95499, "r_y3": 350.37753, "coord_origin": "TOPLEFT" }, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 369.71017, "r_x1": 558.20184, "r_y1": 369.71017, "r_x2": 558.20184, "r_y2": 361.33554, "r_x3": 317.95499, "r_y3": 361.33554, "coord_origin": "TOPLEFT" }, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 380.66916, "r_x1": 559.71442, "r_y1": 380.66916, "r_x2": 559.71442, "r_y2": 372.29453, "r_x3": 317.95499, "r_y3": 372.29453, "coord_origin": "TOPLEFT" }, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 391.62817, "r_x1": 559.1864, "r_y1": 391.62817, "r_x2": 559.1864, "r_y2": 383.25354, "r_x3": 317.95499, "r_y3": 383.25354, "coord_origin": "TOPLEFT" }, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 316.94199, "r_y0": 402.58716, "r_x1": 558.20111, "r_y1": 402.58716, "r_x2": 558.20111, "r_y2": 394.21252, "r_x3": 316.94199, "r_y3": 394.21252, "coord_origin": "TOPLEFT" }, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "orig": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 413.54617, "r_x1": 559.7132, "r_y1": 413.54617, "r_x2": 559.7132, "r_y2": 405.17154, "r_x3": 317.95499, "r_y3": 405.17154, "coord_origin": "TOPLEFT" }, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 424.50515999999993, "r_x1": 558.20013, "r_y1": 424.50515999999993, "r_x2": 558.20013, "r_y2": 416.13052, "r_x3": 317.95499, "r_y3": 416.13052, "coord_origin": "TOPLEFT" }, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 435.46417, "r_x1": 464.50613, "r_y1": 435.46417, "r_x2": 464.50613, "r_y2": 427.08953999999994, "r_x3": 317.95499, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.98199000000005, "r_y0": 435.47313999999994, "r_x1": 482.14560000000006, "r_y1": 435.47313999999994, "r_x2": 482.14560000000006, "r_y2": 427.13439999999997, "r_x3": 466.98199000000005, "r_y3": 427.13439999999997, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 485.13199000000003, "r_y0": 435.46417, "r_x1": 558.19727, "r_y1": 435.46417, "r_x2": 558.19727, "r_y2": 427.08953999999994, "r_x3": 485.13199000000003, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 446.4231899999999, "r_x1": 558.20221, "r_y1": 446.4231899999999, "r_x2": 558.20221, "r_y2": 438.04855, "r_x3": 317.95499, "r_y3": 438.04855, "coord_origin": "TOPLEFT" }, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 457.38217, "r_x1": 558.51501, "r_y1": 457.38217, "r_x2": 558.51501, "r_y2": 449.00754, "r_x3": 317.95499, "r_y3": 449.00754, "coord_origin": "TOPLEFT" }, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "orig": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 468.34119, "r_x1": 558.19855, "r_y1": 468.34119, "r_x2": 558.19855, "r_y2": 459.96655000000004, "r_x3": 317.95499, "r_y3": 459.96655000000004, "coord_origin": "TOPLEFT" }, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 479.29916, "r_x1": 559.58215, "r_y1": 479.29916, "r_x2": 559.58215, "r_y2": 470.92453, "r_x3": 317.95499, "r_y3": 470.92453, "coord_origin": "TOPLEFT" }, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "orig": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 490.25818, "r_x1": 558.41559, "r_y1": 490.25818, "r_x2": 558.41559, "r_y2": 481.88354, "r_x3": 317.64099, "r_y3": 481.88354, "coord_origin": "TOPLEFT" }, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 501.21716, "r_x1": 559.72156, "r_y1": 501.21716, "r_x2": 559.72156, "r_y2": 492.84253, "r_x3": 317.95499, "r_y3": 492.84253, "coord_origin": "TOPLEFT" }, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 512.1761799999999, "r_x1": 558.19928, "r_y1": 512.1761799999999, "r_x2": 558.19928, "r_y2": 503.80154, "r_x3": 317.95499, "r_y3": 503.80154, "coord_origin": "TOPLEFT" }, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 523.13516, "r_x1": 558.20581, "r_y1": 523.13516, "r_x2": 558.20581, "r_y2": 514.76053, "r_x3": 317.95499, "r_y3": 514.76053, "coord_origin": "TOPLEFT" }, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 534.09418, "r_x1": 559.71729, "r_y1": 534.09418, "r_x2": 559.71729, "r_y2": 525.71954, "r_x3": 317.95499, "r_y3": 525.71954, "coord_origin": "TOPLEFT" }, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 545.05318, "r_x1": 558.20404, "r_y1": 545.05318, "r_x2": 558.20404, "r_y2": 536.6785600000001, "r_x3": 317.95499, "r_y3": 536.6785600000001, "coord_origin": "TOPLEFT" }, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 556.01218, "r_x1": 558.20074, "r_y1": 556.01218, "r_x2": 558.20074, "r_y2": 547.63756, "r_x3": 317.95499, "r_y3": 547.63756, "coord_origin": "TOPLEFT" }, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.97118, "r_x1": 558.20062, "r_y1": 566.97118, "r_x2": 558.20062, "r_y2": 558.59656, "r_x3": 317.95499, "r_y3": 558.59656, "coord_origin": "TOPLEFT" }, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.9301800000001, "r_x1": 558.20416, "r_y1": 577.9301800000001, "r_x2": 558.20416, "r_y2": 569.55556, "r_x3": 317.95499, "r_y3": 569.55556, "coord_origin": "TOPLEFT" }, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 588.88918, "r_x1": 457.62469, "r_y1": 588.88918, "r_x2": 457.62469, "r_y2": 580.51456, "r_x3": 317.95499, "r_y3": 580.51456, "coord_origin": "TOPLEFT" }, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 10, "label": "section_header", "bbox": { "l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT" }, "confidence": 0.9422592520713806, "cells": [ { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 617.15901, "r_x1": 323.56226, "r_y1": 617.15901, "r_x2": 323.56226, "r_y2": 606.84991, "r_x3": 317.95499, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47134, "r_y0": 617.15901, "r_x1": 470.21326, "r_y1": 617.15901, "r_x2": 470.21326, "r_y2": 606.84991, "r_x3": 334.47134, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "text", "bbox": { "l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT" }, "confidence": 0.9863831996917725, "cells": [ { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 640.34818, "r_x1": 558.20148, "r_y1": 640.34818, "r_x2": 558.20148, "r_y2": 631.97356, "r_x3": 317.686, "r_y3": 631.97356, "coord_origin": "TOPLEFT" }, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. 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"coord_origin": "TOPLEFT" }, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 301.13718, "r_x1": 294.04868, "r_y1": 301.13718, "r_x2": 294.04868, "r_y2": 292.76254, "r_x3": 53.79800000000001, "r_y3": 292.76254, "coord_origin": "TOPLEFT" }, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 312.09616, "r_x1": 98.881348, "r_y1": 312.09616, "r_x2": 98.881348, "r_y2": 303.72153, "r_x3": 53.79800000000001, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 99.070999, "r_y0": 310.08768, "r_x1": 104.77363, "r_y1": 310.08768, "r_x2": 104.77363, "r_y2": 306.07971, "r_x3": 99.070999, "r_y3": 306.07971, "coord_origin": "TOPLEFT" }, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 107.46399999999998, "r_y0": 312.09616, "r_x1": 115.83373999999999, "r_y1": 312.09616, "r_x2": 115.83373999999999, "r_y2": 303.72153, "r_x3": 107.46399999999998, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 118.08366, "r_y0": 312.09616, "r_x1": 294.26233, "r_y1": 312.09616, "r_x2": 294.26233, "r_y2": 303.72153, "r_x3": 118.08366, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "pages) with technical content, dense in complex", "orig": "pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 323.05518, "r_x1": 294.04715, "r_y1": 323.05518, "r_x2": 294.04715, "r_y2": 314.68054, "r_x3": 53.79800000000001, "r_y3": 314.68054, "coord_origin": "TOPLEFT" }, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 334.01318, "r_x1": 294.27383, "r_y1": 334.01318, "r_x2": 294.27383, "r_y2": 325.63855, "r_x3": 53.79800000000001, "r_y3": 325.63855, "coord_origin": "TOPLEFT" }, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 344.97217, "r_x1": 294.0416, "r_y1": 344.97217, "r_x2": 294.0416, "r_y2": 336.59753, "r_x3": 53.79800000000001, "r_y3": 336.59753, "coord_origin": "TOPLEFT" }, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 355.9311799999999, "r_x1": 294.04712, "r_y1": 355.9311799999999, "r_x2": 294.04712, "r_y2": 347.5565500000001, "r_x3": 53.79800000000001, "r_y3": 347.5565500000001, "coord_origin": "TOPLEFT" }, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 366.89017, "r_x1": 251.73131, "r_y1": 366.89017, "r_x2": 251.73131, "r_y2": 358.51553, "r_x3": 53.79800000000001, "r_y3": 358.51553, "coord_origin": "TOPLEFT" }, "text": "documents or photographs showing \u201ctext in the wild\".", "orig": "documents or photographs showing \u201ctext in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"." }, { "label": "text", "id": 2, "page_no": 2, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT" }, "confidence": 0.987700879573822, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 377.84918, "r_x1": 295.55945, "r_y1": 377.84918, "r_x2": 295.55945, "r_y2": 369.47455, "r_x3": 63.76100199999999, "r_y3": 369.47455, "coord_origin": "TOPLEFT" }, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 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Each document cate-", "orig": ". Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.72617, "r_x1": 294.04535, "r_y1": 410.72617, "r_x2": 294.04535, "r_y2": 402.3515300000001, "r_x3": 53.79800000000001, "r_y3": 402.3515300000001, "coord_origin": "TOPLEFT" }, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.68518000000006, "r_x1": 132.19516, "r_y1": 421.68518000000006, "r_x2": 132.19516, "r_y2": 413.31055, "r_x3": 53.79800000000001, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.528, "r_y0": 421.69415, "r_x1": 167.77272, "r_y1": 421.69415, "r_x2": 167.77272, "r_y2": 413.35541, "r_x3": 134.528, "r_y3": 413.35541, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 170.314, "r_y0": 421.68518000000006, "r_x1": 252.36031, "r_y1": 421.68518000000006, "r_x2": 252.36031, "r_y2": 413.31055, "r_x3": 170.314, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "format annual reports", "orig": "format annual reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 252.356, "r_y0": 418.03555, "r_x1": 255.73781, "r_y1": 418.03555, "r_x2": 255.73781, "r_y2": 411.24283, "r_x3": 252.356, "r_y3": 411.24283, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 258.56601, "r_y0": 421.68518000000006, "r_x1": 295.56046, "r_y1": 421.68518000000006, "r_x2": 295.56046, "r_y2": 413.31055, "r_x3": 258.56601, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.6441699999999, "r_x1": 294.04376, "r_y1": 432.6441699999999, "r_x2": 294.04376, "r_y2": 424.26953, "r_x3": 53.79800000000001, "r_y3": 424.26953, "coord_origin": "TOPLEFT" }, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.60217, "r_x1": 197.59023, "r_y1": 443.60217, "r_x2": 197.59023, "r_y2": 435.22754000000003, "r_x3": 53.79800000000001, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 197.591, "r_y0": 443.61115, "r_x1": 258.02774, "r_y1": 443.61115, "r_x2": 258.02774, "r_y2": 435.27240000000006, "r_x3": 197.591, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 260.48901, "r_y0": 443.60217, "r_x1": 273.78104, "r_y1": 443.60217, "r_x2": 273.78104, "r_y2": 435.22754000000003, "r_x3": 260.48901, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 276.03201, "r_y0": 443.61115, "r_x1": 294.94113, "r_y1": 443.61115, "r_x2": 294.94113, "r_y2": 435.27240000000006, "r_x3": 276.03201, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 454.57013, "r_x1": 68.085777, "r_y1": 454.57013, "r_x2": 68.085777, "r_y2": 446.23138, "r_x3": 53.79800000000001, "r_y3": 446.23138, "coord_origin": "TOPLEFT" }, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 68.296997, "r_y0": 454.56116, "r_x1": 294.04565, "r_y1": 454.56116, "r_x2": 294.04565, "r_y2": 446.18652, "r_x3": 68.296997, "r_y3": 446.18652, "coord_origin": "TOPLEFT" }, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.52017, "r_x1": 294.04163, "r_y1": 465.52017, "r_x2": 294.04163, "r_y2": 457.14554, "r_x3": 53.79800000000001, "r_y3": 457.14554, "coord_origin": "TOPLEFT" }, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 476.47919, "r_x1": 294.04889, "r_y1": 476.47919, "r_x2": 294.04889, "r_y2": 468.10455, "r_x3": 53.57400100000001, "r_y3": 468.10455, "coord_origin": "TOPLEFT" }, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 487.43817, "r_x1": 294.04715, "r_y1": 487.43817, "r_x2": 294.04715, "r_y2": 479.06354, "r_x3": 53.79800000000001, "r_y3": 479.06354, "coord_origin": "TOPLEFT" }, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 498.39719, "r_x1": 294.21643, "r_y1": 498.39719, "r_x2": 294.21643, "r_y2": 490.02255, "r_x3": 53.79800000000001, "r_y3": 490.02255, "coord_origin": "TOPLEFT" }, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 509.35617, "r_x1": 112.2948, "r_y1": 509.35617, "r_x2": 112.2948, "r_y2": 500.98154, "r_x3": 53.79800000000001, "r_y3": 500.98154, "coord_origin": "TOPLEFT" }, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes." }, { "label": "text", "id": 1, "page_no": 2, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT" }, "confidence": 0.9877581000328064, "cells": [ { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 520.31519, "r_x1": 295.55954, "r_y1": 520.31519, "r_x2": 295.55954, "r_y2": 511.94055, "r_x3": 63.76100199999999, "r_y3": 511.94055, "coord_origin": "TOPLEFT" }, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 531.27417, "r_x1": 294.04718, "r_y1": 531.27417, "r_x2": 294.04718, "r_y2": 522.89954, "r_x3": 53.79800000000001, "r_y3": 522.89954, "coord_origin": "TOPLEFT" }, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 542.23317, "r_x1": 295.56155, "r_y1": 542.23317, "r_x2": 295.56155, "r_y2": 533.8585499999999, "r_x3": 53.528999, "r_y3": 533.8585499999999, "coord_origin": "TOPLEFT" }, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 553.19217, "r_x1": 294.04144, "r_y1": 553.19217, "r_x2": 294.04144, "r_y2": 544.81755, "r_x3": 53.79800000000001, "r_y3": 544.81755, "coord_origin": "TOPLEFT" }, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 564.15018, "r_x1": 294.04709, "r_y1": 564.15018, "r_x2": 294.04709, "r_y2": 555.77556, "r_x3": 53.79800000000001, "r_y3": 555.77556, "coord_origin": "TOPLEFT" }, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 575.10918, "r_x1": 294.04163, "r_y1": 575.10918, "r_x2": 294.04163, "r_y2": 566.73456, "r_x3": 53.79800000000001, "r_y3": 566.73456, "coord_origin": "TOPLEFT" }, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 586.06818, "r_x1": 295.5567, "r_y1": 586.06818, "r_x2": 295.5567, "r_y2": 577.6935599999999, "r_x3": 53.79800000000001, "r_y3": 577.6935599999999, "coord_origin": "TOPLEFT" }, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 597.02718, "r_x1": 294.04541, "r_y1": 597.02718, "r_x2": 294.04541, "r_y2": 588.65256, "r_x3": 53.79800000000001, "r_y3": 588.65256, "coord_origin": "TOPLEFT" }, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 607.98618, "r_x1": 164.39928, "r_y1": 607.98618, "r_x2": 164.39928, "r_y2": 599.61156, "r_x3": 53.466999, "r_y3": 599.61156, "coord_origin": "TOPLEFT" }, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features." }, { "label": "text", "id": 0, "page_no": 2, "cluster": { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT" }, "confidence": 0.9880735278129578, "cells": [ { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 618.9451799999999, "r_x1": 295.56396, "r_y1": 618.9451799999999, "r_x2": 295.56396, "r_y2": 610.57056, "r_x3": 63.76100199999999, "r_y3": 610.57056, "coord_origin": "TOPLEFT" }, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 629.90417, "r_x1": 294.04532, "r_y1": 629.90417, "r_x2": 294.04532, "r_y2": 621.52956, "r_x3": 53.79800000000001, "r_y3": 621.52956, "coord_origin": "TOPLEFT" }, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 640.86317, "r_x1": 294.04538, "r_y1": 640.86317, "r_x2": 294.04538, "r_y2": 632.48856, "r_x3": 53.79800000000001, "r_y3": 632.48856, "coord_origin": "TOPLEFT" }, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 651.82217, "r_x1": 294.04315, "r_y1": 651.82217, "r_x2": 294.04315, "r_y2": 643.4475600000001, "r_x3": 53.79800000000001, "r_y3": 643.4475600000001, "coord_origin": "TOPLEFT" }, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 662.78117, "r_x1": 294.04712, "r_y1": 662.78117, "r_x2": 294.04712, "r_y2": 654.40656, "r_x3": 53.79800000000001, "r_y3": 654.40656, "coord_origin": "TOPLEFT" }, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 673.7391700000001, "r_x1": 294.04333, "r_y1": 673.7391700000001, "r_x2": 294.04333, "r_y2": 665.36456, "r_x3": 53.79800000000001, "r_y3": 665.36456, "coord_origin": "TOPLEFT" }, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 684.69817, "r_x1": 98.916931, "r_y1": 684.69817, "r_x2": 98.916931, "r_y2": 676.32355, "r_x3": 53.79800000000001, "r_y3": 676.32355, "coord_origin": "TOPLEFT" }, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions." }, { "label": "footnote", "id": 12, "page_no": 2, "cluster": { "id": 12, "label": "footnote", "bbox": { "l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT" }, "confidence": 0.9048352241516113, "cells": [ { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.169891, "r_x1": 195.78998, "r_y1": 708.169891, "r_x2": 195.78998, "r_y2": 701.6563639999999, "r_x3": 53.79800000000001, "r_y3": 701.6563639999999, "coord_origin": "TOPLEFT" }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/" }, { "label": "text", "id": 3, "page_no": 2, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.986920952796936, "cells": [ { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 95.73816, "r_x1": 558.20093, "r_y1": 95.73816, "r_x2": 558.20093, "r_y2": 87.36352999999997, "r_x3": 327.918, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.69713999999999, "r_x1": 558.20013, "r_y1": 106.69713999999999, "r_x2": 558.20013, "r_y2": 98.32250999999997, "r_x3": 317.95499, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.65618999999992, "r_x1": 558.20056, "r_y1": 117.65618999999992, "r_x2": 558.20056, "r_y2": 109.28156000000001, "r_x3": 317.95499, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 128.61517000000003, "r_x1": 559.19183, "r_y1": 128.61517000000003, "r_x2": 559.19183, "r_y2": 120.24054000000001, "r_x3": 317.95499, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 139.57317999999998, "r_x1": 558.20349, "r_y1": 139.57317999999998, "r_x2": 558.20349, "r_y2": 131.19854999999995, "r_x3": 317.62299, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 150.53216999999995, "r_x1": 558.52936, "r_y1": 150.53216999999995, "r_x2": 558.52936, "r_y2": 142.15752999999995, "r_x3": 317.95499, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 161.49114999999995, "r_x1": 461.6416, "r_y1": 161.49114999999995, "r_x2": 461.6416, "r_y2": 153.11652000000004, "r_x3": 317.95499, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5." }, { "label": "text", "id": 6, "page_no": 2, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT" }, "confidence": 0.9860605001449585, "cells": [ { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 172.4502, "r_x1": 558.43811, "r_y1": 172.4502, "r_x2": 558.43811, "r_y2": 164.07556, "r_x3": 327.918, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 183.40918, "r_x1": 516.8219, "r_y1": 183.40918, "r_x2": 516.8219, "r_y2": 175.03454999999997, "r_x3": 317.95499, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 519.07501, "r_y0": 183.41814999999997, "r_x1": 558.20135, "r_y1": 183.41814999999997, "r_x2": 558.20135, "r_y2": 175.07941000000005, "r_x3": 519.07501, "r_y3": 175.07941000000005, "coord_origin": "TOPLEFT" }, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 194.36816, "r_x1": 558.20325, "r_y1": 194.36816, "r_x2": 558.20325, "r_y2": 185.99352999999996, "r_x3": 317.95499, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 205.32714999999996, "r_x1": 558.20404, "r_y1": 205.32714999999996, "r_x2": 558.20404, "r_y2": 196.95250999999996, "r_x3": 317.95499, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "file (in JSON format) with the associated page images (in PNG", "orig": "file (in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 216.28619000000003, "r_x1": 364.28769, "r_y1": 216.28619000000003, "r_x2": 364.28769, "r_y2": 207.91156, "r_x3": 317.95499, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 364.28699, "r_y0": 215.55993999999998, "r_x1": 369.98962, "r_y1": 215.55993999999998, "r_x2": 369.98962, "r_y2": 207.85779000000002, "r_x3": 364.28699, "r_y3": 207.85779000000002, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 369.98999, "r_y0": 216.28619000000003, "r_x1": 558.20526, "r_y1": 216.28619000000003, "r_x2": 558.20526, "r_y2": 207.91156, "r_x3": 369.98999, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 227.24518, "r_x1": 558.20251, "r_y1": 227.24518, "r_x2": 558.20251, "r_y2": 218.87054, "r_x3": 317.95499, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 238.20416, "r_x1": 558.203, "r_y1": 238.20416, "r_x2": 558.203, "r_y2": 229.82952999999998, "r_x3": 317.95499, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 249.16216999999995, "r_x1": 558.20404, "r_y1": 249.16216999999995, "r_x2": 558.20404, "r_y2": 240.78754000000004, "r_x3": 317.95499, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 260.12114999999994, "r_x1": 558.20227, "r_y1": 260.12114999999994, "r_x2": 558.20227, "r_y2": 251.74652000000003, "r_x3": 317.95499, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 271.0802, "r_x1": 550.36414, "r_y1": 271.0802, "r_x2": 550.36414, "r_y2": 262.70556999999997, "r_x3": 317.95499, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames." }, { "label": "text", "id": 7, "page_no": 2, "cluster": { "id": 7, "label": "text", "bbox": { "l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT" }, "confidence": 0.9853296875953674, "cells": [ { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 282.03918, "r_x1": 559.18488, "r_y1": 282.03918, "r_x2": 559.18488, "r_y2": 273.66454999999996, "r_x3": 327.918, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 292.99817, "r_x1": 559.7132, "r_y1": 292.99817, "r_x2": 559.7132, "r_y2": 284.62354, "r_x3": 317.95499, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 303.95717999999994, "r_x1": 558.20416, "r_y1": 303.95717999999994, "r_x2": 558.20416, "r_y2": 295.5825500000001, "r_x3": 317.95499, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 314.91617, "r_x1": 559.71326, "r_y1": 314.91617, "r_x2": 559.71326, "r_y2": 306.54153, "r_x3": 317.95499, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 325.87518, "r_x1": 558.41443, "r_y1": 325.87518, "r_x2": 558.41443, "r_y2": 317.50055, "r_x3": 317.95499, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 336.83417, "r_x1": 559.71545, "r_y1": 336.83417, "r_x2": 559.71545, "r_y2": 328.4595299999999, "r_x3": 317.95499, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 347.79318, "r_x1": 558.36865, "r_y1": 347.79318, "r_x2": 558.36865, "r_y2": 339.41855000000004, "r_x3": 317.95499, "r_y3": 339.41855000000004, "coord_origin": "TOPLEFT" }, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 358.75217, "r_x1": 558.20062, "r_y1": 358.75217, "r_x2": 558.20062, "r_y2": 350.37753, "r_x3": 317.95499, "r_y3": 350.37753, "coord_origin": "TOPLEFT" }, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 369.71017, "r_x1": 558.20184, "r_y1": 369.71017, "r_x2": 558.20184, "r_y2": 361.33554, "r_x3": 317.95499, "r_y3": 361.33554, "coord_origin": "TOPLEFT" }, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 380.66916, "r_x1": 559.71442, "r_y1": 380.66916, "r_x2": 559.71442, "r_y2": 372.29453, "r_x3": 317.95499, "r_y3": 372.29453, "coord_origin": "TOPLEFT" }, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 391.62817, "r_x1": 559.1864, "r_y1": 391.62817, "r_x2": 559.1864, "r_y2": 383.25354, "r_x3": 317.95499, "r_y3": 383.25354, "coord_origin": "TOPLEFT" }, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 316.94199, "r_y0": 402.58716, "r_x1": 558.20111, "r_y1": 402.58716, "r_x2": 558.20111, "r_y2": 394.21252, "r_x3": 316.94199, "r_y3": 394.21252, "coord_origin": "TOPLEFT" }, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "orig": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 413.54617, "r_x1": 559.7132, "r_y1": 413.54617, "r_x2": 559.7132, "r_y2": 405.17154, "r_x3": 317.95499, "r_y3": 405.17154, "coord_origin": "TOPLEFT" }, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 424.50515999999993, "r_x1": 558.20013, "r_y1": 424.50515999999993, "r_x2": 558.20013, "r_y2": 416.13052, "r_x3": 317.95499, "r_y3": 416.13052, "coord_origin": "TOPLEFT" }, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 435.46417, "r_x1": 464.50613, "r_y1": 435.46417, "r_x2": 464.50613, "r_y2": 427.08953999999994, "r_x3": 317.95499, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.98199000000005, "r_y0": 435.47313999999994, "r_x1": 482.14560000000006, "r_y1": 435.47313999999994, "r_x2": 482.14560000000006, "r_y2": 427.13439999999997, "r_x3": 466.98199000000005, "r_y3": 427.13439999999997, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 485.13199000000003, "r_y0": 435.46417, "r_x1": 558.19727, "r_y1": 435.46417, "r_x2": 558.19727, "r_y2": 427.08953999999994, "r_x3": 485.13199000000003, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 446.4231899999999, "r_x1": 558.20221, "r_y1": 446.4231899999999, "r_x2": 558.20221, "r_y2": 438.04855, "r_x3": 317.95499, "r_y3": 438.04855, "coord_origin": "TOPLEFT" }, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 457.38217, "r_x1": 558.51501, "r_y1": 457.38217, "r_x2": 558.51501, "r_y2": 449.00754, "r_x3": 317.95499, "r_y3": 449.00754, "coord_origin": "TOPLEFT" }, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "orig": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 468.34119, "r_x1": 558.19855, "r_y1": 468.34119, "r_x2": 558.19855, "r_y2": 459.96655000000004, "r_x3": 317.95499, "r_y3": 459.96655000000004, "coord_origin": "TOPLEFT" }, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 479.29916, "r_x1": 559.58215, "r_y1": 479.29916, "r_x2": 559.58215, "r_y2": 470.92453, "r_x3": 317.95499, "r_y3": 470.92453, "coord_origin": "TOPLEFT" }, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "orig": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 490.25818, "r_x1": 558.41559, "r_y1": 490.25818, "r_x2": 558.41559, "r_y2": 481.88354, "r_x3": 317.64099, "r_y3": 481.88354, "coord_origin": "TOPLEFT" }, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 501.21716, "r_x1": 559.72156, "r_y1": 501.21716, "r_x2": 559.72156, "r_y2": 492.84253, "r_x3": 317.95499, "r_y3": 492.84253, "coord_origin": "TOPLEFT" }, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 512.1761799999999, "r_x1": 558.19928, "r_y1": 512.1761799999999, "r_x2": 558.19928, "r_y2": 503.80154, "r_x3": 317.95499, "r_y3": 503.80154, "coord_origin": "TOPLEFT" }, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 523.13516, "r_x1": 558.20581, "r_y1": 523.13516, "r_x2": 558.20581, "r_y2": 514.76053, "r_x3": 317.95499, "r_y3": 514.76053, "coord_origin": "TOPLEFT" }, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 534.09418, "r_x1": 559.71729, "r_y1": 534.09418, "r_x2": 559.71729, "r_y2": 525.71954, "r_x3": 317.95499, "r_y3": 525.71954, "coord_origin": "TOPLEFT" }, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 545.05318, "r_x1": 558.20404, "r_y1": 545.05318, "r_x2": 558.20404, "r_y2": 536.6785600000001, "r_x3": 317.95499, "r_y3": 536.6785600000001, "coord_origin": "TOPLEFT" }, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 556.01218, "r_x1": 558.20074, "r_y1": 556.01218, "r_x2": 558.20074, "r_y2": 547.63756, "r_x3": 317.95499, "r_y3": 547.63756, "coord_origin": "TOPLEFT" }, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.97118, "r_x1": 558.20062, "r_y1": 566.97118, "r_x2": 558.20062, "r_y2": 558.59656, "r_x3": 317.95499, "r_y3": 558.59656, "coord_origin": "TOPLEFT" }, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.9301800000001, "r_x1": 558.20416, "r_y1": 577.9301800000001, "r_x2": 558.20416, "r_y2": 569.55556, "r_x3": 317.95499, "r_y3": 569.55556, "coord_origin": "TOPLEFT" }, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 588.88918, "r_x1": 457.62469, "r_y1": 588.88918, "r_x2": 457.62469, "r_y2": 580.51456, "r_x3": 317.95499, "r_y3": 580.51456, "coord_origin": "TOPLEFT" }, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4." }, { "label": "section_header", "id": 10, "page_no": 2, "cluster": { "id": 10, "label": "section_header", "bbox": { "l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT" }, "confidence": 0.9422592520713806, "cells": [ { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 617.15901, "r_x1": 323.56226, "r_y1": 617.15901, "r_x2": 323.56226, "r_y2": 606.84991, "r_x3": 317.95499, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47134, "r_y0": 617.15901, "r_x1": 470.21326, "r_y1": 617.15901, "r_x2": 470.21326, "r_y2": 606.84991, "r_x3": 334.47134, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "4 ANNOTATION CAMPAIGN" }, { "label": "text", "id": 5, "page_no": 2, "cluster": { "id": 5, "label": "text", "bbox": { "l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT" }, "confidence": 0.9863831996917725, "cells": [ { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 640.34818, "r_x1": 558.20148, "r_y1": 640.34818, "r_x2": 558.20148, "r_y2": 631.97356, "r_x3": 317.686, "r_y3": 631.97356, "coord_origin": "TOPLEFT" }, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.30717, "r_x1": 559.58521, "r_y1": 651.30717, "r_x2": 559.58521, "r_y2": 642.93256, "r_x3": 317.95499, "r_y3": 642.93256, "coord_origin": "TOPLEFT" }, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.26617, "r_x1": 558.20007, "r_y1": 662.26617, "r_x2": 558.20007, "r_y2": 653.89156, "r_x3": 317.95499, "r_y3": 653.89156, "coord_origin": "TOPLEFT" }, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.22517, "r_x1": 559.71375, "r_y1": 673.22517, "r_x2": 559.71375, "r_y2": 664.85056, "r_x3": 317.95499, "r_y3": 664.85056, "coord_origin": "TOPLEFT" }, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.18417, "r_x1": 558.20233, "r_y1": 684.18417, "r_x2": 558.20233, "r_y2": 675.80956, "r_x3": 317.95499, "r_y3": 675.80956, "coord_origin": "TOPLEFT" }, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 695.143173, "r_x1": 559.71381, "r_y1": 695.143173, "r_x2": 559.71381, "r_y2": 686.76855, "r_x3": 317.95499, "r_y3": 686.76855, "coord_origin": "TOPLEFT" }, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 706.102173, "r_x1": 559.1864, "r_y1": 706.102173, "r_x2": 559.1864, "r_y2": 697.7275539999999, "r_x3": 317.95499, "r_y3": 697.7275539999999, "coord_origin": "TOPLEFT" }, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. 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"coord_origin": "TOPLEFT" }, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 301.13718, "r_x1": 294.04868, "r_y1": 301.13718, "r_x2": 294.04868, "r_y2": 292.76254, "r_x3": 53.79800000000001, "r_y3": 292.76254, "coord_origin": "TOPLEFT" }, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 312.09616, "r_x1": 98.881348, "r_y1": 312.09616, "r_x2": 98.881348, "r_y2": 303.72153, "r_x3": 53.79800000000001, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 99.070999, "r_y0": 310.08768, "r_x1": 104.77363, "r_y1": 310.08768, "r_x2": 104.77363, "r_y2": 306.07971, "r_x3": 99.070999, "r_y3": 306.07971, "coord_origin": "TOPLEFT" }, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 107.46399999999998, "r_y0": 312.09616, "r_x1": 115.83373999999999, "r_y1": 312.09616, "r_x2": 115.83373999999999, "r_y2": 303.72153, "r_x3": 107.46399999999998, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 118.08366, "r_y0": 312.09616, "r_x1": 294.26233, "r_y1": 312.09616, "r_x2": 294.26233, "r_y2": 303.72153, "r_x3": 118.08366, "r_y3": 303.72153, "coord_origin": "TOPLEFT" }, "text": "pages) with technical content, dense in complex", "orig": "pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 323.05518, "r_x1": 294.04715, "r_y1": 323.05518, "r_x2": 294.04715, "r_y2": 314.68054, "r_x3": 53.79800000000001, "r_y3": 314.68054, "coord_origin": "TOPLEFT" }, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 334.01318, "r_x1": 294.27383, "r_y1": 334.01318, "r_x2": 294.27383, "r_y2": 325.63855, "r_x3": 53.79800000000001, "r_y3": 325.63855, "coord_origin": "TOPLEFT" }, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 344.97217, "r_x1": 294.0416, "r_y1": 344.97217, "r_x2": 294.0416, "r_y2": 336.59753, "r_x3": 53.79800000000001, "r_y3": 336.59753, "coord_origin": "TOPLEFT" }, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 355.9311799999999, "r_x1": 294.04712, "r_y1": 355.9311799999999, "r_x2": 294.04712, "r_y2": 347.5565500000001, "r_x3": 53.79800000000001, "r_y3": 347.5565500000001, "coord_origin": "TOPLEFT" }, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 366.89017, "r_x1": 251.73131, "r_y1": 366.89017, "r_x2": 251.73131, "r_y2": 358.51553, "r_x3": 53.79800000000001, "r_y3": 358.51553, "coord_origin": "TOPLEFT" }, "text": "documents or photographs showing \u201ctext in the wild\".", "orig": "documents or photographs showing \u201ctext in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"." }, { "label": "text", "id": 2, "page_no": 2, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT" }, "confidence": 0.987700879573822, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 377.84918, "r_x1": 295.55945, "r_y1": 377.84918, "r_x2": 295.55945, "r_y2": 369.47455, "r_x3": 63.76100199999999, "r_y3": 369.47455, "coord_origin": "TOPLEFT" }, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 388.80816999999996, "r_x1": 105.90533, "r_y1": 388.80816999999996, "r_x2": 105.90533, "r_y2": 380.43353, "r_x3": 53.79800000000001, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": "gories, namely", "orig": "gories, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 107.754, "r_y0": 388.81714, "r_x1": 167.4973, "r_y1": 388.81714, "r_x2": 167.4973, "r_y2": 380.47838999999993, "r_x3": 107.754, "r_y3": 380.47838999999993, "coord_origin": "TOPLEFT" }, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 167.496, "r_y0": 388.80816999999996, "r_x1": 169.42915, "r_y1": 388.80816999999996, "r_x2": 169.42915, "r_y2": 380.43353, "r_x3": 167.496, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 171.28101, "r_y0": 388.81714, "r_x1": 201.45581, "r_y1": 388.81714, "r_x2": 201.45581, "r_y2": 380.47838999999993, "r_x3": 171.28101, "r_y3": 380.47838999999993, "coord_origin": "TOPLEFT" }, "text": "Manuals", "orig": "Manuals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 201.455, "r_y0": 388.80816999999996, "r_x1": 203.38815, "r_y1": 388.80816999999996, "r_x2": 203.38815, "r_y2": 380.43353, "r_x3": 201.455, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 205.24001, "r_y0": 388.81714, "r_x1": 264.55273, "r_y1": 388.81714, "r_x2": 264.55273, "r_y2": 380.47838999999993, "r_x3": 205.24001, "r_y3": 380.47838999999993, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 264.54901, "r_y0": 388.80816999999996, "r_x1": 266.48218, "r_y1": 388.80816999999996, "r_x2": 266.48218, "r_y2": 380.43353, "r_x3": 264.54901, "r_y3": 380.43353, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 268.33401, "r_y0": 388.81714, "r_x1": 294.36133, "r_y1": 388.81714, "r_x2": 294.36133, "r_y2": 380.47838999999993, "r_x3": 268.33401, "r_y3": 380.47838999999993, "coord_origin": "TOPLEFT" }, "text": "Laws &", "orig": "Laws &", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 399.77615, "r_x1": 94.899666, "r_y1": 399.77615, "r_x2": 94.899666, "r_y2": 391.43741000000006, "r_x3": 53.79800000000001, "r_y3": 391.43741000000006, "coord_origin": "TOPLEFT" }, "text": "Regulations", "orig": "Regulations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 94.900002, "r_y0": 399.76718, "r_x1": 96.862747, "r_y1": 399.76718, "r_x2": 96.862747, "r_y2": 391.39255, "r_x3": 94.900002, "r_y3": 391.39255, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 99.109001, "r_y0": 399.77615, "r_x1": 124.72282, "r_y1": 399.77615, "r_x2": 124.72282, "r_y2": 391.43741000000006, "r_x3": 99.109001, "r_y3": 391.43741000000006, "coord_origin": "TOPLEFT" }, "text": "Patents", "orig": "Patents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 127.17899999999999, "r_y0": 399.76718, "r_x1": 140.60596, "r_y1": 399.76718, "r_x2": 140.60596, "r_y2": 391.39255, "r_x3": 127.17899999999999, "r_y3": 391.39255, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.853, "r_y0": 399.77615, "r_x1": 215.15340000000003, "r_y1": 399.77615, "r_x2": 215.15340000000003, "r_y2": 391.43741000000006, "r_x3": 142.853, "r_y3": 391.43741000000006, "coord_origin": "TOPLEFT" }, "text": "Government Tenders", "orig": "Government Tenders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 215.15601000000004, "r_y0": 399.76718, "r_x1": 295.55716, "r_y1": 399.76718, "r_x2": 295.55716, "r_y2": 391.39255, "r_x3": 215.15601000000004, "r_y3": 391.39255, "coord_origin": "TOPLEFT" }, "text": ". Each document cate-", "orig": ". Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.72617, "r_x1": 294.04535, "r_y1": 410.72617, "r_x2": 294.04535, "r_y2": 402.3515300000001, "r_x3": 53.79800000000001, "r_y3": 402.3515300000001, "coord_origin": "TOPLEFT" }, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.68518000000006, "r_x1": 132.19516, "r_y1": 421.68518000000006, "r_x2": 132.19516, "r_y2": 413.31055, "r_x3": 53.79800000000001, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.528, "r_y0": 421.69415, "r_x1": 167.77272, "r_y1": 421.69415, "r_x2": 167.77272, "r_y2": 413.35541, "r_x3": 134.528, "r_y3": 413.35541, "coord_origin": "TOPLEFT" }, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 170.314, "r_y0": 421.68518000000006, "r_x1": 252.36031, "r_y1": 421.68518000000006, "r_x2": 252.36031, "r_y2": 413.31055, "r_x3": 170.314, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "format annual reports", "orig": "format annual reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 252.356, "r_y0": 418.03555, "r_x1": 255.73781, "r_y1": 418.03555, "r_x2": 255.73781, "r_y2": 411.24283, "r_x3": 252.356, "r_y3": 411.24283, "coord_origin": "TOPLEFT" }, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 258.56601, "r_y0": 421.68518000000006, "r_x1": 295.56046, "r_y1": 421.68518000000006, "r_x2": 295.56046, "r_y2": 413.31055, "r_x3": 258.56601, "r_y3": 413.31055, "coord_origin": "TOPLEFT" }, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.6441699999999, "r_x1": 294.04376, "r_y1": 432.6441699999999, "r_x2": 294.04376, "r_y2": 424.26953, "r_x3": 53.79800000000001, "r_y3": 424.26953, "coord_origin": "TOPLEFT" }, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.60217, "r_x1": 197.59023, "r_y1": 443.60217, "r_x2": 197.59023, "r_y2": 435.22754000000003, "r_x3": 53.79800000000001, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 197.591, "r_y0": 443.61115, "r_x1": 258.02774, "r_y1": 443.61115, "r_x2": 258.02774, "r_y2": 435.27240000000006, "r_x3": 197.591, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 260.48901, "r_y0": 443.60217, "r_x1": 273.78104, "r_y1": 443.60217, "r_x2": 273.78104, "r_y2": 435.22754000000003, "r_x3": 260.48901, "r_y3": 435.22754000000003, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 276.03201, "r_y0": 443.61115, "r_x1": 294.94113, "r_y1": 443.61115, "r_x2": 294.94113, "r_y2": 435.27240000000006, "r_x3": 276.03201, "r_y3": 435.27240000000006, "coord_origin": "TOPLEFT" }, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 454.57013, "r_x1": 68.085777, "r_y1": 454.57013, "r_x2": 68.085777, "r_y2": 446.23138, "r_x3": 53.79800000000001, "r_y3": 446.23138, "coord_origin": "TOPLEFT" }, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 68.296997, "r_y0": 454.56116, "r_x1": 294.04565, "r_y1": 454.56116, "r_x2": 294.04565, "r_y2": 446.18652, "r_x3": 68.296997, "r_y3": 446.18652, "coord_origin": "TOPLEFT" }, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.52017, "r_x1": 294.04163, "r_y1": 465.52017, "r_x2": 294.04163, "r_y2": 457.14554, "r_x3": 53.79800000000001, "r_y3": 457.14554, "coord_origin": "TOPLEFT" }, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.57400100000001, "r_y0": 476.47919, "r_x1": 294.04889, "r_y1": 476.47919, "r_x2": 294.04889, "r_y2": 468.10455, "r_x3": 53.57400100000001, "r_y3": 468.10455, "coord_origin": "TOPLEFT" }, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 487.43817, "r_x1": 294.04715, "r_y1": 487.43817, "r_x2": 294.04715, "r_y2": 479.06354, "r_x3": 53.79800000000001, "r_y3": 479.06354, "coord_origin": "TOPLEFT" }, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 498.39719, "r_x1": 294.21643, "r_y1": 498.39719, "r_x2": 294.21643, "r_y2": 490.02255, "r_x3": 53.79800000000001, "r_y3": 490.02255, "coord_origin": "TOPLEFT" }, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 509.35617, "r_x1": 112.2948, "r_y1": 509.35617, "r_x2": 112.2948, "r_y2": 500.98154, "r_x3": 53.79800000000001, "r_y3": 500.98154, "coord_origin": "TOPLEFT" }, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes." }, { "label": "text", "id": 1, "page_no": 2, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT" }, "confidence": 0.9877581000328064, "cells": [ { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 520.31519, "r_x1": 295.55954, "r_y1": 520.31519, "r_x2": 295.55954, "r_y2": 511.94055, "r_x3": 63.76100199999999, "r_y3": 511.94055, "coord_origin": "TOPLEFT" }, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 531.27417, "r_x1": 294.04718, "r_y1": 531.27417, "r_x2": 294.04718, "r_y2": 522.89954, "r_x3": 53.79800000000001, "r_y3": 522.89954, "coord_origin": "TOPLEFT" }, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 542.23317, "r_x1": 295.56155, "r_y1": 542.23317, "r_x2": 295.56155, "r_y2": 533.8585499999999, "r_x3": 53.528999, "r_y3": 533.8585499999999, "coord_origin": "TOPLEFT" }, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 553.19217, "r_x1": 294.04144, "r_y1": 553.19217, "r_x2": 294.04144, "r_y2": 544.81755, "r_x3": 53.79800000000001, "r_y3": 544.81755, "coord_origin": "TOPLEFT" }, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 564.15018, "r_x1": 294.04709, "r_y1": 564.15018, "r_x2": 294.04709, "r_y2": 555.77556, "r_x3": 53.79800000000001, "r_y3": 555.77556, "coord_origin": "TOPLEFT" }, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 575.10918, "r_x1": 294.04163, "r_y1": 575.10918, "r_x2": 294.04163, "r_y2": 566.73456, "r_x3": 53.79800000000001, "r_y3": 566.73456, "coord_origin": "TOPLEFT" }, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 586.06818, "r_x1": 295.5567, "r_y1": 586.06818, "r_x2": 295.5567, "r_y2": 577.6935599999999, "r_x3": 53.79800000000001, "r_y3": 577.6935599999999, "coord_origin": "TOPLEFT" }, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 597.02718, "r_x1": 294.04541, "r_y1": 597.02718, "r_x2": 294.04541, "r_y2": 588.65256, "r_x3": 53.79800000000001, "r_y3": 588.65256, "coord_origin": "TOPLEFT" }, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 607.98618, "r_x1": 164.39928, "r_y1": 607.98618, "r_x2": 164.39928, "r_y2": 599.61156, "r_x3": 53.466999, "r_y3": 599.61156, "coord_origin": "TOPLEFT" }, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features." }, { "label": "text", "id": 0, "page_no": 2, "cluster": { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT" }, "confidence": 0.9880735278129578, "cells": [ { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 618.9451799999999, "r_x1": 295.56396, "r_y1": 618.9451799999999, "r_x2": 295.56396, "r_y2": 610.57056, "r_x3": 63.76100199999999, "r_y3": 610.57056, "coord_origin": "TOPLEFT" }, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 629.90417, "r_x1": 294.04532, "r_y1": 629.90417, "r_x2": 294.04532, "r_y2": 621.52956, "r_x3": 53.79800000000001, "r_y3": 621.52956, "coord_origin": "TOPLEFT" }, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 640.86317, "r_x1": 294.04538, "r_y1": 640.86317, "r_x2": 294.04538, "r_y2": 632.48856, "r_x3": 53.79800000000001, "r_y3": 632.48856, "coord_origin": "TOPLEFT" }, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 651.82217, "r_x1": 294.04315, "r_y1": 651.82217, "r_x2": 294.04315, "r_y2": 643.4475600000001, "r_x3": 53.79800000000001, "r_y3": 643.4475600000001, "coord_origin": "TOPLEFT" }, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 662.78117, "r_x1": 294.04712, "r_y1": 662.78117, "r_x2": 294.04712, "r_y2": 654.40656, "r_x3": 53.79800000000001, "r_y3": 654.40656, "coord_origin": "TOPLEFT" }, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 673.7391700000001, "r_x1": 294.04333, "r_y1": 673.7391700000001, "r_x2": 294.04333, "r_y2": 665.36456, "r_x3": 53.79800000000001, "r_y3": 665.36456, "coord_origin": "TOPLEFT" }, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 684.69817, "r_x1": 98.916931, "r_y1": 684.69817, "r_x2": 98.916931, "r_y2": 676.32355, "r_x3": 53.79800000000001, "r_y3": 676.32355, "coord_origin": "TOPLEFT" }, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions." }, { "label": "footnote", "id": 12, "page_no": 2, "cluster": { "id": 12, "label": "footnote", "bbox": { "l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT" }, "confidence": 0.9048352241516113, "cells": [ { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.169891, "r_x1": 195.78998, "r_y1": 708.169891, "r_x2": 195.78998, "r_y2": 701.6563639999999, "r_x3": 53.79800000000001, "r_y3": 701.6563639999999, "coord_origin": "TOPLEFT" }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/" }, { "label": "text", "id": 3, "page_no": 2, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.986920952796936, "cells": [ { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 95.73816, "r_x1": 558.20093, "r_y1": 95.73816, "r_x2": 558.20093, "r_y2": 87.36352999999997, "r_x3": 327.918, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.69713999999999, "r_x1": 558.20013, "r_y1": 106.69713999999999, "r_x2": 558.20013, "r_y2": 98.32250999999997, "r_x3": 317.95499, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.65618999999992, "r_x1": 558.20056, "r_y1": 117.65618999999992, "r_x2": 558.20056, "r_y2": 109.28156000000001, "r_x3": 317.95499, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 128.61517000000003, "r_x1": 559.19183, "r_y1": 128.61517000000003, "r_x2": 559.19183, "r_y2": 120.24054000000001, "r_x3": 317.95499, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 139.57317999999998, "r_x1": 558.20349, "r_y1": 139.57317999999998, "r_x2": 558.20349, "r_y2": 131.19854999999995, "r_x3": 317.62299, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 150.53216999999995, "r_x1": 558.52936, "r_y1": 150.53216999999995, "r_x2": 558.52936, "r_y2": 142.15752999999995, "r_x3": 317.95499, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 161.49114999999995, "r_x1": 461.6416, "r_y1": 161.49114999999995, "r_x2": 461.6416, "r_y2": 153.11652000000004, "r_x3": 317.95499, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5." }, { "label": "text", "id": 6, "page_no": 2, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT" }, "confidence": 0.9860605001449585, "cells": [ { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 172.4502, "r_x1": 558.43811, "r_y1": 172.4502, "r_x2": 558.43811, "r_y2": 164.07556, "r_x3": 327.918, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 183.40918, "r_x1": 516.8219, "r_y1": 183.40918, "r_x2": 516.8219, "r_y2": 175.03454999999997, "r_x3": 317.95499, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 519.07501, "r_y0": 183.41814999999997, "r_x1": 558.20135, "r_y1": 183.41814999999997, "r_x2": 558.20135, "r_y2": 175.07941000000005, "r_x3": 519.07501, "r_y3": 175.07941000000005, "coord_origin": "TOPLEFT" }, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 194.36816, "r_x1": 558.20325, "r_y1": 194.36816, "r_x2": 558.20325, "r_y2": 185.99352999999996, "r_x3": 317.95499, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 205.32714999999996, "r_x1": 558.20404, "r_y1": 205.32714999999996, "r_x2": 558.20404, "r_y2": 196.95250999999996, "r_x3": 317.95499, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "file (in JSON format) with the associated page images (in PNG", "orig": "file (in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 216.28619000000003, "r_x1": 364.28769, "r_y1": 216.28619000000003, "r_x2": 364.28769, "r_y2": 207.91156, "r_x3": 317.95499, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 364.28699, "r_y0": 215.55993999999998, "r_x1": 369.98962, "r_y1": 215.55993999999998, "r_x2": 369.98962, "r_y2": 207.85779000000002, "r_x3": 364.28699, "r_y3": 207.85779000000002, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 369.98999, "r_y0": 216.28619000000003, "r_x1": 558.20526, "r_y1": 216.28619000000003, "r_x2": 558.20526, "r_y2": 207.91156, "r_x3": 369.98999, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 227.24518, "r_x1": 558.20251, "r_y1": 227.24518, "r_x2": 558.20251, "r_y2": 218.87054, "r_x3": 317.95499, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 238.20416, "r_x1": 558.203, "r_y1": 238.20416, "r_x2": 558.203, "r_y2": 229.82952999999998, "r_x3": 317.95499, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 249.16216999999995, "r_x1": 558.20404, "r_y1": 249.16216999999995, "r_x2": 558.20404, "r_y2": 240.78754000000004, "r_x3": 317.95499, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 260.12114999999994, "r_x1": 558.20227, "r_y1": 260.12114999999994, "r_x2": 558.20227, "r_y2": 251.74652000000003, "r_x3": 317.95499, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 271.0802, "r_x1": 550.36414, "r_y1": 271.0802, "r_x2": 550.36414, "r_y2": 262.70556999999997, "r_x3": 317.95499, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames." }, { "label": "text", "id": 7, "page_no": 2, "cluster": { "id": 7, "label": "text", "bbox": { "l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT" }, "confidence": 0.9853296875953674, "cells": [ { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 282.03918, "r_x1": 559.18488, "r_y1": 282.03918, "r_x2": 559.18488, "r_y2": 273.66454999999996, "r_x3": 327.918, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 292.99817, "r_x1": 559.7132, "r_y1": 292.99817, "r_x2": 559.7132, "r_y2": 284.62354, "r_x3": 317.95499, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 303.95717999999994, "r_x1": 558.20416, "r_y1": 303.95717999999994, "r_x2": 558.20416, "r_y2": 295.5825500000001, "r_x3": 317.95499, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 314.91617, "r_x1": 559.71326, "r_y1": 314.91617, "r_x2": 559.71326, "r_y2": 306.54153, "r_x3": 317.95499, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 325.87518, "r_x1": 558.41443, "r_y1": 325.87518, "r_x2": 558.41443, "r_y2": 317.50055, "r_x3": 317.95499, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 336.83417, "r_x1": 559.71545, "r_y1": 336.83417, "r_x2": 559.71545, "r_y2": 328.4595299999999, "r_x3": 317.95499, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 347.79318, "r_x1": 558.36865, "r_y1": 347.79318, "r_x2": 558.36865, "r_y2": 339.41855000000004, "r_x3": 317.95499, "r_y3": 339.41855000000004, "coord_origin": "TOPLEFT" }, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 358.75217, "r_x1": 558.20062, "r_y1": 358.75217, "r_x2": 558.20062, "r_y2": 350.37753, "r_x3": 317.95499, "r_y3": 350.37753, "coord_origin": "TOPLEFT" }, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 369.71017, "r_x1": 558.20184, "r_y1": 369.71017, "r_x2": 558.20184, "r_y2": 361.33554, "r_x3": 317.95499, "r_y3": 361.33554, "coord_origin": "TOPLEFT" }, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 380.66916, "r_x1": 559.71442, "r_y1": 380.66916, "r_x2": 559.71442, "r_y2": 372.29453, "r_x3": 317.95499, "r_y3": 372.29453, "coord_origin": "TOPLEFT" }, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 391.62817, "r_x1": 559.1864, "r_y1": 391.62817, "r_x2": 559.1864, "r_y2": 383.25354, "r_x3": 317.95499, "r_y3": 383.25354, "coord_origin": "TOPLEFT" }, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 316.94199, "r_y0": 402.58716, "r_x1": 558.20111, "r_y1": 402.58716, "r_x2": 558.20111, "r_y2": 394.21252, "r_x3": 316.94199, "r_y3": 394.21252, "coord_origin": "TOPLEFT" }, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "orig": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 413.54617, "r_x1": 559.7132, "r_y1": 413.54617, "r_x2": 559.7132, "r_y2": 405.17154, "r_x3": 317.95499, "r_y3": 405.17154, "coord_origin": "TOPLEFT" }, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 424.50515999999993, "r_x1": 558.20013, "r_y1": 424.50515999999993, "r_x2": 558.20013, "r_y2": 416.13052, "r_x3": 317.95499, "r_y3": 416.13052, "coord_origin": "TOPLEFT" }, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 435.46417, "r_x1": 464.50613, "r_y1": 435.46417, "r_x2": 464.50613, "r_y2": 427.08953999999994, "r_x3": 317.95499, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.98199000000005, "r_y0": 435.47313999999994, "r_x1": 482.14560000000006, "r_y1": 435.47313999999994, "r_x2": 482.14560000000006, "r_y2": 427.13439999999997, "r_x3": 466.98199000000005, "r_y3": 427.13439999999997, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 485.13199000000003, "r_y0": 435.46417, "r_x1": 558.19727, "r_y1": 435.46417, "r_x2": 558.19727, "r_y2": 427.08953999999994, "r_x3": 485.13199000000003, "r_y3": 427.08953999999994, "coord_origin": "TOPLEFT" }, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 446.4231899999999, "r_x1": 558.20221, "r_y1": 446.4231899999999, "r_x2": 558.20221, "r_y2": 438.04855, "r_x3": 317.95499, "r_y3": 438.04855, "coord_origin": "TOPLEFT" }, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 457.38217, "r_x1": 558.51501, "r_y1": 457.38217, "r_x2": 558.51501, "r_y2": 449.00754, "r_x3": 317.95499, "r_y3": 449.00754, "coord_origin": "TOPLEFT" }, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "orig": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 468.34119, "r_x1": 558.19855, "r_y1": 468.34119, "r_x2": 558.19855, "r_y2": 459.96655000000004, "r_x3": 317.95499, "r_y3": 459.96655000000004, "coord_origin": "TOPLEFT" }, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 479.29916, "r_x1": 559.58215, "r_y1": 479.29916, "r_x2": 559.58215, "r_y2": 470.92453, "r_x3": 317.95499, "r_y3": 470.92453, "coord_origin": "TOPLEFT" }, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "orig": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 490.25818, "r_x1": 558.41559, "r_y1": 490.25818, "r_x2": 558.41559, "r_y2": 481.88354, "r_x3": 317.64099, "r_y3": 481.88354, "coord_origin": "TOPLEFT" }, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 501.21716, "r_x1": 559.72156, "r_y1": 501.21716, "r_x2": 559.72156, "r_y2": 492.84253, "r_x3": 317.95499, "r_y3": 492.84253, "coord_origin": "TOPLEFT" }, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 512.1761799999999, "r_x1": 558.19928, "r_y1": 512.1761799999999, "r_x2": 558.19928, "r_y2": 503.80154, "r_x3": 317.95499, "r_y3": 503.80154, "coord_origin": "TOPLEFT" }, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 523.13516, "r_x1": 558.20581, "r_y1": 523.13516, "r_x2": 558.20581, "r_y2": 514.76053, "r_x3": 317.95499, "r_y3": 514.76053, "coord_origin": "TOPLEFT" }, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 534.09418, "r_x1": 559.71729, "r_y1": 534.09418, "r_x2": 559.71729, "r_y2": 525.71954, "r_x3": 317.95499, "r_y3": 525.71954, "coord_origin": "TOPLEFT" }, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 545.05318, "r_x1": 558.20404, "r_y1": 545.05318, "r_x2": 558.20404, "r_y2": 536.6785600000001, "r_x3": 317.95499, "r_y3": 536.6785600000001, "coord_origin": "TOPLEFT" }, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 556.01218, "r_x1": 558.20074, "r_y1": 556.01218, "r_x2": 558.20074, "r_y2": 547.63756, "r_x3": 317.95499, "r_y3": 547.63756, "coord_origin": "TOPLEFT" }, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.97118, "r_x1": 558.20062, "r_y1": 566.97118, "r_x2": 558.20062, "r_y2": 558.59656, "r_x3": 317.95499, "r_y3": 558.59656, "coord_origin": "TOPLEFT" }, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.9301800000001, "r_x1": 558.20416, "r_y1": 577.9301800000001, "r_x2": 558.20416, "r_y2": 569.55556, "r_x3": 317.95499, "r_y3": 569.55556, "coord_origin": "TOPLEFT" }, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 588.88918, "r_x1": 457.62469, "r_y1": 588.88918, "r_x2": 457.62469, "r_y2": 580.51456, "r_x3": 317.95499, "r_y3": 580.51456, "coord_origin": "TOPLEFT" }, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4." }, { "label": "section_header", "id": 10, "page_no": 2, "cluster": { "id": 10, "label": "section_header", "bbox": { "l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT" }, "confidence": 0.9422592520713806, "cells": [ { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 617.15901, "r_x1": 323.56226, "r_y1": 617.15901, "r_x2": 323.56226, "r_y2": 606.84991, "r_x3": 317.95499, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47134, "r_y0": 617.15901, "r_x1": 470.21326, "r_y1": 617.15901, "r_x2": 470.21326, "r_y2": 606.84991, "r_x3": 334.47134, "r_y3": 606.84991, "coord_origin": "TOPLEFT" }, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "4 ANNOTATION CAMPAIGN" }, { "label": "text", "id": 5, "page_no": 2, "cluster": { "id": 5, "label": "text", "bbox": { "l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT" }, "confidence": 0.9863831996917725, "cells": [ { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 640.34818, "r_x1": 558.20148, "r_y1": 640.34818, "r_x2": 558.20148, "r_y2": 631.97356, "r_x3": 317.686, "r_y3": 631.97356, "coord_origin": "TOPLEFT" }, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.30717, "r_x1": 559.58521, "r_y1": 651.30717, "r_x2": 559.58521, "r_y2": 642.93256, "r_x3": 317.95499, "r_y3": 642.93256, "coord_origin": "TOPLEFT" }, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.26617, "r_x1": 558.20007, "r_y1": 662.26617, "r_x2": 558.20007, "r_y2": 653.89156, "r_x3": 317.95499, "r_y3": 653.89156, "coord_origin": "TOPLEFT" }, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.22517, "r_x1": 559.71375, "r_y1": 673.22517, "r_x2": 559.71375, "r_y2": 664.85056, "r_x3": 317.95499, "r_y3": 664.85056, "coord_origin": "TOPLEFT" }, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.18417, "r_x1": 558.20233, "r_y1": 684.18417, "r_x2": 558.20233, "r_y2": 675.80956, "r_x3": 317.95499, "r_y3": 675.80956, "coord_origin": "TOPLEFT" }, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 695.143173, "r_x1": 559.71381, "r_y1": 695.143173, "r_x2": 559.71381, "r_y2": 686.76855, "r_x3": 317.95499, "r_y3": 686.76855, "coord_origin": "TOPLEFT" }, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 706.102173, "r_x1": 559.1864, "r_y1": 706.102173, "r_x2": 559.1864, "r_y2": 697.7275539999999, "r_x3": 317.95499, "r_y3": 697.7275539999999, "coord_origin": "TOPLEFT" }, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four," } ], "headers": [ { "label": "page_header", "id": 13, "page_no": 2, "cluster": { "id": 13, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8919971585273743, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" }, { "label": "page_header", "id": 11, "page_no": 2, "cluster": { "id": 11, "label": "page_header", "bbox": { "l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.9104517102241516, "cells": [ { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA" } ] } }, { "page_no": 3, "size": { "width": 612.0, "height": 792.0 }, "parsed_page": { "dimension": { "angle": 0.0, "rect": { "r_x0": 0.0, "r_y0": 0.0, "r_x1": 612.0, "r_y1": 0.0, "r_x2": 612.0, "r_y2": 792.0, "r_x3": 0.0, "r_y3": 792.0, "coord_origin": "BOTTOMLEFT" }, "boundary_type": "crop_box", "art_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "bleed_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "crop_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "media_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "trim_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" } }, "bitmap_resources": [], "char_cells": [], "word_cells": [], "textline_cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 93.42822000000001, "r_x1": 558.48969, "r_y1": 93.42822000000001, "r_x2": 558.48969, "r_y2": 84.95495999999991, "r_x3": 53.501999, "r_y3": 84.95495999999991, "coord_origin": "TOPLEFT" }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 104.3872100000001, "r_x1": 558.20294, "r_y1": 104.3872100000001, "r_x2": 558.20294, "r_y2": 95.91394000000003, "r_x3": 53.79800000000001, "r_y3": 95.91394000000003, "coord_origin": "TOPLEFT" }, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 115.34618999999998, "r_x1": 469.84805000000006, "r_y1": 115.34618999999998, "r_x2": 469.84805000000006, "r_y2": 106.87292000000002, "r_x3": 53.79800000000001, "r_y3": 106.87292000000002, "coord_origin": "TOPLEFT" }, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 233.94400000000002, "r_y0": 148.59813999999994, "r_x1": 270.04272, "r_y1": 148.59813999999994, "r_x2": 270.04272, "r_y2": 140.22351000000003, "r_x3": 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"r_y3": 283.48654, "coord_origin": "TOPLEFT" }, "text": "68-85", "orig": "68-85", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 562.48325, "r_x1": 295.64871, "r_y1": 562.48325, "r_x2": 295.64871, "r_y2": 554.00999, "r_x3": 53.79800000000001, "r_y3": 554.00999, "coord_origin": "TOPLEFT" }, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 573.44225, "r_x1": 295.64874, "r_y1": 573.44225, "r_x2": 295.64874, "r_y2": 564.96899, "r_x3": 53.79800000000001, "r_y3": 564.96899, "coord_origin": "TOPLEFT" }, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 584.40125, "r_x1": 294.04376, "r_y1": 584.40125, "r_x2": 294.04376, "r_y2": 575.92799, "r_x3": 53.79800000000001, "r_y3": 575.92799, "coord_origin": "TOPLEFT" }, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 595.36024, "r_x1": 294.04373, "r_y1": 595.36024, "r_x2": 294.04373, "r_y2": 586.88699, "r_x3": 53.79800000000001, "r_y3": 586.88699, "coord_origin": "TOPLEFT" }, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 606.31924, "r_x1": 252.78931000000003, "r_y1": 606.31924, "r_x2": 252.78931000000003, "r_y2": 597.84599, "r_x3": 53.79800000000001, "r_y3": 597.84599, "coord_origin": "TOPLEFT" }, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 642.66617, "r_x1": 294.04745, "r_y1": 642.66617, "r_x2": 294.04745, "r_y2": 634.29155, "r_x3": 53.466999, "r_y3": 634.29155, "coord_origin": "TOPLEFT" }, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.62517, "r_x1": 294.04535, "r_y1": 653.62517, "r_x2": 294.04535, "r_y2": 645.25055, "r_x3": 53.79800000000001, "r_y3": 645.25055, "coord_origin": "TOPLEFT" }, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.58417, "r_x1": 294.04422, "r_y1": 664.58417, "r_x2": 294.04422, "r_y2": 656.20955, "r_x3": 53.79800000000001, "r_y3": 656.20955, "coord_origin": "TOPLEFT" }, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 675.54317, "r_x1": 170.58611, "r_y1": 675.54317, "r_x2": 170.58611, "r_y2": 667.16856, "r_x3": 53.466999, "r_y3": 667.16856, "coord_origin": "TOPLEFT" }, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76099800000001, "r_y0": 686.48424, "r_x1": 226.72533000000004, "r_y1": 686.48424, "r_x2": 226.72533000000004, "r_y2": 678.01099, "r_x3": 63.76099800000001, "r_y3": 678.01099, "coord_origin": "TOPLEFT" }, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 229.06900000000002, "r_y0": 686.50217, "r_x1": 295.55844, "r_y1": 686.50217, "r_x2": 295.55844, "r_y2": 678.12756, "r_x3": 229.06900000000002, "r_y3": 678.12756, "coord_origin": "TOPLEFT" }, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.461174, "r_x1": 294.04538, "r_y1": 697.461174, "r_x2": 294.04538, "r_y2": 689.08656, "r_x3": 53.79800000000001, "r_y3": 689.08656, "coord_origin": "TOPLEFT" }, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.420174, "r_x1": 294.04642, "r_y1": 708.420174, "r_x2": 294.04642, "r_y2": 700.045555, "r_x3": 53.79800000000001, "r_y3": 700.045555, "coord_origin": "TOPLEFT" }, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.45416000000006, "r_x1": 559.18536, "r_y1": 320.45416000000006, "r_x2": 559.18536, "r_y2": 312.07953, "r_x3": 317.95499, "r_y3": 312.07953, "coord_origin": "TOPLEFT" }, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "orig": "include publication repositories such as arXiv$^{3}$, government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.41217, "r_x1": 558.19843, "r_y1": 331.41217, "r_x2": 558.19843, "r_y2": 323.03754, "r_x3": 317.95499, "r_y3": 323.03754, "coord_origin": "TOPLEFT" }, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.37119, "r_x1": 558.36963, "r_y1": 342.37119, "r_x2": 558.36963, "r_y2": 333.99655, "r_x3": 317.95499, "r_y3": 333.99655, "coord_origin": "TOPLEFT" }, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 353.33017, "r_x1": 558.2041, "r_y1": 353.33017, "r_x2": 558.2041, "r_y2": 344.95554, "r_x3": 317.95499, "r_y3": 344.95554, "coord_origin": "TOPLEFT" }, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 364.28918, "r_x1": 558.20294, "r_y1": 364.28918, "r_x2": 558.20294, "r_y2": 355.91455, "r_x3": 317.95499, "r_y3": 355.91455, "coord_origin": "TOPLEFT" }, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 375.24817, "r_x1": 496.71826, "r_y1": 375.24817, "r_x2": 496.71826, "r_y2": 366.87354, "r_x3": 317.95499, "r_y3": 366.87354, "coord_origin": "TOPLEFT" }, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 386.20717999999994, "r_x1": 558.20618, "r_y1": 386.20717999999994, "r_x2": 558.20618, "r_y2": 377.8325500000001, "r_x3": 327.918, "r_y3": 377.8325500000001, "coord_origin": "TOPLEFT" }, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 397.16617, "r_x1": 558.2019, "r_y1": 397.16617, "r_x2": 558.2019, "r_y2": 388.79153, "r_x3": 317.95499, "r_y3": 388.79153, "coord_origin": "TOPLEFT" }, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 408.12518, "r_x1": 558.20233, "r_y1": 408.12518, "r_x2": 558.20233, "r_y2": 399.75055, "r_x3": 317.95499, "r_y3": 399.75055, "coord_origin": "TOPLEFT" }, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 419.08417, "r_x1": 559.71277, "r_y1": 419.08417, "r_x2": 559.71277, "r_y2": 410.7095299999999, "r_x3": 317.95499, "r_y3": 410.7095299999999, "coord_origin": "TOPLEFT" }, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 430.04318, "r_x1": 558.20062, "r_y1": 430.04318, "r_x2": 558.20062, "r_y2": 421.66855000000004, "r_x3": 317.95499, "r_y3": 421.66855000000004, "coord_origin": "TOPLEFT" }, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 441.00217, "r_x1": 559.71307, "r_y1": 441.00217, "r_x2": 559.71307, "r_y2": 432.62753, "r_x3": 317.95499, "r_y3": 432.62753, "coord_origin": "TOPLEFT" }, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 451.96017, "r_x1": 558.36877, "r_y1": 451.96017, "r_x2": 558.36877, "r_y2": 443.58554, "r_x3": 317.95499, "r_y3": 443.58554, "coord_origin": "TOPLEFT" }, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 462.91916, "r_x1": 558.20428, "r_y1": 462.91916, "r_x2": 558.20428, "r_y2": 454.54453, "r_x3": 317.95499, "r_y3": 454.54453, "coord_origin": "TOPLEFT" }, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 473.87817, "r_x1": 558.36877, "r_y1": 473.87817, "r_x2": 558.36877, "r_y2": 465.50354, "r_x3": 317.95499, "r_y3": 465.50354, "coord_origin": "TOPLEFT" }, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 484.83716, "r_x1": 558.20428, "r_y1": 484.83716, "r_x2": 558.20428, "r_y2": 476.46252, "r_x3": 317.95499, "r_y3": 476.46252, "coord_origin": "TOPLEFT" }, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 495.79617, "r_x1": 558.5307, "r_y1": 495.79617, "r_x2": 558.5307, "r_y2": 487.42154, "r_x3": 317.95499, "r_y3": 487.42154, "coord_origin": "TOPLEFT" }, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 506.75516, "r_x1": 488.46914999999996, "r_y1": 506.75516, "r_x2": 488.46914999999996, "r_y2": 498.38052, "r_x3": 317.95499, "r_y3": 498.38052, "coord_origin": "TOPLEFT" }, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 517.69623, "r_x1": 482.41809, "r_y1": 517.69623, "r_x2": 482.41809, "r_y2": 509.22299, "r_x3": 327.918, "r_y3": 509.22299, "coord_origin": "TOPLEFT" }, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.474, "r_y0": 517.71417, "r_x1": 559.71765, "r_y1": 517.71417, "r_x2": 559.71765, "r_y2": 509.33954, "r_x3": 484.474, "r_y3": 509.33954, "coord_origin": "TOPLEFT" }, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 528.67319, "r_x1": 559.71619, "r_y1": 528.67319, "r_x2": 559.71619, "r_y2": 520.29855, "r_x3": 317.95499, "r_y3": 520.29855, "coord_origin": "TOPLEFT" }, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 539.63217, "r_x1": 558.20239, "r_y1": 539.63217, "r_x2": 558.20239, "r_y2": 531.2575400000001, "r_x3": 317.95499, "r_y3": 531.2575400000001, "coord_origin": "TOPLEFT" }, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 550.59117, "r_x1": 559.58502, "r_y1": 550.59117, "r_x2": 559.58502, "r_y2": 542.21655, "r_x3": 317.95499, "r_y3": 542.21655, "coord_origin": "TOPLEFT" }, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 561.54918, "r_x1": 404.14197, "r_y1": 561.54918, "r_x2": 404.14197, "r_y2": 553.17456, "r_x3": 317.686, "r_y3": 553.17456, "coord_origin": "TOPLEFT" }, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 226, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 616.3441799999999, "r_x1": 558.20105, "r_y1": 616.3441799999999, "r_x2": 558.20105, "r_y2": 607.96956, "r_x3": 317.95499, "r_y3": 607.96956, "coord_origin": "TOPLEFT" }, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 227, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 627.30318, "r_x1": 559.18665, "r_y1": 627.30318, "r_x2": 559.18665, "r_y2": 618.9285600000001, "r_x3": 317.95499, "r_y3": 618.9285600000001, "coord_origin": "TOPLEFT" }, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 228, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 638.26218, "r_x1": 558.20142, "r_y1": 638.26218, "r_x2": 558.20142, "r_y2": 629.88756, "r_x3": 317.62299, "r_y3": 629.88756, "coord_origin": "TOPLEFT" }, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 229, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 649.22118, "r_x1": 558.20227, "r_y1": 649.22118, "r_x2": 558.20227, "r_y2": 640.84656, "r_x3": 317.95499, "r_y3": 640.84656, "coord_origin": "TOPLEFT" }, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 230, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 660.1801800000001, "r_x1": 436.90649, "r_y1": 660.1801800000001, "r_x2": 436.90649, "r_y2": 651.80556, "r_x3": 317.95499, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 231, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 439.13800000000003, "r_y0": 660.18915, "r_x1": 469.69134999999994, "r_y1": 660.18915, "r_x2": 469.69134999999994, "r_y2": 651.8503900000001, "r_x3": 439.13800000000003, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 232, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 472.42898999999994, "r_y0": 660.1801800000001, "r_x1": 493.97348, "r_y1": 660.1801800000001, "r_x2": 493.97348, "r_y2": 651.80556, "r_x3": 472.42898999999994, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 233, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 496.207, "r_y0": 660.18915, "r_x1": 558.20001, "r_y1": 660.18915, "r_x2": 558.20001, "r_y2": 651.8503900000001, "r_x3": 496.207, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 234, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 671.13918, "r_x1": 558.20557, "r_y1": 671.13918, "r_x2": 558.20557, "r_y2": 662.76456, "r_x3": 317.95499, "r_y3": 662.76456, "coord_origin": "TOPLEFT" }, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 235, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 682.09718, "r_x1": 447.65221999999994, "r_y1": 682.09718, "r_x2": 447.65221999999994, "r_y2": 673.7225599999999, "r_x3": 317.95499, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 236, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.85999, "r_y0": 682.10614, "r_x1": 474.31439, "r_y1": 682.10614, "r_x2": 474.31439, "r_y2": 673.76739, "r_x3": 449.85999, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 237, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.172, "r_y0": 682.09718, "r_x1": 490.39655, "r_y1": 682.09718, "r_x2": 490.39655, "r_y2": 673.7225599999999, "r_x3": 477.172, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 238, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 492.60599, "r_y0": 682.10614, "r_x1": 528.29907, "r_y1": 682.10614, "r_x2": 528.29907, "r_y2": 673.76739, "r_x3": 492.60599, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 239, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 528.29901, "r_y0": 682.09718, "r_x1": 558.20148, "r_y1": 682.09718, "r_x2": 558.20148, "r_y2": 673.7225599999999, "r_x3": 528.29901, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": ", as seen", "orig": ", as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 240, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 693.0561749999999, "r_x1": 558.2041, "r_y1": 693.0561749999999, "r_x2": 558.2041, "r_y2": 684.68156, "r_x3": 317.95499, "r_y3": 684.68156, "coord_origin": "TOPLEFT" }, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 241, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.86689, "r_x1": 369.2457, "r_y1": 708.86689, "r_x2": 369.2457, "r_y2": 702.353363, "r_x3": 317.95499, "r_y3": 702.353363, "coord_origin": "TOPLEFT" }, "text": "$^{3}$https://arxiv.org/", "orig": "$^{3}$https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "has_chars": false, "has_words": false, "has_lines": true, "image": null, "lines": [] }, "predictions": { "layout": { "clusters": [ { "id": 10, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.7625049948692322, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 8, "label": "caption", "bbox": { "l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT" }, "confidence": 0.8906616568565369, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 93.42822000000001, "r_x1": 558.48969, "r_y1": 93.42822000000001, "r_x2": 558.48969, "r_y2": 84.95495999999991, "r_x3": 53.501999, "r_y3": 84.95495999999991, "coord_origin": "TOPLEFT" }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 104.3872100000001, "r_x1": 558.20294, "r_y1": 104.3872100000001, "r_x2": 558.20294, "r_y2": 95.91394000000003, "r_x3": 53.79800000000001, "r_y3": 95.91394000000003, "coord_origin": "TOPLEFT" }, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 115.34618999999998, "r_x1": 469.84805000000006, "r_y1": 115.34618999999998, "r_x2": 469.84805000000006, "r_y2": 106.87292000000002, "r_x3": 53.79800000000001, "r_y3": 106.87292000000002, "coord_origin": "TOPLEFT" }, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "table", "bbox": { "l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT" }, "confidence": 0.9860090613365173, "cells": [ { 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The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 584.40125, "r_x1": 294.04376, "r_y1": 584.40125, "r_x2": 294.04376, "r_y2": 575.92799, "r_x3": 53.79800000000001, "r_y3": 575.92799, "coord_origin": "TOPLEFT" }, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 595.36024, "r_x1": 294.04373, "r_y1": 595.36024, "r_x2": 294.04373, "r_y2": 586.88699, "r_x3": 53.79800000000001, "r_y3": 586.88699, "coord_origin": "TOPLEFT" }, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 606.31924, "r_x1": 252.78931000000003, "r_y1": 606.31924, "r_x2": 252.78931000000003, "r_y2": 597.84599, "r_x3": 53.79800000000001, "r_y3": 597.84599, "coord_origin": "TOPLEFT" }, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "text", "bbox": { "l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT" }, "confidence": 0.980393648147583, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 642.66617, "r_x1": 294.04745, "r_y1": 642.66617, "r_x2": 294.04745, "r_y2": 634.29155, "r_x3": 53.466999, "r_y3": 634.29155, "coord_origin": "TOPLEFT" }, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.62517, "r_x1": 294.04535, "r_y1": 653.62517, "r_x2": 294.04535, "r_y2": 645.25055, "r_x3": 53.79800000000001, "r_y3": 645.25055, "coord_origin": "TOPLEFT" }, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.58417, "r_x1": 294.04422, "r_y1": 664.58417, "r_x2": 294.04422, "r_y2": 656.20955, "r_x3": 53.79800000000001, "r_y3": 656.20955, "coord_origin": "TOPLEFT" }, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 675.54317, "r_x1": 170.58611, "r_y1": 675.54317, "r_x2": 170.58611, "r_y2": 667.16856, "r_x3": 53.466999, "r_y3": 667.16856, "coord_origin": "TOPLEFT" }, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 6, "label": "text", "bbox": { "l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT" }, "confidence": 0.9748398661613464, "cells": [ { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76099800000001, "r_y0": 686.48424, "r_x1": 226.72533000000004, "r_y1": 686.48424, "r_x2": 226.72533000000004, "r_y2": 678.01099, "r_x3": 63.76099800000001, "r_y3": 678.01099, "coord_origin": "TOPLEFT" }, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 229.06900000000002, "r_y0": 686.50217, "r_x1": 295.55844, "r_y1": 686.50217, "r_x2": 295.55844, "r_y2": 678.12756, "r_x3": 229.06900000000002, "r_y3": 678.12756, "coord_origin": "TOPLEFT" }, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.461174, "r_x1": 294.04538, "r_y1": 697.461174, "r_x2": 294.04538, "r_y2": 689.08656, "r_x3": 53.79800000000001, "r_y3": 689.08656, "coord_origin": "TOPLEFT" }, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.420174, "r_x1": 294.04642, "r_y1": 708.420174, "r_x2": 294.04642, "r_y2": 700.045555, "r_x3": 53.79800000000001, "r_y3": 700.045555, "coord_origin": "TOPLEFT" }, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 4, "label": "text", "bbox": { "l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT" }, "confidence": 0.9818483591079712, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.45416000000006, "r_x1": 559.18536, "r_y1": 320.45416000000006, "r_x2": 559.18536, "r_y2": 312.07953, "r_x3": 317.95499, "r_y3": 312.07953, "coord_origin": "TOPLEFT" }, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "orig": "include publication repositories such as arXiv$^{3}$, government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.41217, "r_x1": 558.19843, "r_y1": 331.41217, "r_x2": 558.19843, "r_y2": 323.03754, "r_x3": 317.95499, "r_y3": 323.03754, "coord_origin": "TOPLEFT" }, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.37119, "r_x1": 558.36963, "r_y1": 342.37119, "r_x2": 558.36963, "r_y2": 333.99655, "r_x3": 317.95499, "r_y3": 333.99655, "coord_origin": "TOPLEFT" }, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 353.33017, "r_x1": 558.2041, "r_y1": 353.33017, "r_x2": 558.2041, "r_y2": 344.95554, "r_x3": 317.95499, "r_y3": 344.95554, "coord_origin": "TOPLEFT" }, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 364.28918, "r_x1": 558.20294, "r_y1": 364.28918, "r_x2": 558.20294, "r_y2": 355.91455, "r_x3": 317.95499, "r_y3": 355.91455, "coord_origin": "TOPLEFT" }, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 375.24817, "r_x1": 496.71826, "r_y1": 375.24817, "r_x2": 496.71826, "r_y2": 366.87354, "r_x3": 317.95499, "r_y3": 366.87354, "coord_origin": "TOPLEFT" }, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT" }, "confidence": 0.9866719841957092, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 386.20717999999994, "r_x1": 558.20618, "r_y1": 386.20717999999994, "r_x2": 558.20618, "r_y2": 377.8325500000001, "r_x3": 327.918, "r_y3": 377.8325500000001, "coord_origin": "TOPLEFT" }, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 397.16617, "r_x1": 558.2019, "r_y1": 397.16617, "r_x2": 558.2019, "r_y2": 388.79153, "r_x3": 317.95499, "r_y3": 388.79153, "coord_origin": "TOPLEFT" }, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 408.12518, "r_x1": 558.20233, "r_y1": 408.12518, "r_x2": 558.20233, "r_y2": 399.75055, "r_x3": 317.95499, "r_y3": 399.75055, "coord_origin": "TOPLEFT" }, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 419.08417, "r_x1": 559.71277, "r_y1": 419.08417, "r_x2": 559.71277, "r_y2": 410.7095299999999, "r_x3": 317.95499, "r_y3": 410.7095299999999, "coord_origin": "TOPLEFT" }, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 430.04318, "r_x1": 558.20062, "r_y1": 430.04318, "r_x2": 558.20062, "r_y2": 421.66855000000004, "r_x3": 317.95499, "r_y3": 421.66855000000004, "coord_origin": "TOPLEFT" }, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 441.00217, "r_x1": 559.71307, "r_y1": 441.00217, "r_x2": 559.71307, "r_y2": 432.62753, "r_x3": 317.95499, "r_y3": 432.62753, "coord_origin": "TOPLEFT" }, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 451.96017, "r_x1": 558.36877, "r_y1": 451.96017, "r_x2": 558.36877, "r_y2": 443.58554, "r_x3": 317.95499, "r_y3": 443.58554, "coord_origin": "TOPLEFT" }, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 462.91916, "r_x1": 558.20428, "r_y1": 462.91916, "r_x2": 558.20428, "r_y2": 454.54453, "r_x3": 317.95499, "r_y3": 454.54453, "coord_origin": "TOPLEFT" }, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 473.87817, "r_x1": 558.36877, "r_y1": 473.87817, "r_x2": 558.36877, "r_y2": 465.50354, "r_x3": 317.95499, "r_y3": 465.50354, "coord_origin": "TOPLEFT" }, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 484.83716, "r_x1": 558.20428, "r_y1": 484.83716, "r_x2": 558.20428, "r_y2": 476.46252, "r_x3": 317.95499, "r_y3": 476.46252, "coord_origin": "TOPLEFT" }, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 495.79617, "r_x1": 558.5307, "r_y1": 495.79617, "r_x2": 558.5307, "r_y2": 487.42154, "r_x3": 317.95499, "r_y3": 487.42154, "coord_origin": "TOPLEFT" }, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 506.75516, "r_x1": 488.46914999999996, "r_y1": 506.75516, "r_x2": 488.46914999999996, "r_y2": 498.38052, "r_x3": 317.95499, "r_y3": 498.38052, "coord_origin": "TOPLEFT" }, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9847556948661804, "cells": [ { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 517.69623, "r_x1": 482.41809, "r_y1": 517.69623, "r_x2": 482.41809, "r_y2": 509.22299, "r_x3": 327.918, "r_y3": 509.22299, "coord_origin": "TOPLEFT" }, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.474, "r_y0": 517.71417, "r_x1": 559.71765, "r_y1": 517.71417, "r_x2": 559.71765, "r_y2": 509.33954, "r_x3": 484.474, "r_y3": 509.33954, "coord_origin": "TOPLEFT" }, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 528.67319, "r_x1": 559.71619, "r_y1": 528.67319, "r_x2": 559.71619, "r_y2": 520.29855, "r_x3": 317.95499, "r_y3": 520.29855, "coord_origin": "TOPLEFT" }, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 539.63217, "r_x1": 558.20239, "r_y1": 539.63217, "r_x2": 558.20239, "r_y2": 531.2575400000001, "r_x3": 317.95499, "r_y3": 531.2575400000001, "coord_origin": "TOPLEFT" }, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 550.59117, "r_x1": 559.58502, "r_y1": 550.59117, "r_x2": 559.58502, "r_y2": 542.21655, "r_x3": 317.95499, "r_y3": 542.21655, "coord_origin": "TOPLEFT" }, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 561.54918, "r_x1": 404.14197, "r_y1": 561.54918, "r_x2": 404.14197, "r_y2": 553.17456, "r_x3": 317.686, "r_y3": 553.17456, "coord_origin": "TOPLEFT" }, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, 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Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 228, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 638.26218, "r_x1": 558.20142, "r_y1": 638.26218, "r_x2": 558.20142, "r_y2": 629.88756, "r_x3": 317.62299, "r_y3": 629.88756, "coord_origin": "TOPLEFT" }, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 229, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 649.22118, "r_x1": 558.20227, "r_y1": 649.22118, "r_x2": 558.20227, "r_y2": 640.84656, "r_x3": 317.95499, "r_y3": 640.84656, "coord_origin": "TOPLEFT" }, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. 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We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 235, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 682.09718, "r_x1": 447.65221999999994, "r_y1": 682.09718, "r_x2": 447.65221999999994, "r_y2": 673.7225599999999, "r_x3": 317.95499, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 236, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.85999, "r_y0": 682.10614, "r_x1": 474.31439, "r_y1": 682.10614, "r_x2": 474.31439, "r_y2": 673.76739, "r_x3": 449.85999, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 237, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.172, "r_y0": 682.09718, "r_x1": 490.39655, "r_y1": 682.09718, "r_x2": 490.39655, "r_y2": 673.7225599999999, "r_x3": 477.172, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 238, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 492.60599, "r_y0": 682.10614, "r_x1": 528.29907, "r_y1": 682.10614, "r_x2": 528.29907, "r_y2": 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"KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" }, { "label": "caption", "id": 8, "page_no": 3, "cluster": { "id": 8, "label": "caption", "bbox": { "l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT" }, "confidence": 0.8906616568565369, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 93.42822000000001, "r_x1": 558.48969, "r_y1": 93.42822000000001, "r_x2": 558.48969, "r_y2": 84.95495999999991, "r_x3": 53.501999, "r_y3": 84.95495999999991, "coord_origin": "TOPLEFT" }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 104.3872100000001, "r_x1": 558.20294, "r_y1": 104.3872100000001, "r_x2": 558.20294, "r_y2": 95.91394000000003, "r_x3": 53.79800000000001, "r_y3": 95.91394000000003, "coord_origin": "TOPLEFT" }, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 115.34618999999998, "r_x1": 469.84805000000006, "r_y1": 115.34618999999998, "r_x2": 469.84805000000006, "r_y2": 106.87292000000002, "r_x3": 53.79800000000001, "r_y3": 106.87292000000002, "coord_origin": "TOPLEFT" }, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. 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477.50867, "b": 291.86118000000005, "coord_origin": "TOPLEFT" }, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 10, "end_col_offset_idx": 11, "text": "71-76", "column_header": false, "row_header": false, "row_section": false }, { "bbox": { "l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT" }, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 11, "end_col_offset_idx": 12, "text": "68-85", "column_header": false, "row_header": false, "row_section": false } ] }, { "label": "caption", "id": 9, "page_no": 3, "cluster": { "id": 9, "label": "caption", "bbox": { "l": 53.79800000000001, "t": 554.00999, "r": 295.64874, "b": 606.31924, "coord_origin": "TOPLEFT" }, "confidence": 0.810490071773529, "cells": [ { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 562.48325, "r_x1": 295.64871, "r_y1": 562.48325, "r_x2": 295.64871, "r_y2": 554.00999, "r_x3": 53.79800000000001, "r_y3": 554.00999, "coord_origin": "TOPLEFT" }, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 573.44225, "r_x1": 295.64874, "r_y1": 573.44225, "r_x2": 295.64874, "r_y2": 564.96899, "r_x3": 53.79800000000001, "r_y3": 564.96899, "coord_origin": "TOPLEFT" }, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 584.40125, "r_x1": 294.04376, "r_y1": 584.40125, "r_x2": 294.04376, "r_y2": 575.92799, "r_x3": 53.79800000000001, "r_y3": 575.92799, "coord_origin": "TOPLEFT" }, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 595.36024, "r_x1": 294.04373, "r_y1": 595.36024, "r_x2": 294.04373, "r_y2": 586.88699, "r_x3": 53.79800000000001, "r_y3": 586.88699, "coord_origin": "TOPLEFT" }, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 606.31924, "r_x1": 252.78931000000003, "r_y1": 606.31924, "r_x2": 252.78931000000003, "r_y2": 597.84599, "r_x3": 53.79800000000001, "r_y3": 597.84599, "coord_origin": "TOPLEFT" }, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right." }, { "label": "text", "id": 5, "page_no": 3, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT" }, "confidence": 0.980393648147583, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 642.66617, "r_x1": 294.04745, "r_y1": 642.66617, "r_x2": 294.04745, "r_y2": 634.29155, "r_x3": 53.466999, "r_y3": 634.29155, "coord_origin": "TOPLEFT" }, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.62517, "r_x1": 294.04535, "r_y1": 653.62517, "r_x2": 294.04535, "r_y2": 645.25055, "r_x3": 53.79800000000001, "r_y3": 645.25055, "coord_origin": "TOPLEFT" }, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.58417, "r_x1": 294.04422, "r_y1": 664.58417, "r_x2": 294.04422, "r_y2": 656.20955, "r_x3": 53.79800000000001, "r_y3": 656.20955, "coord_origin": "TOPLEFT" }, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 675.54317, "r_x1": 170.58611, "r_y1": 675.54317, "r_x2": 170.58611, "r_y2": 667.16856, "r_x3": 53.466999, "r_y3": 667.16856, "coord_origin": "TOPLEFT" }, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised." }, { "label": "text", "id": 6, "page_no": 3, "cluster": { "id": 6, "label": "text", "bbox": { "l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT" }, "confidence": 0.9748398661613464, "cells": [ { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76099800000001, "r_y0": 686.48424, "r_x1": 226.72533000000004, "r_y1": 686.48424, "r_x2": 226.72533000000004, "r_y2": 678.01099, "r_x3": 63.76099800000001, "r_y3": 678.01099, "coord_origin": "TOPLEFT" }, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 229.06900000000002, "r_y0": 686.50217, "r_x1": 295.55844, "r_y1": 686.50217, "r_x2": 295.55844, "r_y2": 678.12756, "r_x3": 229.06900000000002, "r_y3": 678.12756, "coord_origin": "TOPLEFT" }, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.461174, "r_x1": 294.04538, "r_y1": 697.461174, "r_x2": 294.04538, "r_y2": 689.08656, "r_x3": 53.79800000000001, "r_y3": 689.08656, "coord_origin": "TOPLEFT" }, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.420174, "r_x1": 294.04642, "r_y1": 708.420174, "r_x2": 294.04642, "r_y2": 700.045555, "r_x3": 53.79800000000001, "r_y3": 700.045555, "coord_origin": "TOPLEFT" }, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources" }, { "label": "text", "id": 4, "page_no": 3, "cluster": { "id": 4, "label": "text", "bbox": { "l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT" }, "confidence": 0.9818483591079712, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.45416000000006, "r_x1": 559.18536, "r_y1": 320.45416000000006, "r_x2": 559.18536, "r_y2": 312.07953, "r_x3": 317.95499, "r_y3": 312.07953, "coord_origin": "TOPLEFT" }, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "orig": "include publication repositories such as arXiv$^{3}$, government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.41217, "r_x1": 558.19843, "r_y1": 331.41217, "r_x2": 558.19843, "r_y2": 323.03754, "r_x3": 317.95499, "r_y3": 323.03754, "coord_origin": "TOPLEFT" }, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.37119, "r_x1": 558.36963, "r_y1": 342.37119, "r_x2": 558.36963, "r_y2": 333.99655, "r_x3": 317.95499, "r_y3": 333.99655, "coord_origin": "TOPLEFT" }, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 353.33017, "r_x1": 558.2041, "r_y1": 353.33017, "r_x2": 558.2041, "r_y2": 344.95554, "r_x3": 317.95499, "r_y3": 344.95554, "coord_origin": "TOPLEFT" }, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 364.28918, "r_x1": 558.20294, "r_y1": 364.28918, "r_x2": 558.20294, "r_y2": 355.91455, "r_x3": 317.95499, "r_y3": 355.91455, "coord_origin": "TOPLEFT" }, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 375.24817, "r_x1": 496.71826, "r_y1": 375.24817, "r_x2": 496.71826, "r_y2": 366.87354, "r_x3": 317.95499, "r_y3": 366.87354, "coord_origin": "TOPLEFT" }, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process." }, { "label": "text", "id": 0, "page_no": 3, "cluster": { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT" }, "confidence": 0.9866719841957092, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 386.20717999999994, "r_x1": 558.20618, "r_y1": 386.20717999999994, "r_x2": 558.20618, "r_y2": 377.8325500000001, "r_x3": 327.918, "r_y3": 377.8325500000001, "coord_origin": "TOPLEFT" }, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 397.16617, "r_x1": 558.2019, "r_y1": 397.16617, "r_x2": 558.2019, "r_y2": 388.79153, "r_x3": 317.95499, "r_y3": 388.79153, "coord_origin": "TOPLEFT" }, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 408.12518, "r_x1": 558.20233, "r_y1": 408.12518, "r_x2": 558.20233, "r_y2": 399.75055, "r_x3": 317.95499, "r_y3": 399.75055, "coord_origin": "TOPLEFT" }, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 419.08417, "r_x1": 559.71277, "r_y1": 419.08417, "r_x2": 559.71277, "r_y2": 410.7095299999999, "r_x3": 317.95499, "r_y3": 410.7095299999999, "coord_origin": "TOPLEFT" }, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 430.04318, "r_x1": 558.20062, "r_y1": 430.04318, "r_x2": 558.20062, "r_y2": 421.66855000000004, "r_x3": 317.95499, "r_y3": 421.66855000000004, "coord_origin": "TOPLEFT" }, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 441.00217, "r_x1": 559.71307, "r_y1": 441.00217, "r_x2": 559.71307, "r_y2": 432.62753, "r_x3": 317.95499, "r_y3": 432.62753, "coord_origin": "TOPLEFT" }, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 451.96017, "r_x1": 558.36877, "r_y1": 451.96017, "r_x2": 558.36877, "r_y2": 443.58554, "r_x3": 317.95499, "r_y3": 443.58554, "coord_origin": "TOPLEFT" }, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 462.91916, "r_x1": 558.20428, "r_y1": 462.91916, "r_x2": 558.20428, "r_y2": 454.54453, "r_x3": 317.95499, "r_y3": 454.54453, "coord_origin": "TOPLEFT" }, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 473.87817, "r_x1": 558.36877, "r_y1": 473.87817, "r_x2": 558.36877, "r_y2": 465.50354, "r_x3": 317.95499, "r_y3": 465.50354, "coord_origin": "TOPLEFT" }, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 484.83716, "r_x1": 558.20428, "r_y1": 484.83716, "r_x2": 558.20428, "r_y2": 476.46252, "r_x3": 317.95499, "r_y3": 476.46252, "coord_origin": "TOPLEFT" }, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 495.79617, "r_x1": 558.5307, "r_y1": 495.79617, "r_x2": 558.5307, "r_y2": 487.42154, "r_x3": 317.95499, "r_y3": 487.42154, "coord_origin": "TOPLEFT" }, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 506.75516, "r_x1": 488.46914999999996, "r_y1": 506.75516, "r_x2": 488.46914999999996, "r_y2": 498.38052, "r_x3": 317.95499, "r_y3": 498.38052, "coord_origin": "TOPLEFT" }, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains." }, { "label": "text", "id": 3, "page_no": 3, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9847556948661804, "cells": [ { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 517.69623, "r_x1": 482.41809, "r_y1": 517.69623, "r_x2": 482.41809, "r_y2": 509.22299, "r_x3": 327.918, "r_y3": 509.22299, "coord_origin": "TOPLEFT" }, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.474, "r_y0": 517.71417, "r_x1": 559.71765, "r_y1": 517.71417, "r_x2": 559.71765, "r_y2": 509.33954, "r_x3": 484.474, "r_y3": 509.33954, "coord_origin": "TOPLEFT" }, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 528.67319, "r_x1": 559.71619, "r_y1": 528.67319, "r_x2": 559.71619, "r_y2": 520.29855, "r_x3": 317.95499, "r_y3": 520.29855, "coord_origin": "TOPLEFT" }, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 539.63217, "r_x1": 558.20239, "r_y1": 539.63217, "r_x2": 558.20239, "r_y2": 531.2575400000001, "r_x3": 317.95499, "r_y3": 531.2575400000001, "coord_origin": "TOPLEFT" }, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 550.59117, "r_x1": 559.58502, "r_y1": 550.59117, "r_x2": 559.58502, "r_y2": 542.21655, "r_x3": 317.95499, "r_y3": 542.21655, "coord_origin": "TOPLEFT" }, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 561.54918, "r_x1": 404.14197, "r_y1": 561.54918, "r_x2": 404.14197, "r_y2": 553.17456, "r_x3": 317.686, "r_y3": 553.17456, "coord_origin": "TOPLEFT" }, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 226, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 616.3441799999999, "r_x1": 558.20105, "r_y1": 616.3441799999999, "r_x2": 558.20105, "r_y2": 607.96956, "r_x3": 317.95499, "r_y3": 607.96956, "coord_origin": "TOPLEFT" }, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 227, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 627.30318, "r_x1": 559.18665, "r_y1": 627.30318, "r_x2": 559.18665, "r_y2": 618.9285600000001, "r_x3": 317.95499, "r_y3": 618.9285600000001, "coord_origin": "TOPLEFT" }, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 228, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 638.26218, "r_x1": 558.20142, "r_y1": 638.26218, "r_x2": 558.20142, "r_y2": 629.88756, "r_x3": 317.62299, "r_y3": 629.88756, "coord_origin": "TOPLEFT" }, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 229, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 649.22118, "r_x1": 558.20227, "r_y1": 649.22118, "r_x2": 558.20227, "r_y2": 640.84656, "r_x3": 317.95499, "r_y3": 640.84656, "coord_origin": "TOPLEFT" }, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 230, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 660.1801800000001, "r_x1": 436.90649, "r_y1": 660.1801800000001, "r_x2": 436.90649, "r_y2": 651.80556, "r_x3": 317.95499, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 231, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 439.13800000000003, "r_y0": 660.18915, "r_x1": 469.69134999999994, "r_y1": 660.18915, "r_x2": 469.69134999999994, "r_y2": 651.8503900000001, "r_x3": 439.13800000000003, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 232, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 472.42898999999994, "r_y0": 660.1801800000001, "r_x1": 493.97348, "r_y1": 660.1801800000001, "r_x2": 493.97348, "r_y2": 651.80556, "r_x3": 472.42898999999994, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 233, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 496.207, "r_y0": 660.18915, "r_x1": 558.20001, "r_y1": 660.18915, "r_x2": 558.20001, "r_y2": 651.8503900000001, "r_x3": 496.207, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 234, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 671.13918, "r_x1": 558.20557, "r_y1": 671.13918, "r_x2": 558.20557, "r_y2": 662.76456, "r_x3": 317.95499, "r_y3": 662.76456, "coord_origin": "TOPLEFT" }, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 235, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 682.09718, "r_x1": 447.65221999999994, "r_y1": 682.09718, "r_x2": 447.65221999999994, "r_y2": 673.7225599999999, "r_x3": 317.95499, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 236, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.85999, "r_y0": 682.10614, "r_x1": 474.31439, "r_y1": 682.10614, "r_x2": 474.31439, "r_y2": 673.76739, "r_x3": 449.85999, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 237, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.172, "r_y0": 682.09718, "r_x1": 490.39655, "r_y1": 682.09718, "r_x2": 490.39655, "r_y2": 673.7225599999999, "r_x3": 477.172, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 238, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 492.60599, "r_y0": 682.10614, "r_x1": 528.29907, "r_y1": 682.10614, "r_x2": 528.29907, "r_y2": 673.76739, "r_x3": 492.60599, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 239, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 528.29901, "r_y0": 682.09718, "r_x1": 558.20148, "r_y1": 682.09718, "r_x2": 558.20148, "r_y2": 673.7225599999999, "r_x3": 528.29901, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": ", as seen", "orig": ", as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 240, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 693.0561749999999, "r_x1": 558.2041, "r_y1": 693.0561749999999, "r_x2": 558.2041, "r_y2": 684.68156, "r_x3": 317.95499, "r_y3": 684.68156, "coord_origin": "TOPLEFT" }, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on" }, { "label": "footnote", "id": 7, "page_no": 3, "cluster": { "id": 7, "label": "footnote", "bbox": { "l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT" }, "confidence": 0.9080352783203125, "cells": [ { "index": 241, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.86689, "r_x1": 369.2457, "r_y1": 708.86689, "r_x2": 369.2457, "r_y2": 702.353363, "r_x3": 317.95499, "r_y3": 702.353363, "coord_origin": "TOPLEFT" }, "text": "$^{3}$https://arxiv.org/", "orig": "$^{3}$https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{3}$https://arxiv.org/" }, { "label": "picture", "id": 2, "page_no": 3, "cluster": { "id": 2, "label": "picture", "bbox": { "l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT" }, "confidence": 0.9847871661186218, "cells": [], "children": [] }, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null } ], "body": [ { "label": "caption", "id": 8, "page_no": 3, "cluster": { "id": 8, "label": "caption", "bbox": { "l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT" }, "confidence": 0.8906616568565369, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 93.42822000000001, "r_x1": 558.48969, "r_y1": 93.42822000000001, "r_x2": 558.48969, "r_y2": 84.95495999999991, "r_x3": 53.501999, "r_y3": 84.95495999999991, "coord_origin": "TOPLEFT" }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 104.3872100000001, "r_x1": 558.20294, "r_y1": 104.3872100000001, "r_x2": 558.20294, "r_y2": 95.91394000000003, "r_x3": 53.79800000000001, "r_y3": 95.91394000000003, "coord_origin": "TOPLEFT" }, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 115.34618999999998, "r_x1": 469.84805000000006, "r_y1": 115.34618999999998, "r_x2": 469.84805000000006, "r_y2": 106.87292000000002, "r_x3": 53.79800000000001, "r_y3": 106.87292000000002, "coord_origin": "TOPLEFT" }, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges." }, { "label": "table", "id": 1, "page_no": 3, "cluster": { "id": 1, "label": "table", "bbox": { "l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT" }, "confidence": 0.9860090613365173, "cells": [ { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 233.94400000000002, "r_y0": 148.59813999999994, "r_x1": 270.04272, "r_y1": 148.59813999999994, "r_x2": 270.04272, "r_y2": 140.22351000000003, "r_x3": 233.94400000000002, "r_y3": 140.22351000000003, "coord_origin": "TOPLEFT" }, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 329.04999, "r_y0": 148.59813999999994, "r_x1": 483.3976400000001, "r_y1": 148.59813999999994, "r_x2": 483.3976400000001, "r_y2": 140.22351000000003, "r_x3": 329.04999, "r_y3": 140.22351000000003, "coord_origin": "TOPLEFT" }, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "orig": "triple inter-annotator mAP @ 0.5-0.95 (%)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 104.825, "r_y0": 159.55719, "r_x1": 141.71277, "r_y1": 159.55719, "r_x2": 141.71277, "r_y2": 151.18255999999997, "r_x3": 104.825, "r_y3": 151.18255999999997, "coord_origin": "TOPLEFT" }, "text": "class label", "orig": "class label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 175.94701, "r_y0": 159.55719, "r_x1": 198.71269, "r_y1": 159.55719, "r_x2": 198.71269, "r_y2": 151.18255999999997, "r_x3": 175.94701, "r_y3": 151.18255999999997, "coord_origin": 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159.55719, "r_x2": 295.30856, "r_y2": 151.18255999999997, "r_x3": 283.53568, "r_y3": 151.18255999999997, "coord_origin": "TOPLEFT" }, "text": "Val", "orig": "Val", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 314.01501, "r_y0": 159.55719, "r_x1": 324.98093, "r_y1": 159.55719, "r_x2": 324.98093, "r_y2": 151.18255999999997, "r_x3": 314.01501, "r_y3": 151.18255999999997, "coord_origin": "TOPLEFT" }, "text": "All", "orig": "All", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 343.01236, "r_y0": 159.55719, "r_x1": 354.65076, "r_y1": 159.55719, "r_x2": 354.65076, "r_y2": 151.18255999999997, "r_x3": 343.01236, "r_y3": 151.18255999999997, "coord_origin": "TOPLEFT" }, "text": "Fin", "orig": "Fin", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 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"r_x1": 295.64871, "r_y1": 562.48325, "r_x2": 295.64871, "r_y2": 554.00999, "r_x3": 53.79800000000001, "r_y3": 554.00999, "coord_origin": "TOPLEFT" }, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 573.44225, "r_x1": 295.64874, "r_y1": 573.44225, "r_x2": 295.64874, "r_y2": 564.96899, "r_x3": 53.79800000000001, "r_y3": 564.96899, "coord_origin": "TOPLEFT" }, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 584.40125, "r_x1": 294.04376, "r_y1": 584.40125, "r_x2": 294.04376, "r_y2": 575.92799, "r_x3": 53.79800000000001, "r_y3": 575.92799, "coord_origin": "TOPLEFT" }, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 595.36024, "r_x1": 294.04373, "r_y1": 595.36024, "r_x2": 294.04373, "r_y2": 586.88699, "r_x3": 53.79800000000001, "r_y3": 586.88699, "coord_origin": "TOPLEFT" }, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 606.31924, "r_x1": 252.78931000000003, "r_y1": 606.31924, "r_x2": 252.78931000000003, "r_y2": 597.84599, "r_x3": 53.79800000000001, "r_y3": 597.84599, "coord_origin": "TOPLEFT" }, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right." }, { "label": "text", "id": 5, "page_no": 3, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT" }, "confidence": 0.980393648147583, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 642.66617, "r_x1": 294.04745, "r_y1": 642.66617, "r_x2": 294.04745, "r_y2": 634.29155, "r_x3": 53.466999, "r_y3": 634.29155, "coord_origin": "TOPLEFT" }, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.62517, "r_x1": 294.04535, "r_y1": 653.62517, "r_x2": 294.04535, "r_y2": 645.25055, "r_x3": 53.79800000000001, "r_y3": 645.25055, "coord_origin": "TOPLEFT" }, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.58417, "r_x1": 294.04422, "r_y1": 664.58417, "r_x2": 294.04422, "r_y2": 656.20955, "r_x3": 53.79800000000001, "r_y3": 656.20955, "coord_origin": "TOPLEFT" }, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 675.54317, "r_x1": 170.58611, "r_y1": 675.54317, "r_x2": 170.58611, "r_y2": 667.16856, "r_x3": 53.466999, "r_y3": 667.16856, "coord_origin": "TOPLEFT" }, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised." }, { "label": "text", "id": 6, "page_no": 3, "cluster": { "id": 6, "label": "text", "bbox": { "l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT" }, "confidence": 0.9748398661613464, "cells": [ { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76099800000001, "r_y0": 686.48424, "r_x1": 226.72533000000004, "r_y1": 686.48424, "r_x2": 226.72533000000004, "r_y2": 678.01099, "r_x3": 63.76099800000001, "r_y3": 678.01099, "coord_origin": "TOPLEFT" }, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 229.06900000000002, "r_y0": 686.50217, "r_x1": 295.55844, "r_y1": 686.50217, "r_x2": 295.55844, "r_y2": 678.12756, "r_x3": 229.06900000000002, "r_y3": 678.12756, "coord_origin": "TOPLEFT" }, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.461174, "r_x1": 294.04538, "r_y1": 697.461174, "r_x2": 294.04538, "r_y2": 689.08656, "r_x3": 53.79800000000001, "r_y3": 689.08656, "coord_origin": "TOPLEFT" }, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.420174, "r_x1": 294.04642, "r_y1": 708.420174, "r_x2": 294.04642, "r_y2": 700.045555, "r_x3": 53.79800000000001, "r_y3": 700.045555, "coord_origin": "TOPLEFT" }, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources" }, { "label": "text", "id": 4, "page_no": 3, "cluster": { "id": 4, "label": "text", "bbox": { "l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT" }, "confidence": 0.9818483591079712, "cells": [ { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.45416000000006, "r_x1": 559.18536, "r_y1": 320.45416000000006, "r_x2": 559.18536, "r_y2": 312.07953, "r_x3": 317.95499, "r_y3": 312.07953, "coord_origin": "TOPLEFT" }, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "orig": "include publication repositories such as arXiv$^{3}$, government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.41217, "r_x1": 558.19843, "r_y1": 331.41217, "r_x2": 558.19843, "r_y2": 323.03754, "r_x3": 317.95499, "r_y3": 323.03754, "coord_origin": "TOPLEFT" }, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.37119, "r_x1": 558.36963, "r_y1": 342.37119, "r_x2": 558.36963, "r_y2": 333.99655, "r_x3": 317.95499, "r_y3": 333.99655, "coord_origin": "TOPLEFT" }, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 353.33017, "r_x1": 558.2041, "r_y1": 353.33017, "r_x2": 558.2041, "r_y2": 344.95554, "r_x3": 317.95499, "r_y3": 344.95554, "coord_origin": "TOPLEFT" }, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 364.28918, "r_x1": 558.20294, "r_y1": 364.28918, "r_x2": 558.20294, "r_y2": 355.91455, "r_x3": 317.95499, "r_y3": 355.91455, "coord_origin": "TOPLEFT" }, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 375.24817, "r_x1": 496.71826, "r_y1": 375.24817, "r_x2": 496.71826, "r_y2": 366.87354, "r_x3": 317.95499, "r_y3": 366.87354, "coord_origin": "TOPLEFT" }, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process." }, { "label": "text", "id": 0, "page_no": 3, "cluster": { "id": 0, "label": "text", "bbox": { "l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT" }, "confidence": 0.9866719841957092, "cells": [ { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 386.20717999999994, "r_x1": 558.20618, "r_y1": 386.20717999999994, "r_x2": 558.20618, "r_y2": 377.8325500000001, "r_x3": 327.918, "r_y3": 377.8325500000001, "coord_origin": "TOPLEFT" }, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 397.16617, "r_x1": 558.2019, "r_y1": 397.16617, "r_x2": 558.2019, "r_y2": 388.79153, "r_x3": 317.95499, "r_y3": 388.79153, "coord_origin": "TOPLEFT" }, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 408.12518, "r_x1": 558.20233, "r_y1": 408.12518, "r_x2": 558.20233, "r_y2": 399.75055, "r_x3": 317.95499, "r_y3": 399.75055, "coord_origin": "TOPLEFT" }, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 419.08417, "r_x1": 559.71277, "r_y1": 419.08417, "r_x2": 559.71277, "r_y2": 410.7095299999999, "r_x3": 317.95499, "r_y3": 410.7095299999999, "coord_origin": "TOPLEFT" }, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 430.04318, "r_x1": 558.20062, "r_y1": 430.04318, "r_x2": 558.20062, "r_y2": 421.66855000000004, "r_x3": 317.95499, "r_y3": 421.66855000000004, "coord_origin": "TOPLEFT" }, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 441.00217, "r_x1": 559.71307, "r_y1": 441.00217, "r_x2": 559.71307, "r_y2": 432.62753, "r_x3": 317.95499, "r_y3": 432.62753, "coord_origin": "TOPLEFT" }, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 451.96017, "r_x1": 558.36877, "r_y1": 451.96017, "r_x2": 558.36877, "r_y2": 443.58554, "r_x3": 317.95499, "r_y3": 443.58554, "coord_origin": "TOPLEFT" }, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 462.91916, "r_x1": 558.20428, "r_y1": 462.91916, "r_x2": 558.20428, "r_y2": 454.54453, "r_x3": 317.95499, "r_y3": 454.54453, "coord_origin": "TOPLEFT" }, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 473.87817, "r_x1": 558.36877, "r_y1": 473.87817, "r_x2": 558.36877, "r_y2": 465.50354, "r_x3": 317.95499, "r_y3": 465.50354, "coord_origin": "TOPLEFT" }, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 484.83716, "r_x1": 558.20428, "r_y1": 484.83716, "r_x2": 558.20428, "r_y2": 476.46252, "r_x3": 317.95499, "r_y3": 476.46252, "coord_origin": "TOPLEFT" }, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 495.79617, "r_x1": 558.5307, "r_y1": 495.79617, "r_x2": 558.5307, "r_y2": 487.42154, "r_x3": 317.95499, "r_y3": 487.42154, "coord_origin": "TOPLEFT" }, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 506.75516, "r_x1": 488.46914999999996, "r_y1": 506.75516, "r_x2": 488.46914999999996, "r_y2": 498.38052, "r_x3": 317.95499, "r_y3": 498.38052, "coord_origin": "TOPLEFT" }, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains." }, { "label": "text", "id": 3, "page_no": 3, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9847556948661804, "cells": [ { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 517.69623, "r_x1": 482.41809, "r_y1": 517.69623, "r_x2": 482.41809, "r_y2": 509.22299, "r_x3": 327.918, "r_y3": 509.22299, "coord_origin": "TOPLEFT" }, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.474, "r_y0": 517.71417, "r_x1": 559.71765, "r_y1": 517.71417, "r_x2": 559.71765, "r_y2": 509.33954, "r_x3": 484.474, "r_y3": 509.33954, "coord_origin": "TOPLEFT" }, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 528.67319, "r_x1": 559.71619, "r_y1": 528.67319, "r_x2": 559.71619, "r_y2": 520.29855, "r_x3": 317.95499, "r_y3": 520.29855, "coord_origin": "TOPLEFT" }, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 539.63217, "r_x1": 558.20239, "r_y1": 539.63217, "r_x2": 558.20239, "r_y2": 531.2575400000001, "r_x3": 317.95499, "r_y3": 531.2575400000001, "coord_origin": "TOPLEFT" }, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 550.59117, "r_x1": 559.58502, "r_y1": 550.59117, "r_x2": 559.58502, "r_y2": 542.21655, "r_x3": 317.95499, "r_y3": 542.21655, "coord_origin": "TOPLEFT" }, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 561.54918, "r_x1": 404.14197, "r_y1": 561.54918, "r_x2": 404.14197, "r_y2": 553.17456, "r_x3": 317.686, "r_y3": 553.17456, "coord_origin": "TOPLEFT" }, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 226, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 616.3441799999999, "r_x1": 558.20105, "r_y1": 616.3441799999999, "r_x2": 558.20105, "r_y2": 607.96956, "r_x3": 317.95499, "r_y3": 607.96956, "coord_origin": "TOPLEFT" }, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 227, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 627.30318, "r_x1": 559.18665, "r_y1": 627.30318, "r_x2": 559.18665, "r_y2": 618.9285600000001, "r_x3": 317.95499, "r_y3": 618.9285600000001, "coord_origin": "TOPLEFT" }, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 228, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 638.26218, "r_x1": 558.20142, "r_y1": 638.26218, "r_x2": 558.20142, "r_y2": 629.88756, "r_x3": 317.62299, "r_y3": 629.88756, "coord_origin": "TOPLEFT" }, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 229, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 649.22118, "r_x1": 558.20227, "r_y1": 649.22118, "r_x2": 558.20227, "r_y2": 640.84656, "r_x3": 317.95499, "r_y3": 640.84656, "coord_origin": "TOPLEFT" }, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 230, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 660.1801800000001, "r_x1": 436.90649, "r_y1": 660.1801800000001, "r_x2": 436.90649, "r_y2": 651.80556, "r_x3": 317.95499, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 231, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 439.13800000000003, "r_y0": 660.18915, "r_x1": 469.69134999999994, "r_y1": 660.18915, "r_x2": 469.69134999999994, "r_y2": 651.8503900000001, "r_x3": 439.13800000000003, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 232, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 472.42898999999994, "r_y0": 660.1801800000001, "r_x1": 493.97348, "r_y1": 660.1801800000001, "r_x2": 493.97348, "r_y2": 651.80556, "r_x3": 472.42898999999994, "r_y3": 651.80556, "coord_origin": "TOPLEFT" }, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 233, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 496.207, "r_y0": 660.18915, "r_x1": 558.20001, "r_y1": 660.18915, "r_x2": 558.20001, "r_y2": 651.8503900000001, "r_x3": 496.207, "r_y3": 651.8503900000001, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 234, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 671.13918, "r_x1": 558.20557, "r_y1": 671.13918, "r_x2": 558.20557, "r_y2": 662.76456, "r_x3": 317.95499, "r_y3": 662.76456, "coord_origin": "TOPLEFT" }, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 235, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 682.09718, "r_x1": 447.65221999999994, "r_y1": 682.09718, "r_x2": 447.65221999999994, "r_y2": 673.7225599999999, "r_x3": 317.95499, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 236, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 449.85999, "r_y0": 682.10614, "r_x1": 474.31439, "r_y1": 682.10614, "r_x2": 474.31439, "r_y2": 673.76739, "r_x3": 449.85999, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 237, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.172, "r_y0": 682.09718, "r_x1": 490.39655, "r_y1": 682.09718, "r_x2": 490.39655, "r_y2": 673.7225599999999, "r_x3": 477.172, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 238, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 492.60599, "r_y0": 682.10614, "r_x1": 528.29907, "r_y1": 682.10614, "r_x2": 528.29907, "r_y2": 673.76739, "r_x3": 492.60599, "r_y3": 673.76739, "coord_origin": "TOPLEFT" }, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 239, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 528.29901, "r_y0": 682.09718, "r_x1": 558.20148, "r_y1": 682.09718, "r_x2": 558.20148, "r_y2": 673.7225599999999, "r_x3": 528.29901, "r_y3": 673.7225599999999, "coord_origin": "TOPLEFT" }, "text": ", as seen", "orig": ", as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 240, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 693.0561749999999, "r_x1": 558.2041, "r_y1": 693.0561749999999, "r_x2": 558.2041, "r_y2": 684.68156, "r_x3": 317.95499, "r_y3": 684.68156, "coord_origin": "TOPLEFT" }, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on" }, { "label": "footnote", "id": 7, "page_no": 3, "cluster": { "id": 7, "label": "footnote", "bbox": { "l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT" }, "confidence": 0.9080352783203125, "cells": [ { "index": 241, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.86689, "r_x1": 369.2457, "r_y1": 708.86689, "r_x2": 369.2457, "r_y2": 702.353363, "r_x3": 317.95499, "r_y3": 702.353363, "coord_origin": "TOPLEFT" }, "text": "$^{3}$https://arxiv.org/", "orig": "$^{3}$https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "$^{3}$https://arxiv.org/" }, { "label": "picture", "id": 2, "page_no": 3, "cluster": { "id": 2, "label": "picture", "bbox": { "l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT" }, "confidence": 0.9847871661186218, "cells": [], "children": [] }, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null } ], "headers": [ { "label": "page_header", "id": 10, "page_no": 3, "cluster": { "id": 10, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.7625049948692322, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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"coord_origin": "TOPLEFT" }, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.61517000000003, "r_x1": 294.04266, "r_y1": 128.61517000000003, "r_x2": 294.04266, "r_y2": 120.24054000000001, "r_x3": 53.79800000000001, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.57317999999998, "r_x1": 295.55615, "r_y1": 139.57317999999998, "r_x2": 295.55615, "r_y2": 131.19854999999995, "r_x3": 53.79800000000001, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 150.53216999999995, "r_x1": 294.04715, "r_y1": 150.53216999999995, "r_x2": 294.04715, "r_y2": 142.15752999999995, "r_x3": 53.79800000000001, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 161.49114999999995, "r_x1": 295.55923, "r_y1": 161.49114999999995, "r_x2": 295.55923, "r_y2": 153.11652000000004, "r_x3": 53.79800000000001, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 172.4502, "r_x1": 294.04535, "r_y1": 172.4502, "r_x2": 294.04535, "r_y2": 164.07556, "r_x3": 53.79800000000001, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 183.40918, "r_x1": 294.04803, "r_y1": 183.40918, "r_x2": 294.04803, "r_y2": 175.03454999999997, "r_x3": 53.79800000000001, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 194.36816, "r_x1": 294.04709, "r_y1": 194.36816, "r_x2": 294.04709, "r_y2": 185.99352999999996, "r_x3": 53.79800000000001, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 205.32714999999996, "r_x1": 295.55612, "r_y1": 205.32714999999996, "r_x2": 295.55612, "r_y2": 196.95250999999996, "r_x3": 53.79800000000001, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 216.28619000000003, "r_x1": 294.04712, "r_y1": 216.28619000000003, "r_x2": 294.04712, "r_y2": 207.91156, "r_x3": 53.79800000000001, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 227.24518, "r_x1": 294.04495, "r_y1": 227.24518, "r_x2": 294.04495, "r_y2": 218.87054, "r_x3": 53.79800000000001, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 238.20416, "r_x1": 294.04712, "r_y1": 238.20416, "r_x2": 294.04712, "r_y2": 229.82952999999998, "r_x3": 53.79800000000001, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 249.16216999999995, "r_x1": 168.61276, "r_y1": 249.16216999999995, "r_x2": 168.61276, "r_y2": 240.78754000000004, "r_x3": 53.79800000000001, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 260.12114999999994, "r_x1": 294.21884, "r_y1": 260.12114999999994, "r_x2": 294.21884, "r_y2": 251.74652000000003, "r_x3": 63.76100199999999, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 271.0802, "r_x1": 295.56006, "r_y1": 271.0802, "r_x2": 295.56006, "r_y2": 262.70556999999997, "r_x3": 53.79800000000001, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 282.03918, "r_x1": 295.55676, "r_y1": 282.03918, "r_x2": 295.55676, "r_y2": 273.66454999999996, "r_x3": 53.79800000000001, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 292.99817, "r_x1": 294.04922, "r_y1": 292.99817, "r_x2": 294.04922, "r_y2": 284.62354, "r_x3": 53.79800000000001, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 303.95717999999994, "r_x1": 294.04343, "r_y1": 303.95717999999994, "r_x2": 294.04343, "r_y2": 295.5825500000001, "r_x3": 53.79800000000001, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 314.91617, "r_x1": 294.04712, "r_y1": 314.91617, "r_x2": 294.04712, "r_y2": 306.54153, "r_x3": 53.79800000000001, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 325.87518, "r_x1": 294.04718, "r_y1": 325.87518, "r_x2": 294.04718, "r_y2": 317.50055, "r_x3": 53.79800000000001, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 336.83417, "r_x1": 67.28347, "r_y1": 336.83417, "r_x2": 67.28347, "r_y2": 328.4595299999999, "r_x3": 53.79800000000001, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 356.88718, "r_x1": 74.626793, "r_y1": 356.88718, "r_x2": 74.626793, "r_y2": 348.51254, "r_x3": 64.708, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.963936, "r_y0": 356.88718, "r_x1": 294.0462, "r_y1": 356.88718, "r_x2": 294.0462, "r_y2": 348.51254, "r_x3": 76.963936, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 367.84616, "r_x1": 95.730484, "r_y1": 367.84616, "r_x2": 95.730484, "r_y2": 359.47153, "r_x3": 78.207001, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 97.976997, "r_y0": 367.85513, "r_x1": 130.17131, "r_y1": 367.85513, "r_x2": 130.17131, "r_y2": 359.51639, "r_x3": 97.976997, "r_y3": 359.51639, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 130.17101, "r_y0": 367.84616, "r_x1": 294.04053, "r_y1": 367.84616, "r_x2": 294.04053, "r_y2": 359.47153, "r_x3": 130.17101, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": ". This definition is different from PubLayNet", "orig": ". This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 378.80518, "r_x1": 294.04385, "r_y1": 378.80518, "r_x2": 294.04385, "r_y2": 370.43054, "r_x3": 78.207001, "r_y3": 370.43054, "coord_origin": "TOPLEFT" }, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 389.76416, "r_x1": 91.593834, "r_y1": 389.76416, "r_x2": 91.593834, "r_y2": 381.38953000000004, "r_x3": 78.207001, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 93.834999, "r_y0": 389.77313, "r_x1": 106.88111, "r_y1": 389.77313, "r_x2": 106.88111, "r_y2": 381.43439000000006, "r_x3": 93.834999, "r_y3": 381.43439000000006, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 109.629, "r_y0": 389.76416, "r_x1": 133.72173, "r_y1": 389.76416, "r_x2": 133.72173, "r_y2": 381.38953000000004, "r_x3": 109.629, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.707993, "r_y0": 400.72313999999994, "r_x1": 75.097656, "r_y1": 400.72313999999994, "r_x2": 75.097656, "r_y2": 392.34851, "r_x3": 64.707993, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.545731, "r_y0": 400.72313999999994, "r_x1": 84.351402, "r_y1": 400.72313999999994, "r_x2": 84.351402, "r_y2": 392.34851, "r_x3": 77.545731, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 86.584, "r_y0": 400.73215, "r_x1": 118.01329000000001, "r_y1": 400.73215, "r_x2": 118.01329000000001, "r_y2": 392.39339999999993, "r_x3": 86.584, "r_y3": 392.39339999999993, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 120.26899999999999, "r_y0": 400.72317999999996, "r_x1": 295.55695, "r_y1": 400.72317999999996, "r_x2": 295.55695, "r_y2": 392.34854, "r_x3": 120.26899999999999, "r_y3": 392.34854, "coord_origin": "TOPLEFT" }, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 411.68216, "r_x1": 181.80978, "r_y1": 411.68216, "r_x2": 181.80978, "r_y2": 403.30753, "r_x3": 78.207001, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 184.175, "r_y0": 411.69113, "r_x1": 216.68806000000004, "r_y1": 411.69113, "r_x2": 216.68806000000004, "r_y2": 403.35239, "r_x3": 184.175, "r_y3": 403.35239, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.078, "r_y0": 411.68216, "r_x1": 295.56372, "r_y1": 411.68216, "r_x2": 295.56372, "r_y2": 403.30753, "r_x3": 219.078, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 422.64116999999993, "r_x1": 294.04617, "r_y1": 422.64116999999993, "r_x2": 294.04617, "r_y2": 414.2665400000001, "r_x3": 78.207001, "r_y3": 414.2665400000001, "coord_origin": "TOPLEFT" }, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 433.60016, "r_x1": 192.00853, "r_y1": 433.60016, "r_x2": 192.00853, "r_y2": 425.22552, "r_x3": 78.207001, "r_y3": 425.22552, "coord_origin": "TOPLEFT" }, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 444.55914, "r_x1": 74.483009, "r_y1": 444.55914, "r_x2": 74.483009, "r_y2": 436.18451000000005, "r_x3": 64.708, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.786255, "r_y0": 444.55914, "r_x1": 112.61566, "r_y1": 444.55914, "r_x2": 112.61566, "r_y2": 436.18451000000005, "r_x3": 76.786255, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 114.861, "r_y0": 444.56815000000006, "r_x1": 142.61249, "r_y1": 444.56815000000006, "r_x2": 142.61249, "r_y2": 436.2294, "r_x3": 114.861, "r_y3": 436.2294, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.612, "r_y0": 444.55917, "r_x1": 294.04724, "r_y1": 444.55917, "r_x2": 294.04724, "r_y2": 436.18454, "r_x3": 142.612, "r_y3": 436.18454, "coord_origin": "TOPLEFT" }, "text": ", there must be exactly one corresponding", "orig": ", there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 455.52713, "r_x1": 102.79287, "r_y1": 455.52713, "r_x2": 102.79287, "r_y2": 447.18839, "r_x3": 78.207001, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 105.245, "r_y0": 455.51815999999997, "r_x1": 113.09956999999999, "r_y1": 455.51815999999997, "r_x2": 113.09956999999999, "r_y2": 447.14352, "r_x3": 105.245, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 115.341, "r_y0": 455.52713, "r_x1": 134.40356, "r_y1": 455.52713, "r_x2": 134.40356, "r_y2": 447.18839, "r_x3": 115.341, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.403, "r_y0": 455.51815999999997, "r_x1": 136.37561, "r_y1": 455.51815999999997, "r_x2": 136.37561, "r_y2": 447.14352, "r_x3": 134.403, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 466.47617, "r_x1": 74.220215, "r_y1": 466.47617, "r_x2": 74.220215, "r_y2": 458.1015300000001, "r_x3": 64.708, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.461555, "r_y0": 466.47617, "r_x1": 267.46786, "r_y1": 466.47617, "r_x2": 267.46786, "r_y2": 458.1015300000001, "r_x3": 76.461555, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 269.70599, "r_y0": 466.48514, "r_x1": 294.04599, "r_y1": 466.48514, "r_x2": 294.04599, "r_y2": 458.14639, "r_x3": 269.70599, "r_y3": 458.14639, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 477.43518, "r_x1": 102.29972, "r_y1": 477.43518, "r_x2": 102.29972, "r_y2": 469.06055, "r_x3": 78.207001, "r_y3": 469.06055, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 488.39417, "r_x1": 74.345383, "r_y1": 488.39417, "r_x2": 74.345383, "r_y2": 480.01953, "r_x3": 64.708, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.616203, "r_y0": 488.39417, "r_x1": 206.13503, "r_y1": 488.39417, "r_x2": 206.13503, "r_y2": 480.01953, "r_x3": 76.616203, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 208.38, "r_y0": 488.40314, "r_x1": 238.12155, "r_y1": 488.40314, "r_x2": 238.12155, "r_y2": 480.06439, "r_x3": 208.38, "r_y3": 480.06439, "coord_origin": "TOPLEFT" }, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 240.41300999999999, "r_y0": 488.39417, "r_x1": 264.50571, "r_y1": 488.39417, "r_x2": 264.50571, "r_y2": 480.01953, "r_x3": 240.41300999999999, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708008, "r_y0": 499.35315, "r_x1": 74.564522, "r_y1": 499.35315, "r_x2": 74.564522, "r_y2": 490.97852, "r_x3": 64.708008, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.886978, "r_y0": 499.35315, "r_x1": 294.04617, "r_y1": 499.35315, "r_x2": 294.04617, "r_y2": 490.97852, "r_x3": 76.886978, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 510.31216, "r_x1": 200.34819, "r_y1": 510.31216, "r_x2": 200.34819, "r_y2": 501.93753, "r_x3": 78.207001, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 203.66701, "r_y0": 510.32114, "r_x1": 256.57504, "r_y1": 510.32114, "r_x2": 256.57504, "r_y2": 501.98239, "r_x3": 203.66701, "r_y3": 501.98239, "coord_origin": "TOPLEFT" }, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 256.57401, "r_y0": 510.31216, "r_x1": 294.04401, "r_y1": 510.31216, "r_x2": 294.04401, "r_y2": 501.93753, "r_x3": 256.57401, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": ", unless it", "orig": ", unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 521.27118, "r_x1": 208.13017, "r_y1": 521.27118, "r_x2": 208.13017, "r_y2": 512.8965499999999, "r_x3": 78.207001, "r_y3": 512.8965499999999, "coord_origin": "TOPLEFT" }, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 541.32516, "r_x1": 294.04337, "r_y1": 541.32516, "r_x2": 294.04337, "r_y2": 532.9505300000001, "r_x3": 53.528999, "r_y3": 532.9505300000001, "coord_origin": "TOPLEFT" }, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.28317, "r_x1": 295.56253, "r_y1": 552.28317, "r_x2": 295.56253, "r_y2": 543.90855, "r_x3": 53.79800000000001, "r_y3": 543.90855, "coord_origin": "TOPLEFT" }, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.24217, "r_x1": 294.04538, "r_y1": 563.24217, "r_x2": 294.04538, "r_y2": 554.8675499999999, "r_x3": 53.79800000000001, "r_y3": 554.8675499999999, "coord_origin": "TOPLEFT" }, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.20117, "r_x1": 127.2418, "r_y1": 574.20117, "r_x2": 127.2418, "r_y2": 565.82655, "r_x3": 53.79800000000001, "r_y3": 565.82655, "coord_origin": "TOPLEFT" }, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 585.14224, "r_x1": 136.7744, "r_y1": 585.14224, "r_x2": 136.7744, "r_y2": 576.66899, "r_x3": 63.76100199999999, "r_y3": 576.66899, "coord_origin": "TOPLEFT" }, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 139.008, "r_y0": 585.16017, "r_x1": 295.56226, "r_y1": 585.16017, "r_x2": 295.56226, "r_y2": 576.7855500000001, "r_x3": 139.008, "r_y3": 576.7855500000001, "coord_origin": "TOPLEFT" }, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 596.1191699999999, "r_x1": 294.04532, "r_y1": 596.1191699999999, "r_x2": 294.04532, "r_y2": 587.74455, "r_x3": 53.79800000000001, "r_y3": 587.74455, "coord_origin": "TOPLEFT" }, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 607.07817, "r_x1": 295.55676, "r_y1": 607.07817, "r_x2": 295.55676, "r_y2": 598.70355, "r_x3": 53.79800000000001, "r_y3": 598.70355, "coord_origin": "TOPLEFT" }, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 618.0371700000001, "r_x1": 294.04605, "r_y1": 618.0371700000001, "r_x2": 294.04605, "r_y2": 609.66255, "r_x3": 53.79800000000001, "r_y3": 609.66255, "coord_origin": "TOPLEFT" }, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 628.99617, "r_x1": 295.42377, "r_y1": 628.99617, "r_x2": 295.42377, "r_y2": 620.6215500000001, "r_x3": 53.79800000000001, "r_y3": 620.6215500000001, "coord_origin": "TOPLEFT" }, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.95517, "r_x1": 294.04712, "r_y1": 639.95517, "r_x2": 294.04712, "r_y2": 631.58055, "r_x3": 53.79800000000001, "r_y3": 631.58055, "coord_origin": "TOPLEFT" }, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.91417, "r_x1": 294.04922, "r_y1": 650.91417, "r_x2": 294.04922, "r_y2": 642.53955, "r_x3": 53.79800000000001, "r_y3": 642.53955, "coord_origin": "TOPLEFT" }, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.87317, "r_x1": 295.55618, "r_y1": 661.87317, "r_x2": 295.55618, "r_y2": 653.49855, "r_x3": 53.79800000000001, "r_y3": 653.49855, "coord_origin": "TOPLEFT" }, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.83117, "r_x1": 294.04874, "r_y1": 672.83117, "r_x2": 294.04874, "r_y2": 664.45655, "r_x3": 53.79800000000001, "r_y3": 664.45655, "coord_origin": "TOPLEFT" }, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.79017, "r_x1": 295.42496, "r_y1": 683.79017, "r_x2": 295.42496, "r_y2": 675.4155499999999, "r_x3": 53.79800000000001, "r_y3": 675.4155499999999, "coord_origin": "TOPLEFT" }, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.749176, "r_x1": 295.0274, "r_y1": 694.749176, "r_x2": 295.0274, "r_y2": 686.37456, "r_x3": 53.79800000000001, "r_y3": 686.37456, "coord_origin": "TOPLEFT" }, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.708176, "r_x1": 294.04114, "r_y1": 705.708176, "r_x2": 294.04114, "r_y2": 697.333557, "r_x3": 53.79800000000001, "r_y3": 697.333557, "coord_origin": "TOPLEFT" }, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 340.00214, "r_y0": 181.90972999999997, "r_x1": 416.20551, "r_y1": 181.90972999999997, "r_x2": 416.20551, "r_y2": 179.79296999999997, "r_x3": 340.00214, "r_y3": 179.79296999999997, "coord_origin": "TOPLEFT" }, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 339.38269, "r_y0": 92.28399999999999, "r_x1": 417.83722, "r_y1": 92.28399999999999, "r_x2": 417.83722, "r_y2": 85.19066999999995, "r_x3": 339.38269, "r_y3": 85.19066999999995, "coord_origin": "TOPLEFT" }, "text": "Compliant with guidelines", "orig": "Compliant with guidelines", "text_direction": 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"rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 481.96722, "r_x1": 558.3891, "r_y1": 481.96722, "r_x2": 558.3891, "r_y2": 473.49399, "r_x3": 317.95499, "r_y3": 473.49399, "coord_origin": "TOPLEFT" }, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 492.92621, "r_x1": 559.80579, "r_y1": 492.92621, "r_x2": 559.80579, "r_y2": 484.45297, "r_x3": 317.95499, "r_y3": 484.45297, "coord_origin": "TOPLEFT" }, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 503.88519, "r_x1": 544.14148, "r_y1": 503.88519, "r_x2": 544.14148, "r_y2": 495.41196, "r_x3": 317.95499, "r_y3": 495.41196, "coord_origin": "TOPLEFT" }, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 533.87216, "r_x1": 558.20435, "r_y1": 533.87216, "r_x2": 558.20435, "r_y2": 525.49753, "r_x3": 317.62299, "r_y3": 525.49753, "coord_origin": "TOPLEFT" }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 544.83118, "r_x1": 326.07019, "r_y1": 544.83118, "r_x2": 326.07019, "r_y2": 536.45656, "r_x3": 317.74899, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "40", "orig": "40", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.3071, "r_y0": 544.83118, "r_x1": 509.11896, "r_y1": 544.83118, "r_x2": 509.11896, "r_y2": 536.45656, "r_x3": 328.3071, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "initially allocated annotators did not pass the bar.", "orig": "initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 555.77225, "r_x1": 456.80109000000004, "r_y1": 555.77225, "r_x2": 456.80109000000004, "r_y2": 547.299, "r_x3": 327.918, "r_y3": 547.299, "coord_origin": "TOPLEFT" }, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 458.7120100000001, "r_y0": 555.79018, "r_x1": 558.48926, "r_y1": 555.79018, "r_x2": 558.48926, "r_y2": 547.41556, "r_x3": 458.7120100000001, "r_y3": 547.41556, "coord_origin": "TOPLEFT" }, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.74918, "r_x1": 559.71368, "r_y1": 566.74918, "r_x2": 559.71368, "r_y2": 558.37456, "r_x3": 317.95499, "r_y3": 558.37456, "coord_origin": "TOPLEFT" }, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.7081800000001, "r_x1": 559.58124, "r_y1": 577.7081800000001, "r_x2": 559.58124, "r_y2": 569.33356, "r_x3": 317.95499, "r_y3": 569.33356, "coord_origin": "TOPLEFT" }, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 588.66718, "r_x1": 558.20386, "r_y1": 588.66718, "r_x2": 558.20386, "r_y2": 580.29256, "r_x3": 317.64099, "r_y3": 580.29256, "coord_origin": "TOPLEFT" }, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 599.62617, "r_x1": 558.20221, "r_y1": 599.62617, "r_x2": 558.20221, "r_y2": 591.25156, "r_x3": 317.95499, "r_y3": 591.25156, "coord_origin": "TOPLEFT" }, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 610.58417, "r_x1": 559.18457, "r_y1": 610.58417, "r_x2": 559.18457, "r_y2": 602.20955, "r_x3": 317.95499, "r_y3": 602.20955, "coord_origin": "TOPLEFT" }, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 621.54317, "r_x1": 558.20197, "r_y1": 621.54317, "r_x2": 558.20197, "r_y2": 613.16855, "r_x3": 317.95499, "r_y3": 613.16855, "coord_origin": "TOPLEFT" }, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 632.50217, "r_x1": 559.58197, "r_y1": 632.50217, "r_x2": 559.58197, "r_y2": 624.12755, "r_x3": 317.95499, "r_y3": 624.12755, "coord_origin": "TOPLEFT" }, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 643.46117, "r_x1": 559.71411, "r_y1": 643.46117, "r_x2": 559.71411, "r_y2": 635.08655, "r_x3": 317.95499, "r_y3": 635.08655, "coord_origin": "TOPLEFT" }, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 654.42017, "r_x1": 558.20557, "r_y1": 654.42017, "r_x2": 558.20557, "r_y2": 646.04555, "r_x3": 317.95499, "r_y3": 646.04555, "coord_origin": "TOPLEFT" }, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 665.3791699999999, "r_x1": 559.7149, "r_y1": 665.3791699999999, "r_x2": 559.7149, "r_y2": 657.00455, "r_x3": 317.95499, "r_y3": 657.00455, "coord_origin": "TOPLEFT" }, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 676.33817, "r_x1": 558.19849, "r_y1": 676.33817, "r_x2": 558.19849, "r_y2": 667.9635499999999, "r_x3": 317.95499, "r_y3": 667.9635499999999, "coord_origin": "TOPLEFT" }, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 687.29717, "r_x1": 558.43268, "r_y1": 687.29717, "r_x2": 558.43268, "r_y2": 678.92255, "r_x3": 317.62299, "r_y3": 678.92255, "coord_origin": "TOPLEFT" }, "text": "were not able to see each other\u2019s annotations. This was enforced by", "orig": "were not able to see each other\u2019s annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 698.256172, "r_x1": 558.19806, "r_y1": 698.256172, "r_x2": 558.19806, "r_y2": 689.8815500000001, "r_x3": 317.95499, "r_y3": 689.8815500000001, "coord_origin": "TOPLEFT" }, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 709.2151719999999, "r_x1": 558.20227, "r_y1": 709.2151719999999, "r_x2": 558.20227, "r_y2": 700.840553, "r_x3": 317.95499, "r_y3": 700.840553, "coord_origin": "TOPLEFT" }, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "has_chars": false, "has_words": false, "has_lines": true, "image": null, "lines": [] }, "predictions": { "layout": { "clusters": [ { "id": 13, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8877827525138855, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 15, "label": "page_header", "bbox": { "l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8534670472145081, "cells": [ { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 12, "label": "text", "bbox": { "l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9022762179374695, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 95.73816, "r_x1": 294.04541, "r_y1": 95.73816, "r_x2": 294.04541, "r_y2": 87.36352999999997, "r_x3": 53.79800000000001, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.69713999999999, "r_x1": 188.1326, "r_y1": 106.69713999999999, "r_x2": 188.1326, "r_y2": 98.32250999999997, "r_x3": 53.79800000000001, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 190.37401, "r_y0": 106.70612000000006, "r_x1": 251.25586999999996, "r_y1": 106.70612000000006, "r_x2": 251.25586999999996, "r_y2": 98.36737000000005, "r_x3": 190.37401, "r_y3": 98.36737000000005, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.70801, "r_y0": 106.69713999999999, "r_x1": 287.01816, "r_y1": 106.69713999999999, "r_x2": 287.01816, "r_y2": 98.32250999999997, "r_x3": 253.70801, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 4, "label": "text", "bbox": { "l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9711344838142395, "cells": [ { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 117.65618999999992, "r_x1": 294.04257, "r_y1": 117.65618999999992, "r_x2": 294.04257, "r_y2": 109.28156000000001, "r_x3": 63.76100199999999, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.61517000000003, "r_x1": 294.04266, "r_y1": 128.61517000000003, "r_x2": 294.04266, "r_y2": 120.24054000000001, "r_x3": 53.79800000000001, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.57317999999998, "r_x1": 295.55615, "r_y1": 139.57317999999998, "r_x2": 295.55615, "r_y2": 131.19854999999995, "r_x3": 53.79800000000001, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 150.53216999999995, "r_x1": 294.04715, "r_y1": 150.53216999999995, "r_x2": 294.04715, "r_y2": 142.15752999999995, "r_x3": 53.79800000000001, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 161.49114999999995, "r_x1": 295.55923, "r_y1": 161.49114999999995, "r_x2": 295.55923, "r_y2": 153.11652000000004, "r_x3": 53.79800000000001, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 172.4502, "r_x1": 294.04535, "r_y1": 172.4502, "r_x2": 294.04535, "r_y2": 164.07556, "r_x3": 53.79800000000001, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 183.40918, "r_x1": 294.04803, "r_y1": 183.40918, "r_x2": 294.04803, "r_y2": 175.03454999999997, "r_x3": 53.79800000000001, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 194.36816, "r_x1": 294.04709, "r_y1": 194.36816, "r_x2": 294.04709, "r_y2": 185.99352999999996, "r_x3": 53.79800000000001, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 205.32714999999996, "r_x1": 295.55612, "r_y1": 205.32714999999996, "r_x2": 295.55612, "r_y2": 196.95250999999996, "r_x3": 53.79800000000001, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 216.28619000000003, "r_x1": 294.04712, "r_y1": 216.28619000000003, "r_x2": 294.04712, "r_y2": 207.91156, "r_x3": 53.79800000000001, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 227.24518, "r_x1": 294.04495, "r_y1": 227.24518, "r_x2": 294.04495, "r_y2": 218.87054, "r_x3": 53.79800000000001, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 238.20416, "r_x1": 294.04712, "r_y1": 238.20416, "r_x2": 294.04712, "r_y2": 229.82952999999998, "r_x3": 53.79800000000001, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 249.16216999999995, "r_x1": 168.61276, "r_y1": 249.16216999999995, "r_x2": 168.61276, "r_y2": 240.78754000000004, "r_x3": 53.79800000000001, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT" }, "confidence": 0.985905110836029, "cells": [ { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 260.12114999999994, "r_x1": 294.21884, "r_y1": 260.12114999999994, "r_x2": 294.21884, "r_y2": 251.74652000000003, "r_x3": 63.76100199999999, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 271.0802, "r_x1": 295.56006, "r_y1": 271.0802, "r_x2": 295.56006, "r_y2": 262.70556999999997, "r_x3": 53.79800000000001, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 282.03918, "r_x1": 295.55676, "r_y1": 282.03918, "r_x2": 295.55676, "r_y2": 273.66454999999996, "r_x3": 53.79800000000001, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 292.99817, "r_x1": 294.04922, "r_y1": 292.99817, "r_x2": 294.04922, "r_y2": 284.62354, "r_x3": 53.79800000000001, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 303.95717999999994, "r_x1": 294.04343, "r_y1": 303.95717999999994, "r_x2": 294.04343, "r_y2": 295.5825500000001, "r_x3": 53.79800000000001, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 314.91617, "r_x1": 294.04712, "r_y1": 314.91617, "r_x2": 294.04712, "r_y2": 306.54153, "r_x3": 53.79800000000001, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 325.87518, "r_x1": 294.04718, "r_y1": 325.87518, "r_x2": 294.04718, "r_y2": 317.50055, "r_x3": 53.79800000000001, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 336.83417, "r_x1": 67.28347, "r_y1": 336.83417, "r_x2": 67.28347, "r_y2": 328.4595299999999, "r_x3": 53.79800000000001, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "list_item", "bbox": { "l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT" }, "confidence": 0.9691526293754578, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 356.88718, "r_x1": 74.626793, "r_y1": 356.88718, "r_x2": 74.626793, "r_y2": 348.51254, "r_x3": 64.708, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.963936, "r_y0": 356.88718, "r_x1": 294.0462, "r_y1": 356.88718, "r_x2": 294.0462, "r_y2": 348.51254, "r_x3": 76.963936, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 367.84616, "r_x1": 95.730484, "r_y1": 367.84616, "r_x2": 95.730484, "r_y2": 359.47153, "r_x3": 78.207001, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 97.976997, "r_y0": 367.85513, "r_x1": 130.17131, "r_y1": 367.85513, "r_x2": 130.17131, "r_y2": 359.51639, "r_x3": 97.976997, "r_y3": 359.51639, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 130.17101, "r_y0": 367.84616, "r_x1": 294.04053, "r_y1": 367.84616, "r_x2": 294.04053, "r_y2": 359.47153, "r_x3": 130.17101, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": ". This definition is different from PubLayNet", "orig": ". This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 378.80518, "r_x1": 294.04385, "r_y1": 378.80518, "r_x2": 294.04385, "r_y2": 370.43054, "r_x3": 78.207001, "r_y3": 370.43054, "coord_origin": "TOPLEFT" }, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 389.76416, "r_x1": 91.593834, "r_y1": 389.76416, "r_x2": 91.593834, "r_y2": 381.38953000000004, "r_x3": 78.207001, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 93.834999, "r_y0": 389.77313, "r_x1": 106.88111, "r_y1": 389.77313, "r_x2": 106.88111, "r_y2": 381.43439000000006, "r_x3": 93.834999, "r_y3": 381.43439000000006, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 109.629, "r_y0": 389.76416, "r_x1": 133.72173, "r_y1": 389.76416, "r_x2": 133.72173, "r_y2": 381.38953000000004, "r_x3": 109.629, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 7, "label": "list_item", "bbox": { "l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT" }, "confidence": 0.9591284990310669, "cells": [ { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.707993, "r_y0": 400.72313999999994, "r_x1": 75.097656, "r_y1": 400.72313999999994, "r_x2": 75.097656, "r_y2": 392.34851, "r_x3": 64.707993, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.545731, "r_y0": 400.72313999999994, "r_x1": 84.351402, "r_y1": 400.72313999999994, "r_x2": 84.351402, "r_y2": 392.34851, "r_x3": 77.545731, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 86.584, "r_y0": 400.73215, "r_x1": 118.01329000000001, "r_y1": 400.73215, "r_x2": 118.01329000000001, "r_y2": 392.39339999999993, "r_x3": 86.584, "r_y3": 392.39339999999993, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 120.26899999999999, "r_y0": 400.72317999999996, "r_x1": 295.55695, "r_y1": 400.72317999999996, "r_x2": 295.55695, "r_y2": 392.34854, "r_x3": 120.26899999999999, "r_y3": 392.34854, "coord_origin": "TOPLEFT" }, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 411.68216, "r_x1": 181.80978, "r_y1": 411.68216, "r_x2": 181.80978, "r_y2": 403.30753, "r_x3": 78.207001, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 184.175, "r_y0": 411.69113, "r_x1": 216.68806000000004, "r_y1": 411.69113, "r_x2": 216.68806000000004, "r_y2": 403.35239, "r_x3": 184.175, "r_y3": 403.35239, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.078, "r_y0": 411.68216, "r_x1": 295.56372, "r_y1": 411.68216, "r_x2": 295.56372, "r_y2": 403.30753, "r_x3": 219.078, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 422.64116999999993, "r_x1": 294.04617, "r_y1": 422.64116999999993, "r_x2": 294.04617, "r_y2": 414.2665400000001, "r_x3": 78.207001, "r_y3": 414.2665400000001, "coord_origin": "TOPLEFT" }, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 433.60016, "r_x1": 192.00853, "r_y1": 433.60016, "r_x2": 192.00853, "r_y2": 425.22552, "r_x3": 78.207001, "r_y3": 425.22552, "coord_origin": "TOPLEFT" }, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT" }, "confidence": 0.9413429498672485, "cells": [ { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 444.55914, "r_x1": 74.483009, "r_y1": 444.55914, "r_x2": 74.483009, "r_y2": 436.18451000000005, "r_x3": 64.708, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.786255, "r_y0": 444.55914, "r_x1": 112.61566, "r_y1": 444.55914, "r_x2": 112.61566, "r_y2": 436.18451000000005, "r_x3": 76.786255, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 114.861, "r_y0": 444.56815000000006, "r_x1": 142.61249, "r_y1": 444.56815000000006, "r_x2": 142.61249, "r_y2": 436.2294, "r_x3": 114.861, "r_y3": 436.2294, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.612, "r_y0": 444.55917, "r_x1": 294.04724, "r_y1": 444.55917, "r_x2": 294.04724, "r_y2": 436.18454, "r_x3": 142.612, "r_y3": 436.18454, "coord_origin": "TOPLEFT" }, "text": ", there must be exactly one corresponding", "orig": ", there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 455.52713, "r_x1": 102.79287, "r_y1": 455.52713, "r_x2": 102.79287, "r_y2": 447.18839, "r_x3": 78.207001, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 105.245, "r_y0": 455.51815999999997, "r_x1": 113.09956999999999, "r_y1": 455.51815999999997, "r_x2": 113.09956999999999, "r_y2": 447.14352, "r_x3": 105.245, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 115.341, "r_y0": 455.52713, "r_x1": 134.40356, "r_y1": 455.52713, "r_x2": 134.40356, "r_y2": 447.18839, "r_x3": 115.341, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.403, "r_y0": 455.51815999999997, "r_x1": 136.37561, "r_y1": 455.51815999999997, "r_x2": 136.37561, "r_y2": 447.14352, "r_x3": 134.403, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT" }, "confidence": 0.9426738023757935, "cells": [ { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 466.47617, "r_x1": 74.220215, "r_y1": 466.47617, "r_x2": 74.220215, "r_y2": 458.1015300000001, "r_x3": 64.708, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.461555, "r_y0": 466.47617, "r_x1": 267.46786, "r_y1": 466.47617, "r_x2": 267.46786, "r_y2": 458.1015300000001, "r_x3": 76.461555, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 269.70599, "r_y0": 466.48514, "r_x1": 294.04599, "r_y1": 466.48514, "r_x2": 294.04599, "r_y2": 458.14639, "r_x3": 269.70599, "r_y3": 458.14639, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 477.43518, "r_x1": 102.29972, "r_y1": 477.43518, "r_x2": 102.29972, "r_y2": 469.06055, "r_x3": 78.207001, "r_y3": 469.06055, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT" }, "confidence": 0.9147457480430603, "cells": [ { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 488.39417, "r_x1": 74.345383, "r_y1": 488.39417, "r_x2": 74.345383, "r_y2": 480.01953, "r_x3": 64.708, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.616203, "r_y0": 488.39417, "r_x1": 206.13503, "r_y1": 488.39417, "r_x2": 206.13503, "r_y2": 480.01953, "r_x3": 76.616203, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 208.38, "r_y0": 488.40314, "r_x1": 238.12155, "r_y1": 488.40314, "r_x2": 238.12155, "r_y2": 480.06439, "r_x3": 208.38, "r_y3": 480.06439, "coord_origin": "TOPLEFT" }, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 240.41300999999999, "r_y0": 488.39417, "r_x1": 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"r_x3": 76.886978, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 510.31216, "r_x1": 200.34819, "r_y1": 510.31216, "r_x2": 200.34819, "r_y2": 501.93753, "r_x3": 78.207001, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 203.66701, "r_y0": 510.32114, "r_x1": 256.57504, "r_y1": 510.32114, "r_x2": 256.57504, "r_y2": 501.98239, "r_x3": 203.66701, "r_y3": 501.98239, "coord_origin": "TOPLEFT" }, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 256.57401, "r_y0": 510.31216, "r_x1": 294.04401, "r_y1": 510.31216, "r_x2": 294.04401, "r_y2": 501.93753, "r_x3": 256.57401, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": ", unless it", "orig": ", unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 521.27118, "r_x1": 208.13017, "r_y1": 521.27118, "r_x2": 208.13017, "r_y2": 512.8965499999999, "r_x3": 78.207001, "r_y3": 512.8965499999999, "coord_origin": "TOPLEFT" }, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 53.528999, "t": 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Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.24217, "r_x1": 294.04538, "r_y1": 563.24217, "r_x2": 294.04538, "r_y2": 554.8675499999999, "r_x3": 53.79800000000001, "r_y3": 554.8675499999999, "coord_origin": "TOPLEFT" }, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.20117, "r_x1": 127.2418, "r_y1": 574.20117, "r_x2": 127.2418, "r_y2": 565.82655, "r_x3": 53.79800000000001, "r_y3": 565.82655, "coord_origin": "TOPLEFT" }, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT" }, "confidence": 0.9864389896392822, "cells": [ { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 585.14224, "r_x1": 136.7744, "r_y1": 585.14224, "r_x2": 136.7744, "r_y2": 576.66899, "r_x3": 63.76100199999999, "r_y3": 576.66899, "coord_origin": "TOPLEFT" }, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 139.008, "r_y0": 585.16017, "r_x1": 295.56226, "r_y1": 585.16017, "r_x2": 295.56226, "r_y2": 576.7855500000001, "r_x3": 139.008, "r_y3": 576.7855500000001, "coord_origin": "TOPLEFT" }, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 596.1191699999999, "r_x1": 294.04532, "r_y1": 596.1191699999999, "r_x2": 294.04532, "r_y2": 587.74455, "r_x3": 53.79800000000001, "r_y3": 587.74455, "coord_origin": "TOPLEFT" }, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 607.07817, "r_x1": 295.55676, "r_y1": 607.07817, "r_x2": 295.55676, "r_y2": 598.70355, "r_x3": 53.79800000000001, "r_y3": 598.70355, "coord_origin": "TOPLEFT" }, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 618.0371700000001, "r_x1": 294.04605, "r_y1": 618.0371700000001, "r_x2": 294.04605, "r_y2": 609.66255, "r_x3": 53.79800000000001, "r_y3": 609.66255, "coord_origin": "TOPLEFT" }, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 628.99617, "r_x1": 295.42377, "r_y1": 628.99617, "r_x2": 295.42377, "r_y2": 620.6215500000001, "r_x3": 53.79800000000001, "r_y3": 620.6215500000001, "coord_origin": "TOPLEFT" }, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.95517, "r_x1": 294.04712, "r_y1": 639.95517, "r_x2": 294.04712, "r_y2": 631.58055, "r_x3": 53.79800000000001, "r_y3": 631.58055, "coord_origin": "TOPLEFT" }, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.91417, "r_x1": 294.04922, "r_y1": 650.91417, "r_x2": 294.04922, "r_y2": 642.53955, "r_x3": 53.79800000000001, "r_y3": 642.53955, "coord_origin": "TOPLEFT" }, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.87317, "r_x1": 295.55618, "r_y1": 661.87317, "r_x2": 295.55618, "r_y2": 653.49855, "r_x3": 53.79800000000001, "r_y3": 653.49855, "coord_origin": "TOPLEFT" }, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.83117, "r_x1": 294.04874, "r_y1": 672.83117, "r_x2": 294.04874, "r_y2": 664.45655, "r_x3": 53.79800000000001, "r_y3": 664.45655, "coord_origin": "TOPLEFT" }, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.79017, "r_x1": 295.42496, "r_y1": 683.79017, "r_x2": 295.42496, "r_y2": 675.4155499999999, "r_x3": 53.79800000000001, "r_y3": 675.4155499999999, "coord_origin": "TOPLEFT" }, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.749176, "r_x1": 295.0274, "r_y1": 694.749176, "r_x2": 295.0274, "r_y2": 686.37456, "r_x3": 53.79800000000001, "r_y3": 686.37456, "coord_origin": "TOPLEFT" }, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.708176, "r_x1": 294.04114, "r_y1": 705.708176, "r_x2": 294.04114, "r_y2": 697.333557, "r_x3": 53.79800000000001, "r_y3": 697.333557, "coord_origin": "TOPLEFT" }, "text": "staff were admitted into the production phase. 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"g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 481.96722, "r_x1": 558.3891, "r_y1": 481.96722, "r_x2": 558.3891, "r_y2": 473.49399, "r_x3": 317.95499, "r_y3": 473.49399, "coord_origin": "TOPLEFT" }, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 492.92621, "r_x1": 559.80579, "r_y1": 492.92621, "r_x2": 559.80579, "r_y2": 484.45297, "r_x3": 317.95499, "r_y3": 484.45297, "coord_origin": "TOPLEFT" }, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 503.88519, "r_x1": 544.14148, "r_y1": 503.88519, "r_x2": 544.14148, "r_y2": 495.41196, "r_x3": 317.95499, "r_y3": 495.41196, "coord_origin": "TOPLEFT" }, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 6, "label": "text", "bbox": { "l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT" }, "confidence": 0.9611718654632568, "cells": [ { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 533.87216, "r_x1": 558.20435, "r_y1": 533.87216, "r_x2": 558.20435, "r_y2": 525.49753, "r_x3": 317.62299, "r_y3": 525.49753, "coord_origin": "TOPLEFT" }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 544.83118, "r_x1": 326.07019, "r_y1": 544.83118, "r_x2": 326.07019, "r_y2": 536.45656, "r_x3": 317.74899, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "40", "orig": "40", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.3071, "r_y0": 544.83118, "r_x1": 509.11896, "r_y1": 544.83118, "r_x2": 509.11896, "r_y2": 536.45656, "r_x3": 328.3071, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "initially allocated annotators did not pass the bar.", "orig": "initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9829543232917786, "cells": [ { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 555.77225, "r_x1": 456.80109000000004, "r_y1": 555.77225, "r_x2": 456.80109000000004, "r_y2": 547.299, "r_x3": 327.918, "r_y3": 547.299, "coord_origin": "TOPLEFT" }, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 458.7120100000001, "r_y0": 555.79018, "r_x1": 558.48926, "r_y1": 555.79018, "r_x2": 558.48926, "r_y2": 547.41556, "r_x3": 458.7120100000001, "r_y3": 547.41556, "coord_origin": "TOPLEFT" }, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.74918, "r_x1": 559.71368, "r_y1": 566.74918, "r_x2": 559.71368, "r_y2": 558.37456, "r_x3": 317.95499, "r_y3": 558.37456, "coord_origin": "TOPLEFT" }, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.7081800000001, "r_x1": 559.58124, "r_y1": 577.7081800000001, "r_x2": 559.58124, "r_y2": 569.33356, "r_x3": 317.95499, "r_y3": 569.33356, "coord_origin": "TOPLEFT" }, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 588.66718, "r_x1": 558.20386, "r_y1": 588.66718, "r_x2": 558.20386, "r_y2": 580.29256, "r_x3": 317.64099, "r_y3": 580.29256, "coord_origin": "TOPLEFT" }, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 599.62617, "r_x1": 558.20221, "r_y1": 599.62617, "r_x2": 558.20221, "r_y2": 591.25156, "r_x3": 317.95499, "r_y3": 591.25156, "coord_origin": "TOPLEFT" }, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 610.58417, "r_x1": 559.18457, "r_y1": 610.58417, "r_x2": 559.18457, "r_y2": 602.20955, "r_x3": 317.95499, "r_y3": 602.20955, "coord_origin": "TOPLEFT" }, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 621.54317, "r_x1": 558.20197, "r_y1": 621.54317, "r_x2": 558.20197, "r_y2": 613.16855, "r_x3": 317.95499, "r_y3": 613.16855, "coord_origin": "TOPLEFT" }, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 632.50217, "r_x1": 559.58197, "r_y1": 632.50217, "r_x2": 559.58197, "r_y2": 624.12755, "r_x3": 317.95499, "r_y3": 624.12755, "coord_origin": "TOPLEFT" }, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 643.46117, "r_x1": 559.71411, "r_y1": 643.46117, "r_x2": 559.71411, "r_y2": 635.08655, "r_x3": 317.95499, "r_y3": 635.08655, "coord_origin": "TOPLEFT" }, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 654.42017, "r_x1": 558.20557, "r_y1": 654.42017, "r_x2": 558.20557, "r_y2": 646.04555, "r_x3": 317.95499, "r_y3": 646.04555, "coord_origin": "TOPLEFT" }, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 665.3791699999999, "r_x1": 559.7149, "r_y1": 665.3791699999999, "r_x2": 559.7149, "r_y2": 657.00455, "r_x3": 317.95499, "r_y3": 657.00455, "coord_origin": "TOPLEFT" }, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 676.33817, "r_x1": 558.19849, "r_y1": 676.33817, "r_x2": 558.19849, "r_y2": 667.9635499999999, "r_x3": 317.95499, "r_y3": 667.9635499999999, "coord_origin": "TOPLEFT" }, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 687.29717, "r_x1": 558.43268, "r_y1": 687.29717, "r_x2": 558.43268, "r_y2": 678.92255, "r_x3": 317.62299, "r_y3": 678.92255, "coord_origin": "TOPLEFT" }, "text": "were not able to see each other\u2019s annotations. This was enforced by", "orig": "were not able to see each other\u2019s annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 698.256172, "r_x1": 558.19806, "r_y1": 698.256172, "r_x2": 558.19806, "r_y2": 689.8815500000001, "r_x3": 317.95499, "r_y3": 689.8815500000001, "coord_origin": "TOPLEFT" }, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 709.2151719999999, "r_x1": 558.20227, "r_y1": 709.2151719999999, "r_x2": 558.20227, "r_y2": 700.840553, "r_x3": 317.95499, "r_y3": 700.840553, "coord_origin": "TOPLEFT" }, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] } ] }, "tablestructure": { "table_map": {} }, "figures_classification": null, "equations_prediction": null, "vlm_response": null }, "assembled": { "elements": [ { "label": "page_header", "id": 13, "page_no": 4, "cluster": { "id": 13, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8877827525138855, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" }, { "label": "page_header", "id": 15, "page_no": 4, "cluster": { "id": 15, "label": "page_header", "bbox": { "l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8534670472145081, "cells": [ { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA" }, { "label": "text", "id": 12, "page_no": 4, "cluster": { "id": 12, "label": "text", "bbox": { "l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9022762179374695, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 95.73816, "r_x1": 294.04541, "r_y1": 95.73816, "r_x2": 294.04541, "r_y2": 87.36352999999997, "r_x3": 53.79800000000001, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.69713999999999, "r_x1": 188.1326, "r_y1": 106.69713999999999, "r_x2": 188.1326, "r_y2": 98.32250999999997, "r_x3": 53.79800000000001, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 190.37401, "r_y0": 106.70612000000006, "r_x1": 251.25586999999996, "r_y1": 106.70612000000006, "r_x2": 251.25586999999996, "r_y2": 98.36737000000005, "r_x3": 190.37401, "r_y3": 98.36737000000005, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.70801, "r_y0": 106.69713999999999, "r_x1": 287.01816, "r_y1": 106.69713999999999, "r_x2": 287.01816, "r_y2": 98.32250999999997, "r_x3": 253.70801, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category." }, { "label": "text", "id": 4, "page_no": 4, "cluster": { "id": 4, "label": "text", "bbox": { "l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9711344838142395, "cells": [ { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 117.65618999999992, "r_x1": 294.04257, "r_y1": 117.65618999999992, "r_x2": 294.04257, "r_y2": 109.28156000000001, "r_x3": 63.76100199999999, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.61517000000003, "r_x1": 294.04266, "r_y1": 128.61517000000003, "r_x2": 294.04266, "r_y2": 120.24054000000001, "r_x3": 53.79800000000001, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.57317999999998, "r_x1": 295.55615, "r_y1": 139.57317999999998, "r_x2": 295.55615, "r_y2": 131.19854999999995, "r_x3": 53.79800000000001, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 150.53216999999995, "r_x1": 294.04715, "r_y1": 150.53216999999995, "r_x2": 294.04715, "r_y2": 142.15752999999995, "r_x3": 53.79800000000001, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 161.49114999999995, "r_x1": 295.55923, "r_y1": 161.49114999999995, "r_x2": 295.55923, "r_y2": 153.11652000000004, "r_x3": 53.79800000000001, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 172.4502, "r_x1": 294.04535, "r_y1": 172.4502, "r_x2": 294.04535, "r_y2": 164.07556, "r_x3": 53.79800000000001, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 183.40918, "r_x1": 294.04803, "r_y1": 183.40918, "r_x2": 294.04803, "r_y2": 175.03454999999997, "r_x3": 53.79800000000001, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 194.36816, "r_x1": 294.04709, "r_y1": 194.36816, "r_x2": 294.04709, "r_y2": 185.99352999999996, "r_x3": 53.79800000000001, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 205.32714999999996, "r_x1": 295.55612, "r_y1": 205.32714999999996, "r_x2": 295.55612, "r_y2": 196.95250999999996, "r_x3": 53.79800000000001, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 216.28619000000003, "r_x1": 294.04712, "r_y1": 216.28619000000003, "r_x2": 294.04712, "r_y2": 207.91156, "r_x3": 53.79800000000001, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 227.24518, "r_x1": 294.04495, "r_y1": 227.24518, "r_x2": 294.04495, "r_y2": 218.87054, "r_x3": 53.79800000000001, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 238.20416, "r_x1": 294.04712, "r_y1": 238.20416, "r_x2": 294.04712, "r_y2": 229.82952999999998, "r_x3": 53.79800000000001, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 249.16216999999995, "r_x1": 168.61276, "r_y1": 249.16216999999995, "r_x2": 168.61276, "r_y2": 240.78754000000004, "r_x3": 53.79800000000001, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages." }, { "label": "text", "id": 1, "page_no": 4, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT" }, "confidence": 0.985905110836029, "cells": [ { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 260.12114999999994, "r_x1": 294.21884, "r_y1": 260.12114999999994, "r_x2": 294.21884, "r_y2": 251.74652000000003, "r_x3": 63.76100199999999, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 271.0802, "r_x1": 295.56006, "r_y1": 271.0802, "r_x2": 295.56006, "r_y2": 262.70556999999997, "r_x3": 53.79800000000001, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 282.03918, "r_x1": 295.55676, "r_y1": 282.03918, "r_x2": 295.55676, "r_y2": 273.66454999999996, "r_x3": 53.79800000000001, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 292.99817, "r_x1": 294.04922, "r_y1": 292.99817, "r_x2": 294.04922, "r_y2": 284.62354, "r_x3": 53.79800000000001, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 303.95717999999994, "r_x1": 294.04343, "r_y1": 303.95717999999994, "r_x2": 294.04343, "r_y2": 295.5825500000001, "r_x3": 53.79800000000001, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 314.91617, "r_x1": 294.04712, "r_y1": 314.91617, "r_x2": 294.04712, "r_y2": 306.54153, "r_x3": 53.79800000000001, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 325.87518, "r_x1": 294.04718, "r_y1": 325.87518, "r_x2": 294.04718, "r_y2": 317.50055, "r_x3": 53.79800000000001, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 336.83417, "r_x1": 67.28347, "r_y1": 336.83417, "r_x2": 67.28347, "r_y2": 328.4595299999999, "r_x3": 53.79800000000001, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:" }, { "label": "list_item", "id": 5, "page_no": 4, "cluster": { "id": 5, "label": "list_item", "bbox": { "l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT" }, "confidence": 0.9691526293754578, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 356.88718, "r_x1": 74.626793, "r_y1": 356.88718, "r_x2": 74.626793, "r_y2": 348.51254, "r_x3": 64.708, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.963936, "r_y0": 356.88718, "r_x1": 294.0462, "r_y1": 356.88718, "r_x2": 294.0462, "r_y2": 348.51254, "r_x3": 76.963936, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 367.84616, "r_x1": 95.730484, "r_y1": 367.84616, "r_x2": 95.730484, "r_y2": 359.47153, "r_x3": 78.207001, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 97.976997, "r_y0": 367.85513, "r_x1": 130.17131, "r_y1": 367.85513, "r_x2": 130.17131, "r_y2": 359.51639, "r_x3": 97.976997, "r_y3": 359.51639, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 130.17101, "r_y0": 367.84616, "r_x1": 294.04053, "r_y1": 367.84616, "r_x2": 294.04053, "r_y2": 359.47153, "r_x3": 130.17101, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": ". This definition is different from PubLayNet", "orig": ". This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 378.80518, "r_x1": 294.04385, "r_y1": 378.80518, "r_x2": 294.04385, "r_y2": 370.43054, "r_x3": 78.207001, "r_y3": 370.43054, "coord_origin": "TOPLEFT" }, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 389.76416, "r_x1": 91.593834, "r_y1": 389.76416, "r_x2": 91.593834, "r_y2": 381.38953000000004, "r_x3": 78.207001, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 93.834999, "r_y0": 389.77313, "r_x1": 106.88111, "r_y1": 389.77313, "r_x2": 106.88111, "r_y2": 381.43439000000006, "r_x3": 93.834999, "r_y3": 381.43439000000006, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 109.629, "r_y0": 389.76416, "r_x1": 133.72173, "r_y1": 389.76416, "r_x2": 133.72173, "r_y2": 381.38953000000004, "r_x3": 109.629, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object." }, { "label": "list_item", "id": 7, "page_no": 4, "cluster": { "id": 7, "label": "list_item", "bbox": { "l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT" }, "confidence": 0.9591284990310669, "cells": [ { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.707993, "r_y0": 400.72313999999994, "r_x1": 75.097656, "r_y1": 400.72313999999994, "r_x2": 75.097656, "r_y2": 392.34851, "r_x3": 64.707993, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.545731, "r_y0": 400.72313999999994, "r_x1": 84.351402, "r_y1": 400.72313999999994, "r_x2": 84.351402, "r_y2": 392.34851, "r_x3": 77.545731, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 86.584, "r_y0": 400.73215, "r_x1": 118.01329000000001, "r_y1": 400.73215, "r_x2": 118.01329000000001, "r_y2": 392.39339999999993, "r_x3": 86.584, "r_y3": 392.39339999999993, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 120.26899999999999, "r_y0": 400.72317999999996, "r_x1": 295.55695, "r_y1": 400.72317999999996, "r_x2": 295.55695, "r_y2": 392.34854, "r_x3": 120.26899999999999, "r_y3": 392.34854, "coord_origin": "TOPLEFT" }, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 411.68216, "r_x1": 181.80978, "r_y1": 411.68216, "r_x2": 181.80978, "r_y2": 403.30753, "r_x3": 78.207001, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 184.175, "r_y0": 411.69113, "r_x1": 216.68806000000004, "r_y1": 411.69113, "r_x2": 216.68806000000004, "r_y2": 403.35239, "r_x3": 184.175, "r_y3": 403.35239, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.078, "r_y0": 411.68216, "r_x1": 295.56372, "r_y1": 411.68216, "r_x2": 295.56372, "r_y2": 403.30753, "r_x3": 219.078, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 422.64116999999993, "r_x1": 294.04617, "r_y1": 422.64116999999993, "r_x2": 294.04617, "r_y2": 414.2665400000001, "r_x3": 78.207001, "r_y3": 414.2665400000001, "coord_origin": "TOPLEFT" }, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 433.60016, "r_x1": 192.00853, "r_y1": 433.60016, "r_x2": 192.00853, "r_y2": 425.22552, "r_x3": 78.207001, "r_y3": 425.22552, "coord_origin": "TOPLEFT" }, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement." }, { "label": "list_item", "id": 10, "page_no": 4, "cluster": { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT" }, "confidence": 0.9413429498672485, "cells": [ { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 444.55914, "r_x1": 74.483009, "r_y1": 444.55914, "r_x2": 74.483009, "r_y2": 436.18451000000005, "r_x3": 64.708, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.786255, "r_y0": 444.55914, "r_x1": 112.61566, "r_y1": 444.55914, "r_x2": 112.61566, "r_y2": 436.18451000000005, "r_x3": 76.786255, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 114.861, "r_y0": 444.56815000000006, "r_x1": 142.61249, "r_y1": 444.56815000000006, "r_x2": 142.61249, "r_y2": 436.2294, "r_x3": 114.861, "r_y3": 436.2294, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.612, "r_y0": 444.55917, "r_x1": 294.04724, "r_y1": 444.55917, "r_x2": 294.04724, "r_y2": 436.18454, "r_x3": 142.612, "r_y3": 436.18454, "coord_origin": "TOPLEFT" }, "text": ", there must be exactly one corresponding", "orig": ", there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 455.52713, "r_x1": 102.79287, "r_y1": 455.52713, "r_x2": 102.79287, "r_y2": 447.18839, "r_x3": 78.207001, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 105.245, "r_y0": 455.51815999999997, "r_x1": 113.09956999999999, "r_y1": 455.51815999999997, "r_x2": 113.09956999999999, "r_y2": 447.14352, "r_x3": 105.245, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 115.341, "r_y0": 455.52713, "r_x1": 134.40356, "r_y1": 455.52713, "r_x2": 134.40356, "r_y2": 447.18839, "r_x3": 115.341, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.403, "r_y0": 455.51815999999997, "r_x1": 136.37561, "r_y1": 455.51815999999997, "r_x2": 136.37561, "r_y2": 447.14352, "r_x3": 134.403, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ." }, { "label": "list_item", "id": 9, "page_no": 4, "cluster": { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT" }, "confidence": 0.9426738023757935, "cells": [ { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 466.47617, "r_x1": 74.220215, "r_y1": 466.47617, "r_x2": 74.220215, "r_y2": 458.1015300000001, "r_x3": 64.708, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.461555, "r_y0": 466.47617, "r_x1": 267.46786, "r_y1": 466.47617, "r_x2": 267.46786, "r_y2": 458.1015300000001, "r_x3": 76.461555, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 269.70599, "r_y0": 466.48514, "r_x1": 294.04599, "r_y1": 466.48514, "r_x2": 294.04599, "r_y2": 458.14639, "r_x3": 269.70599, "r_y3": 458.14639, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 477.43518, "r_x1": 102.29972, "r_y1": 477.43518, "r_x2": 102.29972, "r_y2": 469.06055, "r_x3": 78.207001, "r_y3": 469.06055, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(4) Connected sub-pictures are grouped together in one Picture object." }, { "label": "list_item", "id": 11, "page_no": 4, "cluster": { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT" }, "confidence": 0.9147457480430603, "cells": [ { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 488.39417, "r_x1": 74.345383, "r_y1": 488.39417, "r_x2": 74.345383, "r_y2": 480.01953, "r_x3": 64.708, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.616203, "r_y0": 488.39417, "r_x1": 206.13503, "r_y1": 488.39417, "r_x2": 206.13503, "r_y2": 480.01953, "r_x3": 76.616203, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 208.38, "r_y0": 488.40314, "r_x1": 238.12155, "r_y1": 488.40314, "r_x2": 238.12155, "r_y2": 480.06439, "r_x3": 208.38, "r_y3": 480.06439, "coord_origin": "TOPLEFT" }, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 240.41300999999999, "r_y0": 488.39417, "r_x1": 264.50571, "r_y1": 488.39417, "r_x2": 264.50571, "r_y2": 480.01953, "r_x3": 240.41300999999999, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(5) Formula numbers are included in a Formula object." }, { "label": "list_item", "id": 8, "page_no": 4, "cluster": { "id": 8, "label": "list_item", "bbox": { "l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT" }, "confidence": 0.9544308185577393, "cells": [ { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708008, "r_y0": 499.35315, "r_x1": 74.564522, "r_y1": 499.35315, "r_x2": 74.564522, "r_y2": 490.97852, "r_x3": 64.708008, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.886978, "r_y0": 499.35315, "r_x1": 294.04617, "r_y1": 499.35315, "r_x2": 294.04617, "r_y2": 490.97852, "r_x3": 76.886978, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 510.31216, "r_x1": 200.34819, "r_y1": 510.31216, "r_x2": 200.34819, "r_y2": 501.93753, "r_x3": 78.207001, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 203.66701, "r_y0": 510.32114, "r_x1": 256.57504, "r_y1": 510.32114, "r_x2": 256.57504, "r_y2": 501.98239, "r_x3": 203.66701, "r_y3": 501.98239, "coord_origin": "TOPLEFT" }, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 256.57401, "r_y0": 510.31216, "r_x1": 294.04401, "r_y1": 510.31216, "r_x2": 294.04401, "r_y2": 501.93753, "r_x3": 256.57401, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": ", unless it", "orig": ", unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 521.27118, "r_x1": 208.13017, "r_y1": 521.27118, "r_x2": 208.13017, "r_y2": 512.8965499999999, "r_x3": 78.207001, "r_y3": 512.8965499999999, "coord_origin": "TOPLEFT" }, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line." }, { "label": "text", "id": 3, "page_no": 4, "cluster": { "id": 3, "label": "text", "bbox": { "l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT" }, "confidence": 0.9811431169509888, "cells": [ { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 541.32516, "r_x1": 294.04337, "r_y1": 541.32516, "r_x2": 294.04337, "r_y2": 532.9505300000001, "r_x3": 53.528999, "r_y3": 532.9505300000001, "coord_origin": "TOPLEFT" }, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.28317, "r_x1": 295.56253, "r_y1": 552.28317, "r_x2": 295.56253, "r_y2": 543.90855, "r_x3": 53.79800000000001, "r_y3": 543.90855, "coord_origin": "TOPLEFT" }, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.24217, "r_x1": 294.04538, "r_y1": 563.24217, "r_x2": 294.04538, "r_y2": 554.8675499999999, "r_x3": 53.79800000000001, "r_y3": 554.8675499999999, "coord_origin": "TOPLEFT" }, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.20117, "r_x1": 127.2418, "r_y1": 574.20117, "r_x2": 127.2418, "r_y2": 565.82655, "r_x3": 53.79800000000001, "r_y3": 565.82655, "coord_origin": "TOPLEFT" }, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference." }, { "label": "text", "id": 0, "page_no": 4, "cluster": { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT" }, "confidence": 0.9864389896392822, "cells": [ { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 585.14224, "r_x1": 136.7744, "r_y1": 585.14224, "r_x2": 136.7744, "r_y2": 576.66899, "r_x3": 63.76100199999999, "r_y3": 576.66899, "coord_origin": "TOPLEFT" }, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 139.008, "r_y0": 585.16017, "r_x1": 295.56226, "r_y1": 585.16017, "r_x2": 295.56226, "r_y2": 576.7855500000001, "r_x3": 139.008, "r_y3": 576.7855500000001, "coord_origin": "TOPLEFT" }, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 596.1191699999999, "r_x1": 294.04532, "r_y1": 596.1191699999999, "r_x2": 294.04532, "r_y2": 587.74455, "r_x3": 53.79800000000001, "r_y3": 587.74455, "coord_origin": "TOPLEFT" }, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 607.07817, "r_x1": 295.55676, "r_y1": 607.07817, "r_x2": 295.55676, "r_y2": 598.70355, "r_x3": 53.79800000000001, "r_y3": 598.70355, "coord_origin": "TOPLEFT" }, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 618.0371700000001, "r_x1": 294.04605, "r_y1": 618.0371700000001, "r_x2": 294.04605, "r_y2": 609.66255, "r_x3": 53.79800000000001, "r_y3": 609.66255, "coord_origin": "TOPLEFT" }, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 628.99617, "r_x1": 295.42377, "r_y1": 628.99617, "r_x2": 295.42377, "r_y2": 620.6215500000001, "r_x3": 53.79800000000001, "r_y3": 620.6215500000001, "coord_origin": "TOPLEFT" }, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.95517, "r_x1": 294.04712, "r_y1": 639.95517, "r_x2": 294.04712, "r_y2": 631.58055, "r_x3": 53.79800000000001, "r_y3": 631.58055, "coord_origin": "TOPLEFT" }, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.91417, "r_x1": 294.04922, "r_y1": 650.91417, "r_x2": 294.04922, "r_y2": 642.53955, "r_x3": 53.79800000000001, "r_y3": 642.53955, "coord_origin": "TOPLEFT" }, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.87317, "r_x1": 295.55618, "r_y1": 661.87317, "r_x2": 295.55618, "r_y2": 653.49855, "r_x3": 53.79800000000001, "r_y3": 653.49855, "coord_origin": "TOPLEFT" }, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.83117, "r_x1": 294.04874, "r_y1": 672.83117, "r_x2": 294.04874, "r_y2": 664.45655, "r_x3": 53.79800000000001, "r_y3": 664.45655, "coord_origin": "TOPLEFT" }, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.79017, "r_x1": 295.42496, "r_y1": 683.79017, "r_x2": 295.42496, "r_y2": 675.4155499999999, "r_x3": 53.79800000000001, "r_y3": 675.4155499999999, "coord_origin": "TOPLEFT" }, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.749176, "r_x1": 295.0274, "r_y1": 694.749176, "r_x2": 295.0274, "r_y2": 686.37456, "r_x3": 53.79800000000001, "r_y3": 686.37456, "coord_origin": "TOPLEFT" }, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.708176, "r_x1": 294.04114, "r_y1": 705.708176, "r_x2": 294.04114, "r_y2": 697.333557, "r_x3": 53.79800000000001, "r_y3": 697.333557, "coord_origin": "TOPLEFT" }, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. 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"annotations": [], "provenance": null, "predicted_class": null, "confidence": null }, { "label": "text", "id": 47, "page_no": 4, "cluster": { "id": 47, "label": "text", "bbox": { "l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 400.12842, "r_y0": 460.5600600000001, "r_x1": 476.33178999999996, "r_y1": 460.5600600000001, "r_x2": 476.33178999999996, "r_y2": 458.44327000000004, "r_x3": 400.12842, "r_y3": 458.44327000000004, "coord_origin": "TOPLEFT" }, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0" }, { "label": "caption", "id": 14, "page_no": 4, "cluster": { "id": 14, "label": "caption", "bbox": { "l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT" }, "confidence": 0.8873711824417114, "cells": [ { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 481.96722, "r_x1": 558.3891, "r_y1": 481.96722, "r_x2": 558.3891, "r_y2": 473.49399, "r_x3": 317.95499, "r_y3": 473.49399, "coord_origin": "TOPLEFT" }, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 492.92621, "r_x1": 559.80579, "r_y1": 492.92621, "r_x2": 559.80579, "r_y2": 484.45297, "r_x3": 317.95499, "r_y3": 484.45297, "coord_origin": "TOPLEFT" }, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 503.88519, "r_x1": 544.14148, "r_y1": 503.88519, "r_x2": 544.14148, "r_y2": 495.41196, "r_x3": 317.95499, "r_y3": 495.41196, "coord_origin": "TOPLEFT" }, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous." }, { "label": "text", "id": 6, "page_no": 4, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT" }, "confidence": 0.9611718654632568, "cells": [ { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 533.87216, "r_x1": 558.20435, "r_y1": 533.87216, "r_x2": 558.20435, "r_y2": 525.49753, "r_x3": 317.62299, "r_y3": 525.49753, "coord_origin": "TOPLEFT" }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 544.83118, "r_x1": 326.07019, "r_y1": 544.83118, "r_x2": 326.07019, "r_y2": 536.45656, "r_x3": 317.74899, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "40", "orig": "40", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.3071, "r_y0": 544.83118, "r_x1": 509.11896, "r_y1": 544.83118, "r_x2": 509.11896, "r_y2": 536.45656, "r_x3": 328.3071, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "initially allocated annotators did not pass the bar.", "orig": "initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar." }, { "label": "text", "id": 2, "page_no": 4, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9829543232917786, "cells": [ { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 555.77225, "r_x1": 456.80109000000004, "r_y1": 555.77225, "r_x2": 456.80109000000004, "r_y2": 547.299, "r_x3": 327.918, "r_y3": 547.299, "coord_origin": "TOPLEFT" }, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 458.7120100000001, "r_y0": 555.79018, "r_x1": 558.48926, "r_y1": 555.79018, "r_x2": 558.48926, "r_y2": 547.41556, "r_x3": 458.7120100000001, "r_y3": 547.41556, "coord_origin": "TOPLEFT" }, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.74918, "r_x1": 559.71368, "r_y1": 566.74918, "r_x2": 559.71368, "r_y2": 558.37456, "r_x3": 317.95499, "r_y3": 558.37456, "coord_origin": "TOPLEFT" }, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.7081800000001, "r_x1": 559.58124, "r_y1": 577.7081800000001, "r_x2": 559.58124, "r_y2": 569.33356, "r_x3": 317.95499, "r_y3": 569.33356, "coord_origin": "TOPLEFT" }, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 588.66718, "r_x1": 558.20386, "r_y1": 588.66718, "r_x2": 558.20386, "r_y2": 580.29256, "r_x3": 317.64099, "r_y3": 580.29256, "coord_origin": "TOPLEFT" }, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 599.62617, "r_x1": 558.20221, "r_y1": 599.62617, "r_x2": 558.20221, "r_y2": 591.25156, "r_x3": 317.95499, "r_y3": 591.25156, "coord_origin": "TOPLEFT" }, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 610.58417, "r_x1": 559.18457, "r_y1": 610.58417, "r_x2": 559.18457, "r_y2": 602.20955, "r_x3": 317.95499, "r_y3": 602.20955, "coord_origin": "TOPLEFT" }, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 621.54317, "r_x1": 558.20197, "r_y1": 621.54317, "r_x2": 558.20197, "r_y2": 613.16855, "r_x3": 317.95499, "r_y3": 613.16855, "coord_origin": "TOPLEFT" }, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 632.50217, "r_x1": 559.58197, "r_y1": 632.50217, "r_x2": 559.58197, "r_y2": 624.12755, "r_x3": 317.95499, "r_y3": 624.12755, "coord_origin": "TOPLEFT" }, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 643.46117, "r_x1": 559.71411, "r_y1": 643.46117, "r_x2": 559.71411, "r_y2": 635.08655, "r_x3": 317.95499, "r_y3": 635.08655, "coord_origin": "TOPLEFT" }, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 654.42017, "r_x1": 558.20557, "r_y1": 654.42017, "r_x2": 558.20557, "r_y2": 646.04555, "r_x3": 317.95499, "r_y3": 646.04555, "coord_origin": "TOPLEFT" }, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 665.3791699999999, "r_x1": 559.7149, "r_y1": 665.3791699999999, "r_x2": 559.7149, "r_y2": 657.00455, "r_x3": 317.95499, "r_y3": 657.00455, "coord_origin": "TOPLEFT" }, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 676.33817, "r_x1": 558.19849, "r_y1": 676.33817, "r_x2": 558.19849, "r_y2": 667.9635499999999, "r_x3": 317.95499, "r_y3": 667.9635499999999, "coord_origin": "TOPLEFT" }, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 687.29717, "r_x1": 558.43268, "r_y1": 687.29717, "r_x2": 558.43268, "r_y2": 678.92255, "r_x3": 317.62299, "r_y3": 678.92255, "coord_origin": "TOPLEFT" }, "text": "were not able to see each other\u2019s annotations. This was enforced by", "orig": "were not able to see each other\u2019s annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 698.256172, "r_x1": 558.19806, "r_y1": 698.256172, "r_x2": 558.19806, "r_y2": 689.8815500000001, "r_x3": 317.95499, "r_y3": 689.8815500000001, "coord_origin": "TOPLEFT" }, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 709.2151719999999, "r_x1": 558.20227, "r_y1": 709.2151719999999, "r_x2": 558.20227, "r_y2": 700.840553, "r_x3": 317.95499, "r_y3": 700.840553, "coord_origin": "TOPLEFT" }, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted" } ], "body": [ { "label": "text", "id": 12, "page_no": 4, "cluster": { "id": 12, "label": "text", "bbox": { "l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9022762179374695, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 95.73816, "r_x1": 294.04541, "r_y1": 95.73816, "r_x2": 294.04541, "r_y2": 87.36352999999997, "r_x3": 53.79800000000001, "r_y3": 87.36352999999997, "coord_origin": "TOPLEFT" }, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.69713999999999, "r_x1": 188.1326, "r_y1": 106.69713999999999, "r_x2": 188.1326, "r_y2": 98.32250999999997, "r_x3": 53.79800000000001, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 190.37401, "r_y0": 106.70612000000006, "r_x1": 251.25586999999996, "r_y1": 106.70612000000006, "r_x2": 251.25586999999996, "r_y2": 98.36737000000005, "r_x3": 190.37401, "r_y3": 98.36737000000005, "coord_origin": "TOPLEFT" }, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.70801, "r_y0": 106.69713999999999, "r_x1": 287.01816, "r_y1": 106.69713999999999, "r_x2": 287.01816, "r_y2": 98.32250999999997, "r_x3": 253.70801, "r_y3": 98.32250999999997, "coord_origin": "TOPLEFT" }, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category." }, { "label": "text", "id": 4, "page_no": 4, "cluster": { "id": 4, "label": "text", "bbox": { "l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT" }, "confidence": 0.9711344838142395, "cells": [ { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 117.65618999999992, "r_x1": 294.04257, "r_y1": 117.65618999999992, "r_x2": 294.04257, "r_y2": 109.28156000000001, "r_x3": 63.76100199999999, "r_y3": 109.28156000000001, "coord_origin": "TOPLEFT" }, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.61517000000003, "r_x1": 294.04266, "r_y1": 128.61517000000003, "r_x2": 294.04266, "r_y2": 120.24054000000001, "r_x3": 53.79800000000001, "r_y3": 120.24054000000001, "coord_origin": "TOPLEFT" }, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.57317999999998, "r_x1": 295.55615, "r_y1": 139.57317999999998, "r_x2": 295.55615, "r_y2": 131.19854999999995, "r_x3": 53.79800000000001, "r_y3": 131.19854999999995, "coord_origin": "TOPLEFT" }, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 150.53216999999995, "r_x1": 294.04715, "r_y1": 150.53216999999995, "r_x2": 294.04715, "r_y2": 142.15752999999995, "r_x3": 53.79800000000001, "r_y3": 142.15752999999995, "coord_origin": "TOPLEFT" }, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 161.49114999999995, "r_x1": 295.55923, "r_y1": 161.49114999999995, "r_x2": 295.55923, "r_y2": 153.11652000000004, "r_x3": 53.79800000000001, "r_y3": 153.11652000000004, "coord_origin": "TOPLEFT" }, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 172.4502, "r_x1": 294.04535, "r_y1": 172.4502, "r_x2": 294.04535, "r_y2": 164.07556, "r_x3": 53.79800000000001, "r_y3": 164.07556, "coord_origin": "TOPLEFT" }, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 183.40918, "r_x1": 294.04803, "r_y1": 183.40918, "r_x2": 294.04803, "r_y2": 175.03454999999997, "r_x3": 53.79800000000001, "r_y3": 175.03454999999997, "coord_origin": "TOPLEFT" }, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 194.36816, "r_x1": 294.04709, "r_y1": 194.36816, "r_x2": 294.04709, "r_y2": 185.99352999999996, "r_x3": 53.79800000000001, "r_y3": 185.99352999999996, "coord_origin": "TOPLEFT" }, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 205.32714999999996, "r_x1": 295.55612, "r_y1": 205.32714999999996, "r_x2": 295.55612, "r_y2": 196.95250999999996, "r_x3": 53.79800000000001, "r_y3": 196.95250999999996, "coord_origin": "TOPLEFT" }, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 15, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 216.28619000000003, "r_x1": 294.04712, "r_y1": 216.28619000000003, "r_x2": 294.04712, "r_y2": 207.91156, "r_x3": 53.79800000000001, "r_y3": 207.91156, "coord_origin": "TOPLEFT" }, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 16, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 227.24518, "r_x1": 294.04495, "r_y1": 227.24518, "r_x2": 294.04495, "r_y2": 218.87054, "r_x3": 53.79800000000001, "r_y3": 218.87054, "coord_origin": "TOPLEFT" }, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 17, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 238.20416, "r_x1": 294.04712, "r_y1": 238.20416, "r_x2": 294.04712, "r_y2": 229.82952999999998, "r_x3": 53.79800000000001, "r_y3": 229.82952999999998, "coord_origin": "TOPLEFT" }, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 18, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 249.16216999999995, "r_x1": 168.61276, "r_y1": 249.16216999999995, "r_x2": 168.61276, "r_y2": 240.78754000000004, "r_x3": 53.79800000000001, "r_y3": 240.78754000000004, "coord_origin": "TOPLEFT" }, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages." }, { "label": "text", "id": 1, "page_no": 4, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT" }, "confidence": 0.985905110836029, "cells": [ { "index": 19, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 260.12114999999994, "r_x1": 294.21884, "r_y1": 260.12114999999994, "r_x2": 294.21884, "r_y2": 251.74652000000003, "r_x3": 63.76100199999999, "r_y3": 251.74652000000003, "coord_origin": "TOPLEFT" }, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 20, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 271.0802, "r_x1": 295.56006, "r_y1": 271.0802, "r_x2": 295.56006, "r_y2": 262.70556999999997, "r_x3": 53.79800000000001, "r_y3": 262.70556999999997, "coord_origin": "TOPLEFT" }, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 21, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 282.03918, "r_x1": 295.55676, "r_y1": 282.03918, "r_x2": 295.55676, "r_y2": 273.66454999999996, "r_x3": 53.79800000000001, "r_y3": 273.66454999999996, "coord_origin": "TOPLEFT" }, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 22, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 292.99817, "r_x1": 294.04922, "r_y1": 292.99817, "r_x2": 294.04922, "r_y2": 284.62354, "r_x3": 53.79800000000001, "r_y3": 284.62354, "coord_origin": "TOPLEFT" }, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 23, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 303.95717999999994, "r_x1": 294.04343, "r_y1": 303.95717999999994, "r_x2": 294.04343, "r_y2": 295.5825500000001, "r_x3": 53.79800000000001, "r_y3": 295.5825500000001, "coord_origin": "TOPLEFT" }, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 314.91617, "r_x1": 294.04712, "r_y1": 314.91617, "r_x2": 294.04712, "r_y2": 306.54153, "r_x3": 53.79800000000001, "r_y3": 306.54153, "coord_origin": "TOPLEFT" }, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 325.87518, "r_x1": 294.04718, "r_y1": 325.87518, "r_x2": 294.04718, "r_y2": 317.50055, "r_x3": 53.79800000000001, "r_y3": 317.50055, "coord_origin": "TOPLEFT" }, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 336.83417, "r_x1": 67.28347, "r_y1": 336.83417, "r_x2": 67.28347, "r_y2": 328.4595299999999, "r_x3": 53.79800000000001, "r_y3": 328.4595299999999, "coord_origin": "TOPLEFT" }, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:" }, { "label": "list_item", "id": 5, "page_no": 4, "cluster": { "id": 5, "label": "list_item", "bbox": { "l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT" }, "confidence": 0.9691526293754578, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 356.88718, "r_x1": 74.626793, "r_y1": 356.88718, "r_x2": 74.626793, "r_y2": 348.51254, "r_x3": 64.708, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.963936, "r_y0": 356.88718, "r_x1": 294.0462, "r_y1": 356.88718, "r_x2": 294.0462, "r_y2": 348.51254, "r_x3": 76.963936, "r_y3": 348.51254, "coord_origin": "TOPLEFT" }, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 367.84616, "r_x1": 95.730484, "r_y1": 367.84616, "r_x2": 95.730484, "r_y2": 359.47153, "r_x3": 78.207001, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 97.976997, "r_y0": 367.85513, "r_x1": 130.17131, "r_y1": 367.85513, "r_x2": 130.17131, "r_y2": 359.51639, "r_x3": 97.976997, "r_y3": 359.51639, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 130.17101, "r_y0": 367.84616, "r_x1": 294.04053, "r_y1": 367.84616, "r_x2": 294.04053, "r_y2": 359.47153, "r_x3": 130.17101, "r_y3": 359.47153, "coord_origin": "TOPLEFT" }, "text": ". This definition is different from PubLayNet", "orig": ". This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 378.80518, "r_x1": 294.04385, "r_y1": 378.80518, "r_x2": 294.04385, "r_y2": 370.43054, "r_x3": 78.207001, "r_y3": 370.43054, "coord_origin": "TOPLEFT" }, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 33, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 389.76416, "r_x1": 91.593834, "r_y1": 389.76416, "r_x2": 91.593834, "r_y2": 381.38953000000004, "r_x3": 78.207001, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 34, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 93.834999, "r_y0": 389.77313, "r_x1": 106.88111, "r_y1": 389.77313, "r_x2": 106.88111, "r_y2": 381.43439000000006, "r_x3": 93.834999, "r_y3": 381.43439000000006, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 35, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 109.629, "r_y0": 389.76416, "r_x1": 133.72173, "r_y1": 389.76416, "r_x2": 133.72173, "r_y2": 381.38953000000004, "r_x3": 109.629, "r_y3": 381.38953000000004, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object." }, { "label": "list_item", "id": 7, "page_no": 4, "cluster": { "id": 7, "label": "list_item", "bbox": { "l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT" }, "confidence": 0.9591284990310669, "cells": [ { "index": 36, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.707993, "r_y0": 400.72313999999994, "r_x1": 75.097656, "r_y1": 400.72313999999994, "r_x2": 75.097656, "r_y2": 392.34851, "r_x3": 64.707993, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 37, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 77.545731, "r_y0": 400.72313999999994, "r_x1": 84.351402, "r_y1": 400.72313999999994, "r_x2": 84.351402, "r_y2": 392.34851, "r_x3": 77.545731, "r_y3": 392.34851, "coord_origin": "TOPLEFT" }, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 38, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 86.584, "r_y0": 400.73215, "r_x1": 118.01329000000001, "r_y1": 400.73215, "r_x2": 118.01329000000001, "r_y2": 392.39339999999993, "r_x3": 86.584, "r_y3": 392.39339999999993, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 39, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 120.26899999999999, "r_y0": 400.72317999999996, "r_x1": 295.55695, "r_y1": 400.72317999999996, "r_x2": 295.55695, "r_y2": 392.34854, "r_x3": 120.26899999999999, "r_y3": 392.34854, "coord_origin": "TOPLEFT" }, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 40, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 411.68216, "r_x1": 181.80978, "r_y1": 411.68216, "r_x2": 181.80978, "r_y2": 403.30753, "r_x3": 78.207001, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 41, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 184.175, "r_y0": 411.69113, "r_x1": 216.68806000000004, "r_y1": 411.69113, "r_x2": 216.68806000000004, "r_y2": 403.35239, "r_x3": 184.175, "r_y3": 403.35239, "coord_origin": "TOPLEFT" }, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 42, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.078, "r_y0": 411.68216, "r_x1": 295.56372, "r_y1": 411.68216, "r_x2": 295.56372, "r_y2": 403.30753, "r_x3": 219.078, "r_y3": 403.30753, "coord_origin": "TOPLEFT" }, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 43, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 422.64116999999993, "r_x1": 294.04617, "r_y1": 422.64116999999993, "r_x2": 294.04617, "r_y2": 414.2665400000001, "r_x3": 78.207001, "r_y3": 414.2665400000001, "coord_origin": "TOPLEFT" }, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 44, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 433.60016, "r_x1": 192.00853, "r_y1": 433.60016, "r_x2": 192.00853, "r_y2": 425.22552, "r_x3": 78.207001, "r_y3": 425.22552, "coord_origin": "TOPLEFT" }, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement." }, { "label": "list_item", "id": 10, "page_no": 4, "cluster": { "id": 10, "label": "list_item", "bbox": { "l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT" }, "confidence": 0.9413429498672485, "cells": [ { "index": 45, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 444.55914, "r_x1": 74.483009, "r_y1": 444.55914, "r_x2": 74.483009, "r_y2": 436.18451000000005, "r_x3": 64.708, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 46, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.786255, "r_y0": 444.55914, "r_x1": 112.61566, "r_y1": 444.55914, "r_x2": 112.61566, "r_y2": 436.18451000000005, "r_x3": 76.786255, "r_y3": 436.18451000000005, "coord_origin": "TOPLEFT" }, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 47, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 114.861, "r_y0": 444.56815000000006, "r_x1": 142.61249, "r_y1": 444.56815000000006, "r_x2": 142.61249, "r_y2": 436.2294, "r_x3": 114.861, "r_y3": 436.2294, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 48, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.612, "r_y0": 444.55917, "r_x1": 294.04724, "r_y1": 444.55917, "r_x2": 294.04724, "r_y2": 436.18454, "r_x3": 142.612, "r_y3": 436.18454, "coord_origin": "TOPLEFT" }, "text": ", there must be exactly one corresponding", "orig": ", there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 49, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 455.52713, "r_x1": 102.79287, "r_y1": 455.52713, "r_x2": 102.79287, "r_y2": 447.18839, "r_x3": 78.207001, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 50, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 105.245, "r_y0": 455.51815999999997, "r_x1": 113.09956999999999, "r_y1": 455.51815999999997, "r_x2": 113.09956999999999, "r_y2": 447.14352, "r_x3": 105.245, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 51, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 115.341, "r_y0": 455.52713, "r_x1": 134.40356, "r_y1": 455.52713, "r_x2": 134.40356, "r_y2": 447.18839, "r_x3": 115.341, "r_y3": 447.18839, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 52, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 134.403, "r_y0": 455.51815999999997, "r_x1": 136.37561, "r_y1": 455.51815999999997, "r_x2": 136.37561, "r_y2": 447.14352, "r_x3": 134.403, "r_y3": 447.14352, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ." }, { "label": "list_item", "id": 9, "page_no": 4, "cluster": { "id": 9, "label": "list_item", "bbox": { "l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT" }, "confidence": 0.9426738023757935, "cells": [ { "index": 53, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 466.47617, "r_x1": 74.220215, "r_y1": 466.47617, "r_x2": 74.220215, "r_y2": 458.1015300000001, "r_x3": 64.708, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 54, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.461555, "r_y0": 466.47617, "r_x1": 267.46786, "r_y1": 466.47617, "r_x2": 267.46786, "r_y2": 458.1015300000001, "r_x3": 76.461555, "r_y3": 458.1015300000001, "coord_origin": "TOPLEFT" }, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 55, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 269.70599, "r_y0": 466.48514, "r_x1": 294.04599, "r_y1": 466.48514, "r_x2": 294.04599, "r_y2": 458.14639, "r_x3": 269.70599, "r_y3": 458.14639, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 56, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 477.43518, "r_x1": 102.29972, "r_y1": 477.43518, "r_x2": 102.29972, "r_y2": 469.06055, "r_x3": 78.207001, "r_y3": 469.06055, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(4) Connected sub-pictures are grouped together in one Picture object." }, { "label": "list_item", "id": 11, "page_no": 4, "cluster": { "id": 11, "label": "list_item", "bbox": { "l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT" }, "confidence": 0.9147457480430603, "cells": [ { "index": 57, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708, "r_y0": 488.39417, "r_x1": 74.345383, "r_y1": 488.39417, "r_x2": 74.345383, "r_y2": 480.01953, "r_x3": 64.708, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 58, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.616203, "r_y0": 488.39417, "r_x1": 206.13503, "r_y1": 488.39417, "r_x2": 206.13503, "r_y2": 480.01953, "r_x3": 76.616203, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 59, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 208.38, "r_y0": 488.40314, "r_x1": 238.12155, "r_y1": 488.40314, "r_x2": 238.12155, "r_y2": 480.06439, "r_x3": 208.38, "r_y3": 480.06439, "coord_origin": "TOPLEFT" }, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 60, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 240.41300999999999, "r_y0": 488.39417, "r_x1": 264.50571, "r_y1": 488.39417, "r_x2": 264.50571, "r_y2": 480.01953, "r_x3": 240.41300999999999, "r_y3": 480.01953, "coord_origin": "TOPLEFT" }, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(5) Formula numbers are included in a Formula object." }, { "label": "list_item", "id": 8, "page_no": 4, "cluster": { "id": 8, "label": "list_item", "bbox": { "l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT" }, "confidence": 0.9544308185577393, "cells": [ { "index": 61, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.708008, "r_y0": 499.35315, "r_x1": 74.564522, "r_y1": 499.35315, "r_x2": 74.564522, "r_y2": 490.97852, "r_x3": 64.708008, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 62, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 76.886978, "r_y0": 499.35315, "r_x1": 294.04617, "r_y1": 499.35315, "r_x2": 294.04617, "r_y2": 490.97852, "r_x3": 76.886978, "r_y3": 490.97852, "coord_origin": "TOPLEFT" }, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 63, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 510.31216, "r_x1": 200.34819, "r_y1": 510.31216, "r_x2": 200.34819, "r_y2": 501.93753, "r_x3": 78.207001, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 64, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 203.66701, "r_y0": 510.32114, "r_x1": 256.57504, "r_y1": 510.32114, "r_x2": 256.57504, "r_y2": 501.98239, "r_x3": 203.66701, "r_y3": 501.98239, "coord_origin": "TOPLEFT" }, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 65, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 256.57401, "r_y0": 510.31216, "r_x1": 294.04401, "r_y1": 510.31216, "r_x2": 294.04401, "r_y2": 501.93753, "r_x3": 256.57401, "r_y3": 501.93753, "coord_origin": "TOPLEFT" }, "text": ", unless it", "orig": ", unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 66, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.207001, "r_y0": 521.27118, "r_x1": 208.13017, "r_y1": 521.27118, "r_x2": 208.13017, "r_y2": 512.8965499999999, "r_x3": 78.207001, "r_y3": 512.8965499999999, "coord_origin": "TOPLEFT" }, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line." }, { "label": "text", "id": 3, "page_no": 4, "cluster": { "id": 3, "label": "text", "bbox": { "l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT" }, "confidence": 0.9811431169509888, "cells": [ { "index": 67, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 541.32516, "r_x1": 294.04337, "r_y1": 541.32516, "r_x2": 294.04337, "r_y2": 532.9505300000001, "r_x3": 53.528999, "r_y3": 532.9505300000001, "coord_origin": "TOPLEFT" }, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 68, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.28317, "r_x1": 295.56253, "r_y1": 552.28317, "r_x2": 295.56253, "r_y2": 543.90855, "r_x3": 53.79800000000001, "r_y3": 543.90855, "coord_origin": "TOPLEFT" }, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 69, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.24217, "r_x1": 294.04538, "r_y1": 563.24217, "r_x2": 294.04538, "r_y2": 554.8675499999999, "r_x3": 53.79800000000001, "r_y3": 554.8675499999999, "coord_origin": "TOPLEFT" }, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 70, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.20117, "r_x1": 127.2418, "r_y1": 574.20117, "r_x2": 127.2418, "r_y2": 565.82655, "r_x3": 53.79800000000001, "r_y3": 565.82655, "coord_origin": "TOPLEFT" }, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference." }, { "label": "text", "id": 0, "page_no": 4, "cluster": { "id": 0, "label": "text", "bbox": { "l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT" }, "confidence": 0.9864389896392822, "cells": [ { "index": 71, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 585.14224, "r_x1": 136.7744, "r_y1": 585.14224, "r_x2": 136.7744, "r_y2": 576.66899, "r_x3": 63.76100199999999, "r_y3": 576.66899, "coord_origin": "TOPLEFT" }, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 139.008, "r_y0": 585.16017, "r_x1": 295.56226, "r_y1": 585.16017, "r_x2": 295.56226, "r_y2": 576.7855500000001, "r_x3": 139.008, "r_y3": 576.7855500000001, "coord_origin": "TOPLEFT" }, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 596.1191699999999, "r_x1": 294.04532, "r_y1": 596.1191699999999, "r_x2": 294.04532, "r_y2": 587.74455, "r_x3": 53.79800000000001, "r_y3": 587.74455, "coord_origin": "TOPLEFT" }, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 607.07817, "r_x1": 295.55676, "r_y1": 607.07817, "r_x2": 295.55676, "r_y2": 598.70355, "r_x3": 53.79800000000001, "r_y3": 598.70355, "coord_origin": "TOPLEFT" }, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 618.0371700000001, "r_x1": 294.04605, "r_y1": 618.0371700000001, "r_x2": 294.04605, "r_y2": 609.66255, "r_x3": 53.79800000000001, "r_y3": 609.66255, "coord_origin": "TOPLEFT" }, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 628.99617, "r_x1": 295.42377, "r_y1": 628.99617, "r_x2": 295.42377, "r_y2": 620.6215500000001, "r_x3": 53.79800000000001, "r_y3": 620.6215500000001, "coord_origin": "TOPLEFT" }, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.95517, "r_x1": 294.04712, "r_y1": 639.95517, "r_x2": 294.04712, "r_y2": 631.58055, "r_x3": 53.79800000000001, "r_y3": 631.58055, "coord_origin": "TOPLEFT" }, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.91417, "r_x1": 294.04922, "r_y1": 650.91417, "r_x2": 294.04922, "r_y2": 642.53955, "r_x3": 53.79800000000001, "r_y3": 642.53955, "coord_origin": "TOPLEFT" }, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.87317, "r_x1": 295.55618, "r_y1": 661.87317, "r_x2": 295.55618, "r_y2": 653.49855, "r_x3": 53.79800000000001, "r_y3": 653.49855, "coord_origin": "TOPLEFT" }, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.83117, "r_x1": 294.04874, "r_y1": 672.83117, "r_x2": 294.04874, "r_y2": 664.45655, "r_x3": 53.79800000000001, "r_y3": 664.45655, "coord_origin": "TOPLEFT" }, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.79017, "r_x1": 295.42496, "r_y1": 683.79017, "r_x2": 295.42496, "r_y2": 675.4155499999999, "r_x3": 53.79800000000001, "r_y3": 675.4155499999999, "coord_origin": "TOPLEFT" }, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.749176, "r_x1": 295.0274, "r_y1": 694.749176, "r_x2": 295.0274, "r_y2": 686.37456, "r_x3": 53.79800000000001, "r_y3": 686.37456, "coord_origin": "TOPLEFT" }, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.708176, "r_x1": 294.04114, "r_y1": 705.708176, "r_x2": 294.04114, "r_y2": 697.333557, "r_x3": 53.79800000000001, "r_y3": 697.333557, "coord_origin": "TOPLEFT" }, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. 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"annotations": [], "provenance": null, "predicted_class": null, "confidence": null }, { "label": "text", "id": 47, "page_no": 4, "cluster": { "id": 47, "label": "text", "bbox": { "l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT" }, "confidence": 1.0, "cells": [ { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 400.12842, "r_y0": 460.5600600000001, "r_x1": 476.33178999999996, "r_y1": 460.5600600000001, "r_x2": 476.33178999999996, "r_y2": 458.44327000000004, "r_x3": 400.12842, "r_y3": 458.44327000000004, "coord_origin": "TOPLEFT" }, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0" }, { "label": "caption", "id": 14, "page_no": 4, "cluster": { "id": 14, "label": "caption", "bbox": { "l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT" }, "confidence": 0.8873711824417114, "cells": [ { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 481.96722, "r_x1": 558.3891, "r_y1": 481.96722, "r_x2": 558.3891, "r_y2": 473.49399, "r_x3": 317.95499, "r_y3": 473.49399, "coord_origin": "TOPLEFT" }, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 492.92621, "r_x1": 559.80579, "r_y1": 492.92621, "r_x2": 559.80579, "r_y2": 484.45297, "r_x3": 317.95499, "r_y3": 484.45297, "coord_origin": "TOPLEFT" }, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 503.88519, "r_x1": 544.14148, "r_y1": 503.88519, "r_x2": 544.14148, "r_y2": 495.41196, "r_x3": 317.95499, "r_y3": 495.41196, "coord_origin": "TOPLEFT" }, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous." }, { "label": "text", "id": 6, "page_no": 4, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT" }, "confidence": 0.9611718654632568, "cells": [ { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 533.87216, "r_x1": 558.20435, "r_y1": 533.87216, "r_x2": 558.20435, "r_y2": 525.49753, "r_x3": 317.62299, "r_y3": 525.49753, "coord_origin": "TOPLEFT" }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 544.83118, "r_x1": 326.07019, "r_y1": 544.83118, "r_x2": 326.07019, "r_y2": 536.45656, "r_x3": 317.74899, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "40", "orig": "40", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 328.3071, "r_y0": 544.83118, "r_x1": 509.11896, "r_y1": 544.83118, "r_x2": 509.11896, "r_y2": 536.45656, "r_x3": 328.3071, "r_y3": 536.45656, "coord_origin": "TOPLEFT" }, "text": "initially allocated annotators did not pass the bar.", "orig": "initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar." }, { "label": "text", "id": 2, "page_no": 4, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT" }, "confidence": 0.9829543232917786, "cells": [ { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 555.77225, "r_x1": 456.80109000000004, "r_y1": 555.77225, "r_x2": 456.80109000000004, "r_y2": 547.299, "r_x3": 327.918, "r_y3": 547.299, "coord_origin": "TOPLEFT" }, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 458.7120100000001, "r_y0": 555.79018, "r_x1": 558.48926, "r_y1": 555.79018, "r_x2": 558.48926, "r_y2": 547.41556, "r_x3": 458.7120100000001, "r_y3": 547.41556, "coord_origin": "TOPLEFT" }, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 566.74918, "r_x1": 559.71368, "r_y1": 566.74918, "r_x2": 559.71368, "r_y2": 558.37456, "r_x3": 317.95499, "r_y3": 558.37456, "coord_origin": "TOPLEFT" }, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 577.7081800000001, "r_x1": 559.58124, "r_y1": 577.7081800000001, "r_x2": 559.58124, "r_y2": 569.33356, "r_x3": 317.95499, "r_y3": 569.33356, "coord_origin": "TOPLEFT" }, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 588.66718, "r_x1": 558.20386, "r_y1": 588.66718, "r_x2": 558.20386, "r_y2": 580.29256, "r_x3": 317.64099, "r_y3": 580.29256, "coord_origin": "TOPLEFT" }, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 599.62617, "r_x1": 558.20221, "r_y1": 599.62617, "r_x2": 558.20221, "r_y2": 591.25156, "r_x3": 317.95499, "r_y3": 591.25156, "coord_origin": "TOPLEFT" }, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 610.58417, "r_x1": 559.18457, "r_y1": 610.58417, "r_x2": 559.18457, "r_y2": 602.20955, "r_x3": 317.95499, "r_y3": 602.20955, "coord_origin": "TOPLEFT" }, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 621.54317, "r_x1": 558.20197, "r_y1": 621.54317, "r_x2": 558.20197, "r_y2": 613.16855, "r_x3": 317.95499, "r_y3": 613.16855, "coord_origin": "TOPLEFT" }, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 632.50217, "r_x1": 559.58197, "r_y1": 632.50217, "r_x2": 559.58197, "r_y2": 624.12755, "r_x3": 317.95499, "r_y3": 624.12755, "coord_origin": "TOPLEFT" }, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 643.46117, "r_x1": 559.71411, "r_y1": 643.46117, "r_x2": 559.71411, "r_y2": 635.08655, "r_x3": 317.95499, "r_y3": 635.08655, "coord_origin": "TOPLEFT" }, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 654.42017, "r_x1": 558.20557, "r_y1": 654.42017, "r_x2": 558.20557, "r_y2": 646.04555, "r_x3": 317.95499, "r_y3": 646.04555, "coord_origin": "TOPLEFT" }, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 665.3791699999999, "r_x1": 559.7149, "r_y1": 665.3791699999999, "r_x2": 559.7149, "r_y2": 657.00455, "r_x3": 317.95499, "r_y3": 657.00455, "coord_origin": "TOPLEFT" }, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 676.33817, "r_x1": 558.19849, "r_y1": 676.33817, "r_x2": 558.19849, "r_y2": 667.9635499999999, "r_x3": 317.95499, "r_y3": 667.9635499999999, "coord_origin": "TOPLEFT" }, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 687.29717, "r_x1": 558.43268, "r_y1": 687.29717, "r_x2": 558.43268, "r_y2": 678.92255, "r_x3": 317.62299, "r_y3": 678.92255, "coord_origin": "TOPLEFT" }, "text": "were not able to see each other\u2019s annotations. This was enforced by", "orig": "were not able to see each other\u2019s annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 698.256172, "r_x1": 558.19806, "r_y1": 698.256172, "r_x2": 558.19806, "r_y2": 689.8815500000001, "r_x3": 317.95499, "r_y3": 689.8815500000001, "coord_origin": "TOPLEFT" }, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 709.2151719999999, "r_x1": 558.20227, "r_y1": 709.2151719999999, "r_x2": 558.20227, "r_y2": 700.840553, "r_x3": 317.95499, "r_y3": 700.840553, "coord_origin": "TOPLEFT" }, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted" } ], "headers": [ { "label": "page_header", "id": 13, "page_no": 4, "cluster": { "id": 13, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8877827525138855, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" }, { "label": "page_header", "id": 15, "page_no": 4, "cluster": { "id": 15, "label": "page_header", "bbox": { "l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8534670472145081, "cells": [ { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA" } ] } }, { "page_no": 5, "size": { "width": 612.0, "height": 792.0 }, "parsed_page": { "dimension": { "angle": 0.0, "rect": { "r_x0": 0.0, "r_y0": 0.0, "r_x1": 612.0, "r_y1": 0.0, "r_x2": 612.0, "r_y2": 792.0, "r_x3": 0.0, "r_y3": 792.0, "coord_origin": "BOTTOMLEFT" }, "boundary_type": "crop_box", "art_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "bleed_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "crop_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "media_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "trim_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" } }, "bitmap_resources": [], "char_cells": [], "word_cells": [], "textline_cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.04361, "r_y1": 95.34618999999998, "r_x2": 294.04361, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04373, "r_y1": 106.30517999999995, "r_x2": 294.04373, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "detection networks on DocLayNet test set. The MRCNN", "orig": "detection networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.52, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 294.04373, "r_y1": 128.22313999999994, "r_x2": 294.04373, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.18213000000003, "r_x1": 202.43402, "r_y1": 139.18213000000003, "r_x2": 202.43402, "r_y2": 130.70885999999996, "r_x3": 53.79800000000001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 206.08501, "r_y0": 139.20905000000005, "r_x1": 247.14215000000002, "r_y1": 139.20905000000005, "r_x2": 247.14215000000002, "r_y2": 130.71783000000005, "r_x3": 206.08501, "r_y3": 130.71783000000005, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 250.95001, "r_y0": 139.18213000000003, "r_x1": 294.04254, "r_y1": 139.18213000000003, "r_x2": 294.04254, "r_y2": 130.70885999999996, "r_x3": 250.95001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52002, "r_y0": 150.14111000000003, "r_x1": 294.04367, "r_y1": 150.14111000000003, "r_x2": 294.04367, "r_y2": 141.66785000000004, "r_x3": 53.52002, "r_y3": 141.66785000000004, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 161.1001, "r_x1": 294.04373, "r_y1": 161.1001, "r_x2": 294.04373, "r_y2": 152.62683000000004, "r_x3": 53.798019, "r_y3": 152.62683000000004, "coord_origin": "TOPLEFT" }, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 172.05908, "r_x1": 295.64874, "r_y1": 172.05908, "r_x2": 295.64874, "r_y2": 163.58582, "r_x3": 53.798019, "r_y3": 163.58582, "coord_origin": "TOPLEFT" }, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 183.01709000000005, "r_x1": 268.62399, "r_y1": 183.01709000000005, "r_x2": 268.62399, "r_y2": 174.54381999999998, "r_x3": 53.798019, "r_y3": 174.54381999999998, "coord_origin": "TOPLEFT" }, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 132.36501, "r_y0": 206.34813999999994, "r_x1": 157.99098, "r_y1": 206.34813999999994, "r_x2": 157.99098, "r_y2": 197.97351000000003, "r_x3": 132.36501, "r_y3": 197.97351000000003, "coord_origin": "TOPLEFT" }, "text": "human", "orig": "human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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"text": "to avoid this at any cost in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 390.26117, "r_x1": 295.55612, "r_y1": 390.26117, "r_x2": 295.55612, "r_y2": 381.88654, "r_x3": 53.79800000000001, "r_y3": 381.88654, "coord_origin": "TOPLEFT" }, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 401.22017999999997, "r_x1": 140.3623, "r_y1": 401.22017999999997, "r_x2": 140.3623, "r_y2": 392.84555, "r_x3": 53.79800000000001, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.99001, "r_y0": 401.2291599999999, "r_x1": 175.9695, "r_y1": 401.2291599999999, "r_x2": 175.9695, "r_y2": 392.89041, "r_x3": 142.99001, "r_y3": 392.89041, "coord_origin": "TOPLEFT" }, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 178.951, "r_y0": 401.22017999999997, "r_x1": 294.04083, "r_y1": 401.22017999999997, "r_x2": 294.04083, "r_y2": 392.84555, "r_x3": 178.951, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 412.17917, "r_x1": 295.42493, "r_y1": 412.17917, "r_x2": 295.42493, "r_y2": 403.80453, "r_x3": 53.79800000000001, "r_y3": 403.80453, "coord_origin": "TOPLEFT" }, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 423.13818, "r_x1": 294.04251, "r_y1": 423.13818, "r_x2": 294.04251, "r_y2": 414.76355, "r_x3": 53.528999, "r_y3": 414.76355, "coord_origin": "TOPLEFT" }, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 434.09717, "r_x1": 294.04807, "r_y1": 434.09717, "r_x2": 294.04807, "r_y2": 425.72253, "r_x3": 53.79800000000001, "r_y3": 425.72253, "coord_origin": "TOPLEFT" }, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 445.0561799999999, "r_x1": 256.80627, "r_y1": 445.0561799999999, "r_x2": 256.80627, "r_y2": 436.6815500000001, "r_x3": 53.79800000000001, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 259.04199, "r_y0": 445.06516, "r_x1": 278.10455, "r_y1": 445.06516, "r_x2": 278.10455, "r_y2": 436.72641, "r_x3": 259.04199, "r_y3": 436.72641, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 280.54999, "r_y0": 445.0561799999999, "r_x1": 294.04443, "r_y1": 445.0561799999999, "r_x2": 294.04443, "r_y2": 436.6815500000001, "r_x3": 280.54999, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 456.02413999999993, "r_x1": 78.875587, "r_y1": 456.02413999999993, "r_x2": 78.875587, "r_y2": 447.68539, "r_x3": 53.79800000000001, "r_y3": 447.68539, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.876999, "r_y0": 456.01517, "r_x1": 294.04852, "r_y1": 456.01517, "r_x2": 294.04852, "r_y2": 447.64053, "r_x3": 78.876999, "r_y3": 447.64053, "coord_origin": "TOPLEFT" }, "text": ". For the latter, we instructed annotation staff to minimise", "orig": ". For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 466.97418, "r_x1": 294.04645, "r_y1": 466.97418, "r_x2": 294.04645, "r_y2": 458.59955, "r_x3": 53.79800000000001, "r_y3": 458.59955, "coord_origin": "TOPLEFT" }, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 477.93317, "r_x1": 294.0416, "r_y1": 477.93317, "r_x2": 294.0416, "r_y2": 469.55853, "r_x3": 53.79800000000001, "r_y3": 469.55853, "coord_origin": "TOPLEFT" }, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 488.89117, "r_x1": 294.04538, "r_y1": 488.89117, "r_x2": 294.04538, "r_y2": 480.51654, "r_x3": 53.79800000000001, "r_y3": 480.51654, "coord_origin": "TOPLEFT" }, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 499.85016, "r_x1": 294.04312, "r_y1": 499.85016, "r_x2": 294.04312, "r_y2": 491.47552, "r_x3": 53.79800000000001, "r_y3": 491.47552, "coord_origin": "TOPLEFT" }, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 510.81815, "r_x1": 80.597939, "r_y1": 510.81815, "r_x2": 80.597939, "r_y2": 502.4794, "r_x3": 53.79800000000001, "r_y3": 502.4794, "coord_origin": "TOPLEFT" }, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 83.366997, "r_y0": 510.80917, "r_x1": 294.04483, "r_y1": 510.80917, "r_x2": 294.04483, "r_y2": 502.43454, "r_x3": 83.366997, "r_y3": 502.43454, "coord_origin": "TOPLEFT" }, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 521.7681600000001, "r_x1": 294.25833, "r_y1": 521.7681600000001, "r_x2": 294.25833, "r_y2": 513.3935200000001, "r_x3": 53.79800000000001, "r_y3": 513.3935200000001, "coord_origin": "TOPLEFT" }, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 532.72717, "r_x1": 294.04535, "r_y1": 532.72717, "r_x2": 294.04535, "r_y2": 524.35254, "r_x3": 53.79800000000001, "r_y3": 524.35254, "coord_origin": "TOPLEFT" }, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 543.68617, "r_x1": 294.04535, "r_y1": 543.68617, "r_x2": 294.04535, "r_y2": 535.31155, "r_x3": 53.79800000000001, "r_y3": 535.31155, "coord_origin": "TOPLEFT" }, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 554.64517, "r_x1": 295.02759, "r_y1": 554.64517, "r_x2": 295.02759, "r_y2": 546.27055, "r_x3": 53.79800000000001, "r_y3": 546.27055, "coord_origin": "TOPLEFT" }, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 565.6041700000001, "r_x1": 294.0488, "r_y1": 565.6041700000001, "r_x2": 294.0488, "r_y2": 557.22955, "r_x3": 53.79800000000001, "r_y3": 557.22955, "coord_origin": "TOPLEFT" }, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 576.56317, "r_x1": 281.80457, "r_y1": 576.56317, "r_x2": 281.80457, "r_y2": 568.18855, "r_x3": 53.79800000000001, "r_y3": 568.18855, "coord_origin": "TOPLEFT" }, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.43901, "r_x1": 59.405277, "r_y1": 598.43901, "r_x2": 59.405277, "r_y2": 588.12991, "r_x3": 53.79800000000001, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.314377, "r_y0": 598.43901, "r_x1": 147.48535, "r_y1": 598.43901, "r_x2": 147.48535, "r_y2": 588.12991, "r_x3": 70.314377, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 621.6281700000001, "r_x1": 294.04871, "r_y1": 621.6281700000001, "r_x2": 294.04871, "r_y2": 613.25356, "r_x3": 53.528999, "r_y3": 613.25356, "coord_origin": "TOPLEFT" }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 632.58717, "r_x1": 294.27576, "r_y1": 632.58717, "r_x2": 294.27576, "r_y2": 624.21255, "r_x3": 53.79800000000001, "r_y3": 624.21255, "coord_origin": "TOPLEFT" }, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 643.54617, "r_x1": 294.04144, "r_y1": 643.54617, "r_x2": 294.04144, "r_y2": 635.17155, "r_x3": 53.79800000000001, "r_y3": 635.17155, "coord_origin": "TOPLEFT" }, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 654.50517, "r_x1": 294.04163, "r_y1": 654.50517, "r_x2": 294.04163, "r_y2": 646.13055, "r_x3": 53.79800000000001, "r_y3": 646.13055, "coord_origin": "TOPLEFT" }, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 665.46317, "r_x1": 294.0412, "r_y1": 665.46317, "r_x2": 294.0412, "r_y2": 657.0885499999999, "r_x3": 53.79800000000001, "r_y3": 657.0885499999999, "coord_origin": "TOPLEFT" }, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 676.42217, "r_x1": 155.01054, "r_y1": 676.42217, "r_x2": 155.01054, "r_y2": 668.04755, "r_x3": 53.79800000000001, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.23599, "r_y0": 676.43114, "r_x1": 193.26666, "r_y1": 676.43114, "r_x2": 193.26666, "r_y2": 668.09238, "r_x3": 157.23599, "r_y3": 668.09238, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 195.867, "r_y0": 676.42217, "r_x1": 294.0473, "r_y1": 676.42217, "r_x2": 294.0473, "r_y2": 668.04755, "r_x3": 195.867, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 687.38117, "r_x1": 294.04538, "r_y1": 687.38117, "r_x2": 294.04538, "r_y2": 679.0065500000001, "r_x3": 53.79800000000001, "r_y3": 679.0065500000001, "coord_origin": "TOPLEFT" }, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 698.3401719999999, "r_x1": 295.4281, "r_y1": 698.3401719999999, "r_x2": 295.4281, "r_y2": 689.96555, "r_x3": 53.79800000000001, "r_y3": 689.96555, "coord_origin": "TOPLEFT" }, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 709.299171, "r_x1": 294.04413, "r_y1": 709.299171, "r_x2": 294.04413, "r_y2": 700.9245530000001, "r_x3": 53.484001, "r_y3": 700.9245530000001, "coord_origin": "TOPLEFT" }, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 349.16577, "r_y0": 252.75427000000002, "r_x1": 352.48175, "r_y1": 252.75427000000002, "r_x2": 352.48175, "r_y2": 246.68017999999995, "r_x3": 349.16577, "r_y3": 246.68017999999995, "coord_origin": "TOPLEFT" }, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { 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152.88489000000004, "coord_origin": "TOPLEFT" }, "text": "70", "orig": "70", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 153, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 287.48923, "r_x1": 558.47876, "r_y1": 287.48923, "r_x2": 558.47876, "r_y2": 279.01599, "r_x3": 317.95499, "r_y3": 279.01599, "coord_origin": "TOPLEFT" }, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 154, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 298.44824, "r_x1": 559.80579, "r_y1": 298.44824, "r_x2": 559.80579, "r_y2": 289.97501, "r_x3": 317.95499, "r_y3": 289.97501, "coord_origin": "TOPLEFT" }, "text": "R-CNN network with ResNet50 backbone trained on increas-", "orig": "R-CNN network with ResNet50 backbone trained on increas-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 155, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 309.40723, "r_x1": 558.20068, "r_y1": 309.40723, "r_x2": 558.20068, "r_y2": 300.93399, "r_x3": 317.95499, "r_y3": 300.93399, "coord_origin": "TOPLEFT" }, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.36620999999997, "r_x1": 558.20062, "r_y1": 320.36620999999997, "r_x2": 558.20062, "r_y2": 311.89297, "r_x3": 317.95499, "r_y3": 311.89297, "coord_origin": "TOPLEFT" }, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.3252, "r_x1": 558.20074, "r_y1": 331.3252, "r_x2": 558.20074, "r_y2": 322.85196, "r_x3": 317.95499, "r_y3": 322.85196, "coord_origin": "TOPLEFT" }, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.28418000000005, "r_x1": 445.24207, "r_y1": 342.28418000000005, "r_x2": 445.24207, "r_y2": 333.81094, "r_x3": 317.95499, "r_y3": 333.81094, "coord_origin": "TOPLEFT" }, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 392.38617, "r_x1": 558.20416, "r_y1": 392.38617, "r_x2": 558.20416, "r_y2": 384.01154, "r_x3": 317.95499, "r_y3": 384.01154, "coord_origin": "TOPLEFT" }, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 403.34517999999997, "r_x1": 463.04938, "r_y1": 403.34517999999997, "r_x2": 463.04938, "r_y2": 394.97055, "r_x3": 317.95499, "r_y3": 394.97055, "coord_origin": "TOPLEFT" }, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 414.30417, "r_x1": 558.19836, "r_y1": 414.30417, "r_x2": 558.19836, "r_y2": 405.92953, "r_x3": 327.918, "r_y3": 405.92953, "coord_origin": "TOPLEFT" }, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 425.26318, "r_x1": 558.4364, "r_y1": 425.26318, "r_x2": 558.4364, "r_y2": 416.88855, "r_x3": 317.95499, "r_y3": 416.88855, "coord_origin": "TOPLEFT" }, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 436.22116, "r_x1": 558.20563, "r_y1": 436.22116, "r_x2": 558.20563, "r_y2": 427.84653, "r_x3": 317.95499, "r_y3": 427.84653, "coord_origin": "TOPLEFT" }, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 447.18018, "r_x1": 558.20044, "r_y1": 447.18018, "r_x2": 558.20044, "r_y2": 438.80554, "r_x3": 317.95499, "r_y3": 438.80554, "coord_origin": "TOPLEFT" }, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 458.13916, "r_x1": 558.19891, "r_y1": 458.13916, "r_x2": 558.19891, "r_y2": 449.76453000000004, "r_x3": 317.95499, "r_y3": 449.76453000000004, "coord_origin": "TOPLEFT" }, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 469.09818, "r_x1": 558.20239, "r_y1": 469.09818, "r_x2": 558.20239, "r_y2": 460.72354, "r_x3": 317.95499, "r_y3": 460.72354, "coord_origin": "TOPLEFT" }, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 480.05716, "r_x1": 350.32352, "r_y1": 480.05716, "r_x2": 350.32352, "r_y2": 471.68253, "r_x3": 317.64099, "r_y3": 471.68253, "coord_origin": "TOPLEFT" }, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 507.13098, "r_x1": 466.8532400000001, "r_y1": 507.13098, "r_x2": 466.8532400000001, "r_y2": 496.8219, "r_x3": 317.95499, "r_y3": 496.8219, "coord_origin": "TOPLEFT" }, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 520.39917, "r_x1": 558.43085, "r_y1": 520.39917, "r_x2": 558.43085, "r_y2": 512.02454, "r_x3": 317.95499, "r_y3": 512.02454, "coord_origin": "TOPLEFT" }, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 531.35818, "r_x1": 558.20117, "r_y1": 531.35818, "r_x2": 558.20117, "r_y2": 522.9835499999999, "r_x3": 317.95499, "r_y3": 522.9835499999999, "coord_origin": "TOPLEFT" }, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 542.31717, "r_x1": 558.20233, "r_y1": 542.31717, "r_x2": 558.20233, "r_y2": 533.94254, "r_x3": 317.95499, "r_y3": 533.94254, "coord_origin": "TOPLEFT" }, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 553.27617, "r_x1": 334.09296, "r_y1": 553.27617, "r_x2": 334.09296, "r_y2": 544.90155, "r_x3": 317.74899, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.81201, "r_y0": 552.5499, "r_x1": 340.51465, "r_y1": 552.5499, "r_x2": 340.51465, "r_y2": 544.84775, "r_x3": 334.81201, "r_y3": 544.84775, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 341.233, "r_y0": 553.27617, "r_x1": 558.20117, "r_y1": 553.27617, "r_x2": 558.20117, "r_y2": 544.90155, "r_x3": 341.233, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 564.23418, "r_x1": 558.20392, "r_y1": 564.23418, "r_x2": 558.20392, "r_y2": 555.85956, "r_x3": 317.95499, "r_y3": 555.85956, "coord_origin": "TOPLEFT" }, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.19318, "r_x1": 558.2041, "r_y1": 575.19318, "r_x2": 558.2041, "r_y2": 566.8185599999999, "r_x3": 317.95499, "r_y3": 566.8185599999999, "coord_origin": "TOPLEFT" }, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 586.15218, "r_x1": 558.2052, "r_y1": 586.15218, "r_x2": 558.2052, "r_y2": 577.77756, "r_x3": 317.95499, "r_y3": 577.77756, "coord_origin": "TOPLEFT" }, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 597.11118, "r_x1": 558.20233, "r_y1": 597.11118, "r_x2": 558.20233, "r_y2": 588.73656, "r_x3": 317.95499, "r_y3": 588.73656, "coord_origin": "TOPLEFT" }, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 608.0701799999999, "r_x1": 558.1983, "r_y1": 608.0701799999999, "r_x2": 558.1983, "r_y2": 599.69556, "r_x3": 317.95499, "r_y3": 599.69556, "coord_origin": "TOPLEFT" }, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 619.02917, "r_x1": 558.20502, "r_y1": 619.02917, "r_x2": 558.20502, "r_y2": 610.65456, "r_x3": 317.95499, "r_y3": 610.65456, "coord_origin": "TOPLEFT" }, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 629.98817, "r_x1": 558.20337, "r_y1": 629.98817, "r_x2": 558.20337, "r_y2": 621.61356, "r_x3": 317.95499, "r_y3": 621.61356, "coord_origin": "TOPLEFT" }, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 640.94717, "r_x1": 558.2041, "r_y1": 640.94717, "r_x2": 558.2041, "r_y2": 632.5725600000001, "r_x3": 317.95499, "r_y3": 632.5725600000001, "coord_origin": "TOPLEFT" }, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.90617, "r_x1": 558.20245, "r_y1": 651.90617, "r_x2": 558.20245, "r_y2": 643.53156, "r_x3": 317.95499, "r_y3": 643.53156, "coord_origin": "TOPLEFT" }, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.86517, "r_x1": 558.19757, "r_y1": 662.86517, "r_x2": 558.19757, "r_y2": 654.49055, "r_x3": 317.95499, "r_y3": 654.49055, "coord_origin": "TOPLEFT" }, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. 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This is", "orig": ". 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Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "text", "bbox": { "l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT" }, "confidence": 0.9782760143280029, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.04361, "r_y1": 95.34618999999998, "r_x2": 294.04361, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04373, "r_y1": 106.30517999999995, "r_x2": 294.04373, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "detection networks on DocLayNet test set. The MRCNN", "orig": "detection networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.52, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 294.04373, "r_y1": 128.22313999999994, "r_x2": 294.04373, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.18213000000003, "r_x1": 202.43402, "r_y1": 139.18213000000003, "r_x2": 202.43402, "r_y2": 130.70885999999996, "r_x3": 53.79800000000001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 206.08501, "r_y0": 139.20905000000005, "r_x1": 247.14215000000002, "r_y1": 139.20905000000005, "r_x2": 247.14215000000002, "r_y2": 130.71783000000005, "r_x3": 206.08501, "r_y3": 130.71783000000005, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 250.95001, "r_y0": 139.18213000000003, "r_x1": 294.04254, "r_y1": 139.18213000000003, "r_x2": 294.04254, "r_y2": 130.70885999999996, "r_x3": 250.95001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52002, "r_y0": 150.14111000000003, "r_x1": 294.04367, "r_y1": 150.14111000000003, "r_x2": 294.04367, "r_y2": 141.66785000000004, "r_x3": 53.52002, "r_y3": 141.66785000000004, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 161.1001, "r_x1": 294.04373, "r_y1": 161.1001, "r_x2": 294.04373, "r_y2": 152.62683000000004, "r_x3": 53.798019, "r_y3": 152.62683000000004, "coord_origin": "TOPLEFT" }, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 172.05908, "r_x1": 295.64874, "r_y1": 172.05908, "r_x2": 295.64874, "r_y2": 163.58582, "r_x3": 53.798019, "r_y3": 163.58582, "coord_origin": "TOPLEFT" }, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. 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Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 401.22017999999997, "r_x1": 140.3623, "r_y1": 401.22017999999997, "r_x2": 140.3623, "r_y2": 392.84555, "r_x3": 53.79800000000001, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.99001, "r_y0": 401.2291599999999, "r_x1": 175.9695, "r_y1": 401.2291599999999, "r_x2": 175.9695, "r_y2": 392.89041, "r_x3": 142.99001, "r_y3": 392.89041, "coord_origin": "TOPLEFT" }, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 178.951, "r_y0": 401.22017999999997, "r_x1": 294.04083, "r_y1": 401.22017999999997, "r_x2": 294.04083, "r_y2": 392.84555, "r_x3": 178.951, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 412.17917, "r_x1": 295.42493, "r_y1": 412.17917, "r_x2": 295.42493, "r_y2": 403.80453, "r_x3": 53.79800000000001, "r_y3": 403.80453, "coord_origin": "TOPLEFT" }, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 423.13818, "r_x1": 294.04251, "r_y1": 423.13818, "r_x2": 294.04251, "r_y2": 414.76355, "r_x3": 53.528999, "r_y3": 414.76355, "coord_origin": "TOPLEFT" }, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 434.09717, "r_x1": 294.04807, "r_y1": 434.09717, "r_x2": 294.04807, "r_y2": 425.72253, "r_x3": 53.79800000000001, "r_y3": 425.72253, "coord_origin": "TOPLEFT" }, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 445.0561799999999, "r_x1": 256.80627, "r_y1": 445.0561799999999, "r_x2": 256.80627, "r_y2": 436.6815500000001, "r_x3": 53.79800000000001, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 259.04199, "r_y0": 445.06516, "r_x1": 278.10455, "r_y1": 445.06516, "r_x2": 278.10455, "r_y2": 436.72641, "r_x3": 259.04199, "r_y3": 436.72641, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 280.54999, "r_y0": 445.0561799999999, "r_x1": 294.04443, "r_y1": 445.0561799999999, "r_x2": 294.04443, "r_y2": 436.6815500000001, "r_x3": 280.54999, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 456.02413999999993, "r_x1": 78.875587, "r_y1": 456.02413999999993, "r_x2": 78.875587, "r_y2": 447.68539, "r_x3": 53.79800000000001, "r_y3": 447.68539, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.876999, "r_y0": 456.01517, "r_x1": 294.04852, "r_y1": 456.01517, "r_x2": 294.04852, "r_y2": 447.64053, "r_x3": 78.876999, "r_y3": 447.64053, "coord_origin": "TOPLEFT" }, "text": ". For the latter, we instructed annotation staff to minimise", "orig": ". For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 466.97418, "r_x1": 294.04645, "r_y1": 466.97418, "r_x2": 294.04645, "r_y2": 458.59955, "r_x3": 53.79800000000001, "r_y3": 458.59955, "coord_origin": "TOPLEFT" }, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 477.93317, "r_x1": 294.0416, "r_y1": 477.93317, "r_x2": 294.0416, "r_y2": 469.55853, "r_x3": 53.79800000000001, "r_y3": 469.55853, "coord_origin": "TOPLEFT" }, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 488.89117, "r_x1": 294.04538, "r_y1": 488.89117, "r_x2": 294.04538, "r_y2": 480.51654, "r_x3": 53.79800000000001, "r_y3": 480.51654, "coord_origin": "TOPLEFT" }, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 499.85016, "r_x1": 294.04312, "r_y1": 499.85016, "r_x2": 294.04312, "r_y2": 491.47552, "r_x3": 53.79800000000001, "r_y3": 491.47552, "coord_origin": "TOPLEFT" }, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 510.81815, "r_x1": 80.597939, "r_y1": 510.81815, "r_x2": 80.597939, "r_y2": 502.4794, "r_x3": 53.79800000000001, "r_y3": 502.4794, "coord_origin": "TOPLEFT" }, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 83.366997, "r_y0": 510.80917, "r_x1": 294.04483, "r_y1": 510.80917, "r_x2": 294.04483, "r_y2": 502.43454, "r_x3": 83.366997, "r_y3": 502.43454, "coord_origin": "TOPLEFT" }, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 521.7681600000001, "r_x1": 294.25833, "r_y1": 521.7681600000001, "r_x2": 294.25833, "r_y2": 513.3935200000001, "r_x3": 53.79800000000001, "r_y3": 513.3935200000001, "coord_origin": "TOPLEFT" }, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 532.72717, "r_x1": 294.04535, "r_y1": 532.72717, "r_x2": 294.04535, "r_y2": 524.35254, "r_x3": 53.79800000000001, "r_y3": 524.35254, "coord_origin": "TOPLEFT" }, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 543.68617, "r_x1": 294.04535, "r_y1": 543.68617, "r_x2": 294.04535, "r_y2": 535.31155, "r_x3": 53.79800000000001, "r_y3": 535.31155, "coord_origin": "TOPLEFT" }, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 554.64517, "r_x1": 295.02759, "r_y1": 554.64517, "r_x2": 295.02759, "r_y2": 546.27055, "r_x3": 53.79800000000001, "r_y3": 546.27055, "coord_origin": "TOPLEFT" }, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 565.6041700000001, "r_x1": 294.0488, "r_y1": 565.6041700000001, "r_x2": 294.0488, "r_y2": 557.22955, "r_x3": 53.79800000000001, "r_y3": 557.22955, "coord_origin": "TOPLEFT" }, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 576.56317, "r_x1": 281.80457, "r_y1": 576.56317, "r_x2": 281.80457, "r_y2": 568.18855, "r_x3": 53.79800000000001, "r_y3": 568.18855, "coord_origin": "TOPLEFT" }, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 10, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT" }, "confidence": 0.9419495463371277, "cells": [ { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.43901, "r_x1": 59.405277, "r_y1": 598.43901, "r_x2": 59.405277, "r_y2": 588.12991, "r_x3": 53.79800000000001, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.314377, "r_y0": 598.43901, "r_x1": 147.48535, "r_y1": 598.43901, "r_x2": 147.48535, "r_y2": 588.12991, "r_x3": 70.314377, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT" }, "confidence": 0.987605094909668, "cells": [ { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 621.6281700000001, "r_x1": 294.04871, "r_y1": 621.6281700000001, "r_x2": 294.04871, "r_y2": 613.25356, "r_x3": 53.528999, "r_y3": 613.25356, "coord_origin": "TOPLEFT" }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 632.58717, "r_x1": 294.27576, "r_y1": 632.58717, "r_x2": 294.27576, "r_y2": 624.21255, "r_x3": 53.79800000000001, "r_y3": 624.21255, "coord_origin": "TOPLEFT" }, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 643.54617, "r_x1": 294.04144, "r_y1": 643.54617, "r_x2": 294.04144, "r_y2": 635.17155, "r_x3": 53.79800000000001, "r_y3": 635.17155, "coord_origin": "TOPLEFT" }, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 654.50517, "r_x1": 294.04163, "r_y1": 654.50517, "r_x2": 294.04163, "r_y2": 646.13055, "r_x3": 53.79800000000001, "r_y3": 646.13055, "coord_origin": "TOPLEFT" }, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 665.46317, "r_x1": 294.0412, "r_y1": 665.46317, "r_x2": 294.0412, "r_y2": 657.0885499999999, "r_x3": 53.79800000000001, "r_y3": 657.0885499999999, "coord_origin": "TOPLEFT" }, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 676.42217, "r_x1": 155.01054, "r_y1": 676.42217, "r_x2": 155.01054, "r_y2": 668.04755, "r_x3": 53.79800000000001, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.23599, "r_y0": 676.43114, "r_x1": 193.26666, "r_y1": 676.43114, "r_x2": 193.26666, "r_y2": 668.09238, "r_x3": 157.23599, "r_y3": 668.09238, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 195.867, "r_y0": 676.42217, "r_x1": 294.0473, "r_y1": 676.42217, "r_x2": 294.0473, "r_y2": 668.04755, "r_x3": 195.867, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 687.38117, "r_x1": 294.04538, "r_y1": 687.38117, "r_x2": 294.04538, "r_y2": 679.0065500000001, "r_x3": 53.79800000000001, "r_y3": 679.0065500000001, "coord_origin": "TOPLEFT" }, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 698.3401719999999, "r_x1": 295.4281, "r_y1": 698.3401719999999, "r_x2": 295.4281, "r_y2": 689.96555, "r_x3": 53.79800000000001, "r_y3": 689.96555, "coord_origin": "TOPLEFT" }, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.36620999999997, "r_x1": 558.20062, "r_y1": 320.36620999999997, "r_x2": 558.20062, "r_y2": 311.89297, "r_x3": 317.95499, "r_y3": 311.89297, "coord_origin": "TOPLEFT" }, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.3252, "r_x1": 558.20074, "r_y1": 331.3252, "r_x2": 558.20074, "r_y2": 322.85196, "r_x3": 317.95499, "r_y3": 322.85196, "coord_origin": "TOPLEFT" }, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.28418000000005, "r_x1": 445.24207, "r_y1": 342.28418000000005, "r_x2": 445.24207, "r_y2": 333.81094, "r_x3": 317.95499, "r_y3": 333.81094, "coord_origin": "TOPLEFT" }, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9594221711158752, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 392.38617, "r_x1": 558.20416, "r_y1": 392.38617, "r_x2": 558.20416, "r_y2": 384.01154, "r_x3": 317.95499, "r_y3": 384.01154, "coord_origin": "TOPLEFT" }, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 403.34517999999997, "r_x1": 463.04938, "r_y1": 403.34517999999997, "r_x2": 463.04938, "r_y2": 394.97055, "r_x3": 317.95499, "r_y3": 394.97055, "coord_origin": "TOPLEFT" }, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT" }, "confidence": 0.9872915148735046, "cells": [ { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 414.30417, "r_x1": 558.19836, "r_y1": 414.30417, "r_x2": 558.19836, "r_y2": 405.92953, "r_x3": 327.918, "r_y3": 405.92953, "coord_origin": "TOPLEFT" }, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 425.26318, "r_x1": 558.4364, "r_y1": 425.26318, "r_x2": 558.4364, "r_y2": 416.88855, "r_x3": 317.95499, "r_y3": 416.88855, "coord_origin": "TOPLEFT" }, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 436.22116, "r_x1": 558.20563, "r_y1": 436.22116, "r_x2": 558.20563, "r_y2": 427.84653, "r_x3": 317.95499, "r_y3": 427.84653, "coord_origin": "TOPLEFT" }, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 447.18018, "r_x1": 558.20044, "r_y1": 447.18018, "r_x2": 558.20044, "r_y2": 438.80554, "r_x3": 317.95499, "r_y3": 438.80554, "coord_origin": "TOPLEFT" }, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 458.13916, "r_x1": 558.19891, "r_y1": 458.13916, "r_x2": 558.19891, "r_y2": 449.76453000000004, "r_x3": 317.95499, "r_y3": 449.76453000000004, "coord_origin": "TOPLEFT" }, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 469.09818, "r_x1": 558.20239, "r_y1": 469.09818, "r_x2": 558.20239, "r_y2": 460.72354, "r_x3": 317.95499, "r_y3": 460.72354, "coord_origin": "TOPLEFT" }, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 480.05716, "r_x1": 350.32352, "r_y1": 480.05716, "r_x2": 350.32352, "r_y2": 471.68253, "r_x3": 317.64099, "r_y3": 471.68253, "coord_origin": "TOPLEFT" }, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 9, "label": "section_header", "bbox": { "l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT" }, "confidence": 0.9579005241394043, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 507.13098, "r_x1": 466.8532400000001, "r_y1": 507.13098, "r_x2": 466.8532400000001, "r_y2": 496.8219, "r_x3": 317.95499, "r_y3": 496.8219, "coord_origin": "TOPLEFT" }, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT" }, "confidence": 0.987722635269165, "cells": [ { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 520.39917, "r_x1": 558.43085, "r_y1": 520.39917, "r_x2": 558.43085, "r_y2": 512.02454, "r_x3": 317.95499, "r_y3": 512.02454, "coord_origin": "TOPLEFT" }, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 531.35818, "r_x1": 558.20117, "r_y1": 531.35818, "r_x2": 558.20117, "r_y2": 522.9835499999999, "r_x3": 317.95499, "r_y3": 522.9835499999999, "coord_origin": "TOPLEFT" }, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. 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For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 564.23418, "r_x1": 558.20392, "r_y1": 564.23418, "r_x2": 558.20392, "r_y2": 555.85956, "r_x3": 317.95499, "r_y3": 555.85956, "coord_origin": "TOPLEFT" }, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.19318, "r_x1": 558.2041, "r_y1": 575.19318, "r_x2": 558.2041, "r_y2": 566.8185599999999, "r_x3": 317.95499, "r_y3": 566.8185599999999, "coord_origin": "TOPLEFT" }, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 586.15218, "r_x1": 558.2052, "r_y1": 586.15218, "r_x2": 558.2052, "r_y2": 577.77756, "r_x3": 317.95499, "r_y3": 577.77756, "coord_origin": "TOPLEFT" }, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 597.11118, "r_x1": 558.20233, "r_y1": 597.11118, "r_x2": 558.20233, "r_y2": 588.73656, "r_x3": 317.95499, "r_y3": 588.73656, "coord_origin": "TOPLEFT" }, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 608.0701799999999, "r_x1": 558.1983, "r_y1": 608.0701799999999, "r_x2": 558.1983, "r_y2": 599.69556, "r_x3": 317.95499, "r_y3": 599.69556, "coord_origin": "TOPLEFT" }, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 619.02917, "r_x1": 558.20502, "r_y1": 619.02917, "r_x2": 558.20502, "r_y2": 610.65456, "r_x3": 317.95499, "r_y3": 610.65456, "coord_origin": "TOPLEFT" }, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 629.98817, "r_x1": 558.20337, "r_y1": 629.98817, "r_x2": 558.20337, "r_y2": 621.61356, "r_x3": 317.95499, "r_y3": 621.61356, "coord_origin": "TOPLEFT" }, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 640.94717, "r_x1": 558.2041, "r_y1": 640.94717, "r_x2": 558.2041, "r_y2": 632.5725600000001, "r_x3": 317.95499, "r_y3": 632.5725600000001, "coord_origin": "TOPLEFT" }, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.90617, "r_x1": 558.20245, "r_y1": 651.90617, "r_x2": 558.20245, "r_y2": 643.53156, "r_x3": 317.95499, "r_y3": 643.53156, "coord_origin": "TOPLEFT" }, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.86517, "r_x1": 558.19757, "r_y1": 662.86517, "r_x2": 558.19757, "r_y2": 654.49055, "r_x3": 317.95499, "r_y3": 654.49055, "coord_origin": "TOPLEFT" }, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.82317, "r_x1": 558.20404, "r_y1": 673.82317, "r_x2": 558.20404, "r_y2": 665.44855, "r_x3": 317.95499, "r_y3": 665.44855, "coord_origin": "TOPLEFT" }, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.78217, "r_x1": 444.54102, "r_y1": 684.78217, "r_x2": 444.54102, "r_y2": 676.40755, "r_x3": 317.95499, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 446.78900000000004, "r_y0": 684.79114, "r_x1": 461.95261000000005, "r_y1": 684.79114, "r_x2": 461.95261000000005, "r_y2": 676.45238, "r_x3": 446.78900000000004, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 461.95599000000004, "r_y0": 684.78217, "r_x1": 463.96805000000006, "r_y1": 684.78217, "r_x2": 463.96805000000006, "r_y2": 676.40755, "r_x3": 461.95599000000004, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.2170100000001, "r_y0": 684.79114, "r_x1": 485.66998000000007, "r_y1": 684.79114, "r_x2": 485.66998000000007, "r_y2": 676.45238, "r_x3": 466.2170100000001, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 488.1290000000001, "r_y0": 684.78217, "r_x1": 501.89330999999993, "r_y1": 684.78217, "r_x2": 501.89330999999993, "r_y2": 676.40755, "r_x3": 488.1290000000001, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 504.142, "r_y0": 684.79114, "r_x1": 529.21954, "r_y1": 684.79114, "r_x2": 529.21954, "r_y2": 676.45238, "r_x3": 504.142, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 529.22101, "r_y0": 684.78217, "r_x1": 558.20392, "r_y1": 684.78217, "r_x2": 558.20392, "r_y2": 676.40755, "r_x3": 529.22101, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ". This is", "orig": ". 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Nassar, and Peter Staar" }, { "label": "text", "id": 5, "page_no": 5, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT" }, "confidence": 0.9782760143280029, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.04361, "r_y1": 95.34618999999998, "r_x2": 294.04361, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04373, "r_y1": 106.30517999999995, "r_x2": 294.04373, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "detection networks on DocLayNet test set. The MRCNN", "orig": "detection networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.52, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 294.04373, "r_y1": 128.22313999999994, "r_x2": 294.04373, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.18213000000003, "r_x1": 202.43402, "r_y1": 139.18213000000003, "r_x2": 202.43402, "r_y2": 130.70885999999996, "r_x3": 53.79800000000001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 206.08501, "r_y0": 139.20905000000005, "r_x1": 247.14215000000002, "r_y1": 139.20905000000005, "r_x2": 247.14215000000002, "r_y2": 130.71783000000005, "r_x3": 206.08501, "r_y3": 130.71783000000005, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 250.95001, "r_y0": 139.18213000000003, "r_x1": 294.04254, "r_y1": 139.18213000000003, "r_x2": 294.04254, "r_y2": 130.70885999999996, "r_x3": 250.95001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52002, "r_y0": 150.14111000000003, "r_x1": 294.04367, "r_y1": 150.14111000000003, "r_x2": 294.04367, "r_y2": 141.66785000000004, "r_x3": 53.52002, "r_y3": 141.66785000000004, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 161.1001, "r_x1": 294.04373, "r_y1": 161.1001, "r_x2": 294.04373, "r_y2": 152.62683000000004, "r_x3": 53.798019, "r_y3": 152.62683000000004, "coord_origin": "TOPLEFT" }, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 172.05908, "r_x1": 295.64874, "r_y1": 172.05908, "r_x2": 295.64874, "r_y2": 163.58582, "r_x3": 53.798019, "r_y3": 163.58582, "coord_origin": "TOPLEFT" }, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 183.01709000000005, "r_x1": 268.62399, "r_y1": 183.01709000000005, "r_x2": 268.62399, "r_y2": 174.54381999999998, "r_x3": 53.798019, "r_y3": 174.54381999999998, "coord_origin": "TOPLEFT" }, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 390.26117, "r_x1": 295.55612, "r_y1": 390.26117, "r_x2": 295.55612, "r_y2": 381.88654, "r_x3": 53.79800000000001, "r_y3": 381.88654, "coord_origin": "TOPLEFT" }, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 401.22017999999997, "r_x1": 140.3623, "r_y1": 401.22017999999997, "r_x2": 140.3623, "r_y2": 392.84555, "r_x3": 53.79800000000001, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.99001, "r_y0": 401.2291599999999, "r_x1": 175.9695, "r_y1": 401.2291599999999, "r_x2": 175.9695, "r_y2": 392.89041, "r_x3": 142.99001, "r_y3": 392.89041, "coord_origin": "TOPLEFT" }, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 178.951, "r_y0": 401.22017999999997, "r_x1": 294.04083, "r_y1": 401.22017999999997, "r_x2": 294.04083, "r_y2": 392.84555, "r_x3": 178.951, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 412.17917, "r_x1": 295.42493, "r_y1": 412.17917, "r_x2": 295.42493, "r_y2": 403.80453, "r_x3": 53.79800000000001, "r_y3": 403.80453, "coord_origin": "TOPLEFT" }, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 423.13818, "r_x1": 294.04251, "r_y1": 423.13818, "r_x2": 294.04251, "r_y2": 414.76355, "r_x3": 53.528999, "r_y3": 414.76355, "coord_origin": "TOPLEFT" }, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 434.09717, "r_x1": 294.04807, "r_y1": 434.09717, "r_x2": 294.04807, "r_y2": 425.72253, "r_x3": 53.79800000000001, "r_y3": 425.72253, "coord_origin": "TOPLEFT" }, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 445.0561799999999, "r_x1": 256.80627, "r_y1": 445.0561799999999, "r_x2": 256.80627, "r_y2": 436.6815500000001, "r_x3": 53.79800000000001, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 259.04199, "r_y0": 445.06516, "r_x1": 278.10455, "r_y1": 445.06516, "r_x2": 278.10455, "r_y2": 436.72641, "r_x3": 259.04199, "r_y3": 436.72641, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 280.54999, "r_y0": 445.0561799999999, "r_x1": 294.04443, "r_y1": 445.0561799999999, "r_x2": 294.04443, "r_y2": 436.6815500000001, "r_x3": 280.54999, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 456.02413999999993, "r_x1": 78.875587, "r_y1": 456.02413999999993, "r_x2": 78.875587, "r_y2": 447.68539, "r_x3": 53.79800000000001, "r_y3": 447.68539, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.876999, "r_y0": 456.01517, "r_x1": 294.04852, "r_y1": 456.01517, "r_x2": 294.04852, "r_y2": 447.64053, "r_x3": 78.876999, "r_y3": 447.64053, "coord_origin": "TOPLEFT" }, "text": ". For the latter, we instructed annotation staff to minimise", "orig": ". For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 466.97418, "r_x1": 294.04645, "r_y1": 466.97418, "r_x2": 294.04645, "r_y2": 458.59955, "r_x3": 53.79800000000001, "r_y3": 458.59955, "coord_origin": "TOPLEFT" }, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 477.93317, "r_x1": 294.0416, "r_y1": 477.93317, "r_x2": 294.0416, "r_y2": 469.55853, "r_x3": 53.79800000000001, "r_y3": 469.55853, "coord_origin": "TOPLEFT" }, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 488.89117, "r_x1": 294.04538, "r_y1": 488.89117, "r_x2": 294.04538, "r_y2": 480.51654, "r_x3": 53.79800000000001, "r_y3": 480.51654, "coord_origin": "TOPLEFT" }, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 499.85016, "r_x1": 294.04312, "r_y1": 499.85016, "r_x2": 294.04312, "r_y2": 491.47552, "r_x3": 53.79800000000001, "r_y3": 491.47552, "coord_origin": "TOPLEFT" }, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 510.81815, "r_x1": 80.597939, "r_y1": 510.81815, "r_x2": 80.597939, "r_y2": 502.4794, "r_x3": 53.79800000000001, "r_y3": 502.4794, "coord_origin": "TOPLEFT" }, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 83.366997, "r_y0": 510.80917, "r_x1": 294.04483, "r_y1": 510.80917, "r_x2": 294.04483, "r_y2": 502.43454, "r_x3": 83.366997, "r_y3": 502.43454, "coord_origin": "TOPLEFT" }, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 521.7681600000001, "r_x1": 294.25833, "r_y1": 521.7681600000001, "r_x2": 294.25833, "r_y2": 513.3935200000001, "r_x3": 53.79800000000001, "r_y3": 513.3935200000001, "coord_origin": "TOPLEFT" }, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 532.72717, "r_x1": 294.04535, "r_y1": 532.72717, "r_x2": 294.04535, "r_y2": 524.35254, "r_x3": 53.79800000000001, "r_y3": 524.35254, "coord_origin": "TOPLEFT" }, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 543.68617, "r_x1": 294.04535, "r_y1": 543.68617, "r_x2": 294.04535, "r_y2": 535.31155, "r_x3": 53.79800000000001, "r_y3": 535.31155, "coord_origin": "TOPLEFT" }, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 554.64517, "r_x1": 295.02759, "r_y1": 554.64517, "r_x2": 295.02759, "r_y2": 546.27055, "r_x3": 53.79800000000001, "r_y3": 546.27055, "coord_origin": "TOPLEFT" }, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 565.6041700000001, "r_x1": 294.0488, "r_y1": 565.6041700000001, "r_x2": 294.0488, "r_y2": 557.22955, "r_x3": 53.79800000000001, "r_y3": 557.22955, "coord_origin": "TOPLEFT" }, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 576.56317, "r_x1": 281.80457, "r_y1": 576.56317, "r_x2": 281.80457, "r_y2": 568.18855, "r_x3": 53.79800000000001, "r_y3": 568.18855, "coord_origin": "TOPLEFT" }, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity." }, { "label": "section_header", "id": 10, "page_no": 5, "cluster": { "id": 10, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT" }, "confidence": 0.9419495463371277, "cells": [ { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.43901, "r_x1": 59.405277, "r_y1": 598.43901, "r_x2": 59.405277, "r_y2": 588.12991, "r_x3": 53.79800000000001, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.314377, "r_y0": 598.43901, "r_x1": 147.48535, "r_y1": 598.43901, "r_x2": 147.48535, "r_y2": 588.12991, "r_x3": 70.314377, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "5 EXPERIMENTS" }, { "label": "text", "id": 2, "page_no": 5, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT" }, "confidence": 0.987605094909668, "cells": [ { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 621.6281700000001, "r_x1": 294.04871, "r_y1": 621.6281700000001, "r_x2": 294.04871, "r_y2": 613.25356, "r_x3": 53.528999, "r_y3": 613.25356, "coord_origin": "TOPLEFT" }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 632.58717, "r_x1": 294.27576, "r_y1": 632.58717, "r_x2": 294.27576, "r_y2": 624.21255, "r_x3": 53.79800000000001, "r_y3": 624.21255, "coord_origin": "TOPLEFT" }, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 643.54617, "r_x1": 294.04144, "r_y1": 643.54617, "r_x2": 294.04144, "r_y2": 635.17155, "r_x3": 53.79800000000001, "r_y3": 635.17155, "coord_origin": "TOPLEFT" }, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 654.50517, "r_x1": 294.04163, "r_y1": 654.50517, "r_x2": 294.04163, "r_y2": 646.13055, "r_x3": 53.79800000000001, "r_y3": 646.13055, "coord_origin": "TOPLEFT" }, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 665.46317, "r_x1": 294.0412, "r_y1": 665.46317, "r_x2": 294.0412, "r_y2": 657.0885499999999, "r_x3": 53.79800000000001, "r_y3": 657.0885499999999, "coord_origin": "TOPLEFT" }, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 676.42217, "r_x1": 155.01054, "r_y1": 676.42217, "r_x2": 155.01054, "r_y2": 668.04755, "r_x3": 53.79800000000001, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.23599, "r_y0": 676.43114, "r_x1": 193.26666, "r_y1": 676.43114, "r_x2": 193.26666, "r_y2": 668.09238, "r_x3": 157.23599, "r_y3": 668.09238, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 195.867, "r_y0": 676.42217, "r_x1": 294.0473, "r_y1": 676.42217, "r_x2": 294.0473, "r_y2": 668.04755, "r_x3": 195.867, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 687.38117, "r_x1": 294.04538, "r_y1": 687.38117, "r_x2": 294.04538, "r_y2": 679.0065500000001, "r_x3": 53.79800000000001, "r_y3": 679.0065500000001, "coord_origin": "TOPLEFT" }, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 698.3401719999999, "r_x1": 295.4281, "r_y1": 698.3401719999999, "r_x2": 295.4281, "r_y2": 689.96555, "r_x3": 53.79800000000001, "r_y3": 689.96555, "coord_origin": "TOPLEFT" }, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 709.299171, "r_x1": 294.04413, "r_y1": 709.299171, "r_x2": 294.04413, "r_y2": 700.9245530000001, "r_x3": 53.484001, "r_y3": 700.9245530000001, "coord_origin": "TOPLEFT" }, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this" }, { "label": "picture", "id": 6, "page_no": 5, "cluster": { "id": 6, "label": "picture", "bbox": { "l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT" }, "confidence": 0.9782691597938538, "cells": [ { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 349.16577, "r_y0": 252.75427000000002, "r_x1": 352.48175, "r_y1": 252.75427000000002, "r_x2": 352.48175, "r_y2": 246.68017999999995, "r_x3": 349.16577, "r_y3": 246.68017999999995, "coord_origin": "TOPLEFT" }, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 385.93698, "r_y0": 252.75427000000002, "r_x1": 392.56894, "r_y1": 252.75427000000002, "r_x2": 392.56894, "r_y2": 246.68017999999995, "r_x3": 385.93698, "r_y3": 246.68017999999995, 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.36620999999997, "r_x1": 558.20062, "r_y1": 320.36620999999997, "r_x2": 558.20062, "r_y2": 311.89297, "r_x3": 317.95499, "r_y3": 311.89297, "coord_origin": "TOPLEFT" }, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.3252, "r_x1": 558.20074, "r_y1": 331.3252, "r_x2": 558.20074, "r_y2": 322.85196, "r_x3": 317.95499, "r_y3": 322.85196, "coord_origin": "TOPLEFT" }, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.28418000000005, "r_x1": 445.24207, "r_y1": 342.28418000000005, "r_x2": 445.24207, "r_y2": 333.81094, "r_x3": 317.95499, "r_y3": 333.81094, "coord_origin": "TOPLEFT" }, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions." }, { "label": "text", "id": 8, "page_no": 5, "cluster": { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9594221711158752, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 392.38617, "r_x1": 558.20416, "r_y1": 392.38617, "r_x2": 558.20416, "r_y2": 384.01154, "r_x3": 317.95499, "r_y3": 384.01154, "coord_origin": "TOPLEFT" }, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 403.34517999999997, "r_x1": 463.04938, "r_y1": 403.34517999999997, "r_x2": 463.04938, "r_y2": 394.97055, "r_x3": 317.95499, "r_y3": 394.97055, "coord_origin": "TOPLEFT" }, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work." }, { "label": "text", "id": 3, "page_no": 5, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT" }, "confidence": 0.9872915148735046, "cells": [ { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 414.30417, "r_x1": 558.19836, "r_y1": 414.30417, "r_x2": 558.19836, "r_y2": 405.92953, "r_x3": 327.918, "r_y3": 405.92953, "coord_origin": "TOPLEFT" }, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 425.26318, "r_x1": 558.4364, "r_y1": 425.26318, "r_x2": 558.4364, "r_y2": 416.88855, "r_x3": 317.95499, "r_y3": 416.88855, "coord_origin": "TOPLEFT" }, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 436.22116, "r_x1": 558.20563, "r_y1": 436.22116, "r_x2": 558.20563, "r_y2": 427.84653, "r_x3": 317.95499, "r_y3": 427.84653, "coord_origin": "TOPLEFT" }, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 447.18018, "r_x1": 558.20044, "r_y1": 447.18018, "r_x2": 558.20044, "r_y2": 438.80554, "r_x3": 317.95499, "r_y3": 438.80554, "coord_origin": "TOPLEFT" }, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 458.13916, "r_x1": 558.19891, "r_y1": 458.13916, "r_x2": 558.19891, "r_y2": 449.76453000000004, "r_x3": 317.95499, "r_y3": 449.76453000000004, "coord_origin": "TOPLEFT" }, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 469.09818, "r_x1": 558.20239, "r_y1": 469.09818, "r_x2": 558.20239, "r_y2": 460.72354, "r_x3": 317.95499, "r_y3": 460.72354, "coord_origin": "TOPLEFT" }, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 480.05716, "r_x1": 350.32352, "r_y1": 480.05716, "r_x2": 350.32352, "r_y2": 471.68253, "r_x3": 317.64099, "r_y3": 471.68253, "coord_origin": "TOPLEFT" }, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]." }, { "label": "section_header", "id": 9, "page_no": 5, "cluster": { "id": 9, "label": "section_header", "bbox": { "l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT" }, "confidence": 0.9579005241394043, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 507.13098, "r_x1": 466.8532400000001, "r_y1": 507.13098, "r_x2": 466.8532400000001, "r_y2": 496.8219, "r_x3": 317.95499, "r_y3": 496.8219, "coord_origin": "TOPLEFT" }, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Baselines for Object Detection" }, { "label": "text", "id": 1, "page_no": 5, "cluster": { "id": 1, "label": "text", "bbox": { "l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT" }, "confidence": 0.987722635269165, "cells": [ { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 520.39917, "r_x1": 558.43085, "r_y1": 520.39917, "r_x2": 558.43085, "r_y2": 512.02454, "r_x3": 317.95499, "r_y3": 512.02454, "coord_origin": "TOPLEFT" }, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 531.35818, "r_x1": 558.20117, "r_y1": 531.35818, "r_x2": 558.20117, "r_y2": 522.9835499999999, "r_x3": 317.95499, "r_y3": 522.9835499999999, "coord_origin": "TOPLEFT" }, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 542.31717, "r_x1": 558.20233, "r_y1": 542.31717, "r_x2": 558.20233, "r_y2": 533.94254, "r_x3": 317.95499, "r_y3": 533.94254, "coord_origin": "TOPLEFT" }, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 553.27617, "r_x1": 334.09296, "r_y1": 553.27617, "r_x2": 334.09296, "r_y2": 544.90155, "r_x3": 317.74899, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.81201, "r_y0": 552.5499, "r_x1": 340.51465, "r_y1": 552.5499, "r_x2": 340.51465, "r_y2": 544.84775, "r_x3": 334.81201, "r_y3": 544.84775, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 341.233, "r_y0": 553.27617, "r_x1": 558.20117, "r_y1": 553.27617, "r_x2": 558.20117, "r_y2": 544.90155, "r_x3": 341.233, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 564.23418, "r_x1": 558.20392, "r_y1": 564.23418, "r_x2": 558.20392, "r_y2": 555.85956, "r_x3": 317.95499, "r_y3": 555.85956, "coord_origin": "TOPLEFT" }, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.19318, "r_x1": 558.2041, "r_y1": 575.19318, "r_x2": 558.2041, "r_y2": 566.8185599999999, "r_x3": 317.95499, "r_y3": 566.8185599999999, "coord_origin": "TOPLEFT" }, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 586.15218, "r_x1": 558.2052, "r_y1": 586.15218, "r_x2": 558.2052, "r_y2": 577.77756, "r_x3": 317.95499, "r_y3": 577.77756, "coord_origin": "TOPLEFT" }, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 597.11118, "r_x1": 558.20233, "r_y1": 597.11118, "r_x2": 558.20233, "r_y2": 588.73656, "r_x3": 317.95499, "r_y3": 588.73656, "coord_origin": "TOPLEFT" }, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 608.0701799999999, "r_x1": 558.1983, "r_y1": 608.0701799999999, "r_x2": 558.1983, "r_y2": 599.69556, "r_x3": 317.95499, "r_y3": 599.69556, "coord_origin": "TOPLEFT" }, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 619.02917, "r_x1": 558.20502, "r_y1": 619.02917, "r_x2": 558.20502, "r_y2": 610.65456, "r_x3": 317.95499, "r_y3": 610.65456, "coord_origin": "TOPLEFT" }, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 629.98817, "r_x1": 558.20337, "r_y1": 629.98817, "r_x2": 558.20337, "r_y2": 621.61356, "r_x3": 317.95499, "r_y3": 621.61356, "coord_origin": "TOPLEFT" }, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 640.94717, "r_x1": 558.2041, "r_y1": 640.94717, "r_x2": 558.2041, "r_y2": 632.5725600000001, "r_x3": 317.95499, "r_y3": 632.5725600000001, "coord_origin": "TOPLEFT" }, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.90617, "r_x1": 558.20245, "r_y1": 651.90617, "r_x2": 558.20245, "r_y2": 643.53156, "r_x3": 317.95499, "r_y3": 643.53156, "coord_origin": "TOPLEFT" }, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.86517, "r_x1": 558.19757, "r_y1": 662.86517, "r_x2": 558.19757, "r_y2": 654.49055, "r_x3": 317.95499, "r_y3": 654.49055, "coord_origin": "TOPLEFT" }, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.82317, "r_x1": 558.20404, "r_y1": 673.82317, "r_x2": 558.20404, "r_y2": 665.44855, "r_x3": 317.95499, "r_y3": 665.44855, "coord_origin": "TOPLEFT" }, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.78217, "r_x1": 444.54102, "r_y1": 684.78217, "r_x2": 444.54102, "r_y2": 676.40755, "r_x3": 317.95499, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 446.78900000000004, "r_y0": 684.79114, "r_x1": 461.95261000000005, "r_y1": 684.79114, "r_x2": 461.95261000000005, "r_y2": 676.45238, "r_x3": 446.78900000000004, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 461.95599000000004, "r_y0": 684.78217, "r_x1": 463.96805000000006, "r_y1": 684.78217, "r_x2": 463.96805000000006, "r_y2": 676.40755, "r_x3": 461.95599000000004, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.2170100000001, "r_y0": 684.79114, "r_x1": 485.66998000000007, "r_y1": 684.79114, "r_x2": 485.66998000000007, "r_y2": 676.45238, "r_x3": 466.2170100000001, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 488.1290000000001, "r_y0": 684.78217, "r_x1": 501.89330999999993, "r_y1": 684.78217, "r_x2": 501.89330999999993, "r_y2": 676.40755, "r_x3": 488.1290000000001, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 504.142, "r_y0": 684.79114, "r_x1": 529.21954, "r_y1": 684.79114, "r_x2": 529.21954, "r_y2": 676.45238, "r_x3": 504.142, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 529.22101, "r_y0": 684.78217, "r_x1": 558.20392, "r_y1": 684.78217, "r_x2": 558.20392, "r_y2": 676.40755, "r_x3": 529.22101, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ". This is", "orig": ". This is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 695.741173, "r_x1": 410.81366, "r_y1": 695.741173, "r_x2": 410.81366, "r_y2": 687.36655, "r_x3": 317.95499, "r_y3": 687.36655, "coord_origin": "TOPLEFT" }, "text": "not entirely surprising, as", "orig": "not entirely surprising, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 413.05301, "r_y0": 695.750137, "r_x1": 427.67865, "r_y1": 695.750137, "r_x2": 427.67865, "r_y2": 687.41138, "r_x3": 413.05301, "r_y3": 687.41138, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 427.67801, "r_y0": 695.741173, "r_x1": 429.62103, "r_y1": 695.741173, "r_x2": 429.62103, "r_y2": 687.36655, "r_x3": 427.67801, "r_y3": 687.36655, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 431.86099, "r_y0": 695.750137, "r_x1": 450.62881000000004, "r_y1": 695.750137, "r_x2": 450.62881000000004, "r_y2": 687.41138, "r_x3": 431.86099, "r_y3": 687.41138, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 453.082, "r_y0": 695.741173, "r_x1": 466.37402, "r_y1": 695.741173, "r_x2": 466.37402, "r_y2": 687.36655, "r_x3": 453.082, "r_y3": 687.36655, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 468.61499, "r_y0": 695.750137, "r_x1": 492.83208999999994, "r_y1": 695.750137, "r_x2": 492.83208999999994, "r_y2": 687.41138, "r_x3": 468.61499, "r_y3": 687.41138, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 495.28201, "r_y0": 695.741173, "r_x1": 558.2005, "r_y1": 695.741173, "r_x2": 558.2005, "r_y2": 687.36655, "r_x3": 495.28201, "r_y3": 687.36655, "coord_origin": "TOPLEFT" }, "text": "are abundant and", "orig": "are abundant and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 200, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 706.700172, "r_x1": 477.53903, "r_y1": 706.700172, "r_x2": 477.53903, "r_y2": 698.325554, "r_x3": 317.95499, "r_y3": 698.325554, "coord_origin": "TOPLEFT" }, "text": "the most visually distinctive in a document.", "orig": "the most visually distinctive in a document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document." } ], "body": [ { "label": "text", "id": 5, "page_no": 5, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT" }, "confidence": 0.9782760143280029, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.04361, "r_y1": 95.34618999999998, "r_x2": 294.04361, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04373, "r_y1": 106.30517999999995, "r_x2": 294.04373, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "detection networks on DocLayNet test set. The MRCNN", "orig": "detection networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.52, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 294.04373, "r_y1": 128.22313999999994, "r_x2": 294.04373, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.18213000000003, "r_x1": 202.43402, "r_y1": 139.18213000000003, "r_x2": 202.43402, "r_y2": 130.70885999999996, "r_x3": 53.79800000000001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 206.08501, "r_y0": 139.20905000000005, "r_x1": 247.14215000000002, "r_y1": 139.20905000000005, "r_x2": 247.14215000000002, "r_y2": 130.71783000000005, "r_x3": 206.08501, "r_y3": 130.71783000000005, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 8, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 250.95001, "r_y0": 139.18213000000003, "r_x1": 294.04254, "r_y1": 139.18213000000003, "r_x2": 294.04254, "r_y2": 130.70885999999996, "r_x3": 250.95001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 9, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.52002, "r_y0": 150.14111000000003, "r_x1": 294.04367, "r_y1": 150.14111000000003, "r_x2": 294.04367, "r_y2": 141.66785000000004, "r_x3": 53.52002, "r_y3": 141.66785000000004, "coord_origin": "TOPLEFT" }, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 10, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 161.1001, "r_x1": 294.04373, "r_y1": 161.1001, "r_x2": 294.04373, "r_y2": 152.62683000000004, "r_x3": 53.798019, "r_y3": 152.62683000000004, "coord_origin": "TOPLEFT" }, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 11, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 172.05908, "r_x1": 295.64874, "r_y1": 172.05908, "r_x2": 295.64874, "r_y2": 163.58582, "r_x3": 53.798019, "r_y3": 163.58582, "coord_origin": "TOPLEFT" }, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 12, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.798019, "r_y0": 183.01709000000005, "r_x1": 268.62399, "r_y1": 183.01709000000005, "r_x2": 268.62399, "r_y2": 174.54381999999998, "r_x3": 53.798019, "r_y3": 174.54381999999998, "coord_origin": "TOPLEFT" }, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset." }, { "label": "table", "id": 0, "page_no": 5, "cluster": { "id": 0, "label": "table", "bbox": { "l": 62.02753829956055, "t": 195.68006896972656, "r": 285.78955078125, "b": 351.6618957519531, "coord_origin": "TOPLEFT" }, "confidence": 0.9883638620376587, "cells": [ { "index": 13, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 132.36501, "r_y0": 206.34813999999994, "r_x1": 157.99098, "r_y1": 206.34813999999994, "r_x2": 157.99098, "r_y2": 197.97351000000003, "r_x3": 132.36501, "r_y3": 197.97351000000003, "coord_origin": "TOPLEFT" }, "text": "human", "orig": "human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 14, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 173.505, "r_y0": 206.34813999999994, "r_x1": 204.61841, "r_y1": 206.34813999999994, "r_x2": 204.61841, "r_y2": 197.97351000000003, "r_x3": 173.505, "r_y3": 197.97351000000003, 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in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 390.26117, "r_x1": 295.55612, "r_y1": 390.26117, "r_x2": 295.55612, "r_y2": 381.88654, "r_x3": 53.79800000000001, "r_y3": 381.88654, "coord_origin": "TOPLEFT" }, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 401.22017999999997, "r_x1": 140.3623, "r_y1": 401.22017999999997, "r_x2": 140.3623, "r_y2": 392.84555, "r_x3": 53.79800000000001, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 142.99001, "r_y0": 401.2291599999999, "r_x1": 175.9695, "r_y1": 401.2291599999999, "r_x2": 175.9695, "r_y2": 392.89041, "r_x3": 142.99001, "r_y3": 392.89041, "coord_origin": "TOPLEFT" }, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 178.951, "r_y0": 401.22017999999997, "r_x1": 294.04083, "r_y1": 401.22017999999997, "r_x2": 294.04083, "r_y2": 392.84555, "r_x3": 178.951, "r_y3": 392.84555, "coord_origin": "TOPLEFT" }, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 412.17917, "r_x1": 295.42493, "r_y1": 412.17917, "r_x2": 295.42493, "r_y2": 403.80453, "r_x3": 53.79800000000001, "r_y3": 403.80453, "coord_origin": "TOPLEFT" }, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 423.13818, "r_x1": 294.04251, "r_y1": 423.13818, "r_x2": 294.04251, "r_y2": 414.76355, "r_x3": 53.528999, "r_y3": 414.76355, "coord_origin": "TOPLEFT" }, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 434.09717, "r_x1": 294.04807, "r_y1": 434.09717, "r_x2": 294.04807, "r_y2": 425.72253, "r_x3": 53.79800000000001, "r_y3": 425.72253, "coord_origin": "TOPLEFT" }, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 445.0561799999999, "r_x1": 256.80627, "r_y1": 445.0561799999999, "r_x2": 256.80627, "r_y2": 436.6815500000001, "r_x3": 53.79800000000001, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 259.04199, "r_y0": 445.06516, "r_x1": 278.10455, "r_y1": 445.06516, "r_x2": 278.10455, "r_y2": 436.72641, "r_x3": 259.04199, "r_y3": 436.72641, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 280.54999, "r_y0": 445.0561799999999, "r_x1": 294.04443, "r_y1": 445.0561799999999, "r_x2": 294.04443, "r_y2": 436.6815500000001, "r_x3": 280.54999, "r_y3": 436.6815500000001, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 456.02413999999993, "r_x1": 78.875587, "r_y1": 456.02413999999993, "r_x2": 78.875587, "r_y2": 447.68539, "r_x3": 53.79800000000001, "r_y3": 447.68539, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 78.876999, "r_y0": 456.01517, "r_x1": 294.04852, "r_y1": 456.01517, "r_x2": 294.04852, "r_y2": 447.64053, "r_x3": 78.876999, "r_y3": 447.64053, "coord_origin": "TOPLEFT" }, "text": ". For the latter, we instructed annotation staff to minimise", "orig": ". For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 466.97418, "r_x1": 294.04645, "r_y1": 466.97418, "r_x2": 294.04645, "r_y2": 458.59955, "r_x3": 53.79800000000001, "r_y3": 458.59955, "coord_origin": "TOPLEFT" }, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 477.93317, "r_x1": 294.0416, "r_y1": 477.93317, "r_x2": 294.0416, "r_y2": 469.55853, "r_x3": 53.79800000000001, "r_y3": 469.55853, "coord_origin": "TOPLEFT" }, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 488.89117, "r_x1": 294.04538, "r_y1": 488.89117, "r_x2": 294.04538, "r_y2": 480.51654, "r_x3": 53.79800000000001, "r_y3": 480.51654, "coord_origin": "TOPLEFT" }, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 499.85016, "r_x1": 294.04312, "r_y1": 499.85016, "r_x2": 294.04312, "r_y2": 491.47552, "r_x3": 53.79800000000001, "r_y3": 491.47552, "coord_origin": "TOPLEFT" }, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 510.81815, "r_x1": 80.597939, "r_y1": 510.81815, "r_x2": 80.597939, "r_y2": 502.4794, "r_x3": 53.79800000000001, "r_y3": 502.4794, "coord_origin": "TOPLEFT" }, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 83.366997, "r_y0": 510.80917, "r_x1": 294.04483, "r_y1": 510.80917, "r_x2": 294.04483, "r_y2": 502.43454, "r_x3": 83.366997, "r_y3": 502.43454, "coord_origin": "TOPLEFT" }, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 521.7681600000001, "r_x1": 294.25833, "r_y1": 521.7681600000001, "r_x2": 294.25833, "r_y2": 513.3935200000001, "r_x3": 53.79800000000001, "r_y3": 513.3935200000001, "coord_origin": "TOPLEFT" }, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 532.72717, "r_x1": 294.04535, "r_y1": 532.72717, "r_x2": 294.04535, "r_y2": 524.35254, "r_x3": 53.79800000000001, "r_y3": 524.35254, "coord_origin": "TOPLEFT" }, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 543.68617, "r_x1": 294.04535, "r_y1": 543.68617, "r_x2": 294.04535, "r_y2": 535.31155, "r_x3": 53.79800000000001, "r_y3": 535.31155, "coord_origin": "TOPLEFT" }, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 554.64517, "r_x1": 295.02759, "r_y1": 554.64517, "r_x2": 295.02759, "r_y2": 546.27055, "r_x3": 53.79800000000001, "r_y3": 546.27055, "coord_origin": "TOPLEFT" }, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 565.6041700000001, "r_x1": 294.0488, "r_y1": 565.6041700000001, "r_x2": 294.0488, "r_y2": 557.22955, "r_x3": 53.79800000000001, "r_y3": 557.22955, "coord_origin": "TOPLEFT" }, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 576.56317, "r_x1": 281.80457, "r_y1": 576.56317, "r_x2": 281.80457, "r_y2": 568.18855, "r_x3": 53.79800000000001, "r_y3": 568.18855, "coord_origin": "TOPLEFT" }, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity." }, { "label": "section_header", "id": 10, "page_no": 5, "cluster": { "id": 10, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT" }, "confidence": 0.9419495463371277, "cells": [ { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.43901, "r_x1": 59.405277, "r_y1": 598.43901, "r_x2": 59.405277, "r_y2": 588.12991, "r_x3": 53.79800000000001, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 70.314377, "r_y0": 598.43901, "r_x1": 147.48535, "r_y1": 598.43901, "r_x2": 147.48535, "r_y2": 588.12991, "r_x3": 70.314377, "r_y3": 588.12991, "coord_origin": "TOPLEFT" }, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "5 EXPERIMENTS" }, { "label": "text", "id": 2, "page_no": 5, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT" }, "confidence": 0.987605094909668, "cells": [ { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 621.6281700000001, "r_x1": 294.04871, "r_y1": 621.6281700000001, "r_x2": 294.04871, "r_y2": 613.25356, "r_x3": 53.528999, "r_y3": 613.25356, "coord_origin": "TOPLEFT" }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 632.58717, "r_x1": 294.27576, "r_y1": 632.58717, "r_x2": 294.27576, "r_y2": 624.21255, "r_x3": 53.79800000000001, "r_y3": 624.21255, "coord_origin": "TOPLEFT" }, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 643.54617, "r_x1": 294.04144, "r_y1": 643.54617, "r_x2": 294.04144, "r_y2": 635.17155, "r_x3": 53.79800000000001, "r_y3": 635.17155, "coord_origin": "TOPLEFT" }, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 654.50517, "r_x1": 294.04163, "r_y1": 654.50517, "r_x2": 294.04163, "r_y2": 646.13055, "r_x3": 53.79800000000001, "r_y3": 646.13055, "coord_origin": "TOPLEFT" }, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 665.46317, "r_x1": 294.0412, "r_y1": 665.46317, "r_x2": 294.0412, "r_y2": 657.0885499999999, "r_x3": 53.79800000000001, "r_y3": 657.0885499999999, "coord_origin": "TOPLEFT" }, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 676.42217, "r_x1": 155.01054, "r_y1": 676.42217, "r_x2": 155.01054, "r_y2": 668.04755, "r_x3": 53.79800000000001, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 157.23599, "r_y0": 676.43114, "r_x1": 193.26666, "r_y1": 676.43114, "r_x2": 193.26666, "r_y2": 668.09238, "r_x3": 157.23599, "r_y3": 668.09238, "coord_origin": "TOPLEFT" }, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 195.867, "r_y0": 676.42217, "r_x1": 294.0473, "r_y1": 676.42217, "r_x2": 294.0473, "r_y2": 668.04755, "r_x3": 195.867, "r_y3": 668.04755, "coord_origin": "TOPLEFT" }, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 687.38117, "r_x1": 294.04538, "r_y1": 687.38117, "r_x2": 294.04538, "r_y2": 679.0065500000001, "r_x3": 53.79800000000001, "r_y3": 679.0065500000001, "coord_origin": "TOPLEFT" }, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 698.3401719999999, "r_x1": 295.4281, "r_y1": 698.3401719999999, "r_x2": 295.4281, "r_y2": 689.96555, "r_x3": 53.79800000000001, "r_y3": 689.96555, "coord_origin": "TOPLEFT" }, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 709.299171, "r_x1": 294.04413, "r_y1": 709.299171, "r_x2": 294.04413, "r_y2": 700.9245530000001, "r_x3": 53.484001, "r_y3": 700.9245530000001, "coord_origin": "TOPLEFT" }, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this" }, { "label": "picture", "id": 6, "page_no": 5, "cluster": { "id": 6, "label": "picture", "bbox": { "l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT" }, "confidence": 0.9782691597938538, "cells": [ { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 349.16577, "r_y0": 252.75427000000002, "r_x1": 352.48175, "r_y1": 252.75427000000002, "r_x2": 352.48175, "r_y2": 246.68017999999995, "r_x3": 349.16577, "r_y3": 246.68017999999995, "coord_origin": "TOPLEFT" }, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 385.93698, "r_y0": 252.75427000000002, "r_x1": 392.56894, "r_y1": 252.75427000000002, "r_x2": 392.56894, "r_y2": 246.68017999999995, "r_x3": 385.93698, "r_y3": 246.68017999999995, 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 156, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 320.36620999999997, "r_x1": 558.20062, "r_y1": 320.36620999999997, "r_x2": 558.20062, "r_y2": 311.89297, "r_x3": 317.95499, "r_y3": 311.89297, "coord_origin": "TOPLEFT" }, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 157, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 331.3252, "r_x1": 558.20074, "r_y1": 331.3252, "r_x2": 558.20074, "r_y2": 322.85196, "r_x3": 317.95499, "r_y3": 322.85196, "coord_origin": "TOPLEFT" }, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 158, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 342.28418000000005, "r_x1": 445.24207, "r_y1": 342.28418000000005, "r_x2": 445.24207, "r_y2": 333.81094, "r_x3": 317.95499, "r_y3": 333.81094, "coord_origin": "TOPLEFT" }, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions." }, { "label": "text", "id": 8, "page_no": 5, "cluster": { "id": 8, "label": "text", "bbox": { "l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT" }, "confidence": 0.9594221711158752, "cells": [ { "index": 159, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 392.38617, "r_x1": 558.20416, "r_y1": 392.38617, "r_x2": 558.20416, "r_y2": 384.01154, "r_x3": 317.95499, "r_y3": 384.01154, "coord_origin": "TOPLEFT" }, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 160, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 403.34517999999997, "r_x1": 463.04938, "r_y1": 403.34517999999997, "r_x2": 463.04938, "r_y2": 394.97055, "r_x3": 317.95499, "r_y3": 394.97055, "coord_origin": "TOPLEFT" }, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work." }, { "label": "text", "id": 3, "page_no": 5, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT" }, "confidence": 0.9872915148735046, "cells": [ { "index": 161, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 414.30417, "r_x1": 558.19836, "r_y1": 414.30417, "r_x2": 558.19836, "r_y2": 405.92953, "r_x3": 327.918, "r_y3": 405.92953, "coord_origin": "TOPLEFT" }, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 162, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 425.26318, "r_x1": 558.4364, "r_y1": 425.26318, "r_x2": 558.4364, "r_y2": 416.88855, "r_x3": 317.95499, "r_y3": 416.88855, "coord_origin": "TOPLEFT" }, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 163, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 436.22116, "r_x1": 558.20563, "r_y1": 436.22116, "r_x2": 558.20563, "r_y2": 427.84653, "r_x3": 317.95499, "r_y3": 427.84653, "coord_origin": "TOPLEFT" }, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 164, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 447.18018, "r_x1": 558.20044, "r_y1": 447.18018, "r_x2": 558.20044, "r_y2": 438.80554, "r_x3": 317.95499, "r_y3": 438.80554, "coord_origin": "TOPLEFT" }, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 165, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 458.13916, "r_x1": 558.19891, "r_y1": 458.13916, "r_x2": 558.19891, "r_y2": 449.76453000000004, "r_x3": 317.95499, "r_y3": 449.76453000000004, "coord_origin": "TOPLEFT" }, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 166, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 469.09818, "r_x1": 558.20239, "r_y1": 469.09818, "r_x2": 558.20239, "r_y2": 460.72354, "r_x3": 317.95499, "r_y3": 460.72354, "coord_origin": "TOPLEFT" }, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 167, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.64099, "r_y0": 480.05716, "r_x1": 350.32352, "r_y1": 480.05716, "r_x2": 350.32352, "r_y2": 471.68253, "r_x3": 317.64099, "r_y3": 471.68253, "coord_origin": "TOPLEFT" }, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]." }, { "label": "section_header", "id": 9, "page_no": 5, "cluster": { "id": 9, "label": "section_header", "bbox": { "l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT" }, "confidence": 0.9579005241394043, "cells": [ { "index": 168, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 507.13098, "r_x1": 466.8532400000001, "r_y1": 507.13098, "r_x2": 466.8532400000001, "r_y2": 496.8219, "r_x3": 317.95499, "r_y3": 496.8219, "coord_origin": "TOPLEFT" }, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Baselines for Object Detection" }, { "label": "text", "id": 1, "page_no": 5, "cluster": { "id": 1, "label": "text", "bbox": { "l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT" }, "confidence": 0.987722635269165, "cells": [ { "index": 169, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 520.39917, "r_x1": 558.43085, "r_y1": 520.39917, "r_x2": 558.43085, "r_y2": 512.02454, "r_x3": 317.95499, "r_y3": 512.02454, "coord_origin": "TOPLEFT" }, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 170, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 531.35818, "r_x1": 558.20117, "r_y1": 531.35818, "r_x2": 558.20117, "r_y2": 522.9835499999999, "r_x3": 317.95499, "r_y3": 522.9835499999999, "coord_origin": "TOPLEFT" }, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 171, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 542.31717, "r_x1": 558.20233, "r_y1": 542.31717, "r_x2": 558.20233, "r_y2": 533.94254, "r_x3": 317.95499, "r_y3": 533.94254, "coord_origin": "TOPLEFT" }, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.74899, "r_y0": 553.27617, "r_x1": 334.09296, "r_y1": 553.27617, "r_x2": 334.09296, "r_y2": 544.90155, "r_x3": 317.74899, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.81201, "r_y0": 552.5499, "r_x1": 340.51465, "r_y1": 552.5499, "r_x2": 340.51465, "r_y2": 544.84775, "r_x3": 334.81201, "r_y3": 544.84775, "coord_origin": "TOPLEFT" }, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 341.233, "r_y0": 553.27617, "r_x1": 558.20117, "r_y1": 553.27617, "r_x2": 558.20117, "r_y2": 544.90155, "r_x3": 341.233, "r_y3": 544.90155, "coord_origin": "TOPLEFT" }, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 564.23418, "r_x1": 558.20392, "r_y1": 564.23418, "r_x2": 558.20392, "r_y2": 555.85956, "r_x3": 317.95499, "r_y3": 555.85956, "coord_origin": "TOPLEFT" }, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 575.19318, "r_x1": 558.2041, "r_y1": 575.19318, "r_x2": 558.2041, "r_y2": 566.8185599999999, "r_x3": 317.95499, "r_y3": 566.8185599999999, "coord_origin": "TOPLEFT" }, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 586.15218, "r_x1": 558.2052, "r_y1": 586.15218, "r_x2": 558.2052, "r_y2": 577.77756, "r_x3": 317.95499, "r_y3": 577.77756, "coord_origin": "TOPLEFT" }, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 597.11118, "r_x1": 558.20233, "r_y1": 597.11118, "r_x2": 558.20233, "r_y2": 588.73656, "r_x3": 317.95499, "r_y3": 588.73656, "coord_origin": "TOPLEFT" }, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 608.0701799999999, "r_x1": 558.1983, "r_y1": 608.0701799999999, "r_x2": 558.1983, "r_y2": 599.69556, "r_x3": 317.95499, "r_y3": 599.69556, "coord_origin": "TOPLEFT" }, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 619.02917, "r_x1": 558.20502, "r_y1": 619.02917, "r_x2": 558.20502, "r_y2": 610.65456, "r_x3": 317.95499, "r_y3": 610.65456, "coord_origin": "TOPLEFT" }, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 629.98817, "r_x1": 558.20337, "r_y1": 629.98817, "r_x2": 558.20337, "r_y2": 621.61356, "r_x3": 317.95499, "r_y3": 621.61356, "coord_origin": "TOPLEFT" }, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 640.94717, "r_x1": 558.2041, "r_y1": 640.94717, "r_x2": 558.2041, "r_y2": 632.5725600000001, "r_x3": 317.95499, "r_y3": 632.5725600000001, "coord_origin": "TOPLEFT" }, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 651.90617, "r_x1": 558.20245, "r_y1": 651.90617, "r_x2": 558.20245, "r_y2": 643.53156, "r_x3": 317.95499, "r_y3": 643.53156, "coord_origin": "TOPLEFT" }, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 662.86517, "r_x1": 558.19757, "r_y1": 662.86517, "r_x2": 558.19757, "r_y2": 654.49055, "r_x3": 317.95499, "r_y3": 654.49055, "coord_origin": "TOPLEFT" }, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 673.82317, "r_x1": 558.20404, "r_y1": 673.82317, "r_x2": 558.20404, "r_y2": 665.44855, "r_x3": 317.95499, "r_y3": 665.44855, "coord_origin": "TOPLEFT" }, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 684.78217, "r_x1": 444.54102, "r_y1": 684.78217, "r_x2": 444.54102, "r_y2": 676.40755, "r_x3": 317.95499, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 446.78900000000004, "r_y0": 684.79114, "r_x1": 461.95261000000005, "r_y1": 684.79114, "r_x2": 461.95261000000005, "r_y2": 676.45238, "r_x3": 446.78900000000004, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 461.95599000000004, "r_y0": 684.78217, "r_x1": 463.96805000000006, "r_y1": 684.78217, "r_x2": 463.96805000000006, "r_y2": 676.40755, "r_x3": 461.95599000000004, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 466.2170100000001, "r_y0": 684.79114, "r_x1": 485.66998000000007, "r_y1": 684.79114, "r_x2": 485.66998000000007, "r_y2": 676.45238, "r_x3": 466.2170100000001, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 488.1290000000001, "r_y0": 684.78217, "r_x1": 501.89330999999993, "r_y1": 684.78217, "r_x2": 501.89330999999993, "r_y2": 676.40755, "r_x3": 488.1290000000001, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 504.142, "r_y0": 684.79114, "r_x1": 529.21954, "r_y1": 684.79114, "r_x2": 529.21954, "r_y2": 676.45238, "r_x3": 504.142, "r_y3": 676.45238, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 529.22101, "r_y0": 684.78217, "r_x1": 558.20392, "r_y1": 684.78217, "r_x2": 558.20392, "r_y2": 676.40755, "r_x3": 529.22101, "r_y3": 676.40755, "coord_origin": "TOPLEFT" }, "text": ". This is", "orig": ". 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Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document." } ], "headers": [ { "label": "page_header", "id": 11, "page_no": 5, "cluster": { "id": 11, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.8662876486778259, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar" } ] } }, { "page_no": 6, "size": { "width": 612.0, "height": 792.0 }, "parsed_page": { "dimension": { "angle": 0.0, "rect": { "r_x0": 0.0, "r_y0": 0.0, "r_x1": 612.0, "r_y1": 0.0, "r_x2": 612.0, "r_y2": 792.0, "r_x3": 0.0, "r_y3": 792.0, "coord_origin": "BOTTOMLEFT" }, "boundary_type": "crop_box", "art_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "bleed_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "crop_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "media_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "trim_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" } }, "bitmap_resources": [], "char_cells": [], "word_cells": [], "textline_cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.0437, "r_y1": 95.34618999999998, "r_x2": 294.0437, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04376, "r_y1": 106.30517999999995, "r_x2": 294.04376, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 117.26415999999995, "r_x1": 295.64865, "r_y1": 117.26415999999995, "r_x2": 295.64865, "r_y2": 108.79088999999999, "r_x3": 53.79800000000001, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "class label sets. 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false }, { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 343.14618, "r_x1": 294.04153, "r_y1": 343.14618, "r_x2": 294.04153, "r_y2": 334.77155, "r_x3": 53.79800000000001, "r_y3": 334.77155, "coord_origin": "TOPLEFT" }, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 52.785, "r_y0": 354.10516000000007, "r_x1": 295.55835, "r_y1": 354.10516000000007, "r_x2": 295.55835, "r_y2": 345.73053, "r_x3": 52.785, "r_y3": 345.73053, "coord_origin": "TOPLEFT" }, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "orig": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 365.06418, "r_x1": 294.04535, "r_y1": 365.06418, "r_x2": 294.04535, "r_y2": 356.6895400000001, "r_x3": 53.79800000000001, "r_y3": 356.6895400000001, "coord_origin": "TOPLEFT" }, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 376.02316, "r_x1": 295.4281, "r_y1": 376.02316, "r_x2": 295.4281, "r_y2": 367.64853, "r_x3": 53.79800000000001, "r_y3": 367.64853, "coord_origin": "TOPLEFT" }, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 386.98218, "r_x1": 295.55667, "r_y1": 386.98218, "r_x2": 295.55667, "r_y2": 378.60754, "r_x3": 53.484001, "r_y3": 378.60754, "coord_origin": "TOPLEFT" }, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 397.94116, "r_x1": 294.04865, "r_y1": 397.94116, "r_x2": 294.04865, "r_y2": 389.56653, "r_x3": 53.79800000000001, "r_y3": 389.56653, "coord_origin": "TOPLEFT" }, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 408.90018, "r_x1": 294.04376, "r_y1": 408.90018, "r_x2": 294.04376, "r_y2": 400.52554000000003, "r_x3": 53.79800000000001, "r_y3": 400.52554000000003, "coord_origin": "TOPLEFT" }, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 419.85919, "r_x1": 295.42459, "r_y1": 419.85919, "r_x2": 295.42459, "r_y2": 411.48456, "r_x3": 53.79800000000001, "r_y3": 411.48456, "coord_origin": "TOPLEFT" }, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 430.81818, "r_x1": 294.04709, "r_y1": 430.81818, "r_x2": 294.04709, "r_y2": 422.44354, "r_x3": 53.79800000000001, "r_y3": 422.44354, "coord_origin": "TOPLEFT" }, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 441.7771599999999, "r_x1": 294.04535, "r_y1": 441.7771599999999, "r_x2": 294.04535, "r_y2": 433.40253000000007, "r_x3": 53.466999, "r_y3": 433.40253000000007, "coord_origin": "TOPLEFT" }, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 452.73517, "r_x1": 295.42902, "r_y1": 452.73517, "r_x2": 295.42902, "r_y2": 444.36053000000004, "r_x3": 53.79800000000001, "r_y3": 444.36053000000004, "coord_origin": "TOPLEFT" }, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 463.69418, "r_x1": 295.03122, "r_y1": 463.69418, "r_x2": 295.03122, "r_y2": 455.31955, "r_x3": 53.528999, "r_y3": 455.31955, "coord_origin": "TOPLEFT" }, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 474.65317, "r_x1": 295.42154, "r_y1": 474.65317, "r_x2": 295.42154, "r_y2": 466.27853, "r_x3": 53.466999, "r_y3": 466.27853, "coord_origin": "TOPLEFT" }, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 485.61218, "r_x1": 294.04553, "r_y1": 485.61218, "r_x2": 294.04553, "r_y2": 477.23755, "r_x3": 53.528999, "r_y3": 477.23755, "coord_origin": "TOPLEFT" }, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 496.57117, "r_x1": 295.55646, "r_y1": 496.57117, "r_x2": 295.55646, "r_y2": 488.19653, "r_x3": 53.79800000000001, "r_y3": 488.19653, "coord_origin": "TOPLEFT" }, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 507.53018, "r_x1": 294.04715, "r_y1": 507.53018, "r_x2": 294.04715, "r_y2": 499.15555, "r_x3": 53.79800000000001, "r_y3": 499.15555, "coord_origin": "TOPLEFT" }, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 518.4891700000001, "r_x1": 294.04245, "r_y1": 518.4891700000001, "r_x2": 294.04245, "r_y2": 510.11453, "r_x3": 53.79800000000001, "r_y3": 510.11453, "coord_origin": "TOPLEFT" }, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 529.44818, "r_x1": 191.47707, "r_y1": 529.44818, "r_x2": 191.47707, "r_y2": 521.0735500000001, "r_x3": 53.79800000000001, "r_y3": 521.0735500000001, "coord_origin": "TOPLEFT" }, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.81902, "r_x1": 164.32898, "r_y1": 552.81902, "r_x2": 164.32898, "r_y2": 542.50992, "r_x3": 53.79800000000001, "r_y3": 542.50992, "coord_origin": "TOPLEFT" }, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 566.08617, "r_x1": 294.04333, "r_y1": 566.08617, "r_x2": 294.04333, "r_y2": 557.71155, "r_x3": 53.528999, "r_y3": 557.71155, "coord_origin": "TOPLEFT" }, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 577.04517, "r_x1": 294.04712, "r_y1": 577.04517, "r_x2": 294.04712, "r_y2": 568.6705499999999, "r_x3": 53.79800000000001, "r_y3": 568.6705499999999, "coord_origin": "TOPLEFT" }, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 588.0041699999999, "r_x1": 294.04538, "r_y1": 588.0041699999999, "r_x2": 294.04538, "r_y2": 579.62955, "r_x3": 53.79800000000001, "r_y3": 579.62955, "coord_origin": "TOPLEFT" }, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.96317, "r_x1": 294.04535, "r_y1": 598.96317, "r_x2": 294.04535, "r_y2": 590.5885499999999, "r_x3": 53.79800000000001, "r_y3": 590.5885499999999, "coord_origin": "TOPLEFT" }, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 609.92216, "r_x1": 294.04266, "r_y1": 609.92216, "r_x2": 294.04266, "r_y2": 601.54755, "r_x3": 53.79800000000001, "r_y3": 601.54755, "coord_origin": "TOPLEFT" }, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 620.88118, "r_x1": 163.59247, "r_y1": 620.88118, "r_x2": 163.59247, "r_y2": 612.50656, "r_x3": 53.79800000000001, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 166.26401, "r_y0": 620.89015, "r_x1": 194.97244, "r_y1": 620.89015, "r_x2": 194.97244, "r_y2": 612.55139, "r_x3": 166.26401, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 194.994, "r_y0": 620.15491, "r_x1": 204.1756, "r_y1": 620.15491, "r_x2": 204.1756, "r_y2": 612.45276, "r_x3": 194.994, "r_y3": 612.45276, "coord_origin": "TOPLEFT" }, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.17599, "r_y0": 620.89015, "r_x1": 219.33961000000002, "r_y1": 620.89015, "r_x2": 219.33961000000002, "r_y2": 612.55139, "r_x3": 204.17599, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.849, "r_y0": 620.88118, "r_x1": 294.04828, "r_y1": 620.88118, "r_x2": 294.04828, "r_y2": 612.50656, "r_x3": 219.849, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 631.84018, "r_x1": 294.04709, "r_y1": 631.84018, "r_x2": 294.04709, "r_y2": 623.46556, "r_x3": 53.79800000000001, "r_y3": 623.46556, "coord_origin": "TOPLEFT" }, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 642.79817, "r_x1": 295.55679, "r_y1": 642.79817, "r_x2": 295.55679, "r_y2": 634.42355, "r_x3": 53.79800000000001, "r_y3": 634.42355, "coord_origin": "TOPLEFT" }, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.75717, "r_x1": 294.04715, "r_y1": 653.75717, "r_x2": 294.04715, "r_y2": 645.38255, "r_x3": 53.79800000000001, "r_y3": 645.38255, "coord_origin": "TOPLEFT" }, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.71617, "r_x1": 294.04715, "r_y1": 664.71617, "r_x2": 294.04715, "r_y2": 656.34155, "r_x3": 53.79800000000001, "r_y3": 656.34155, "coord_origin": "TOPLEFT" }, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 675.67517, "r_x1": 294.04712, "r_y1": 675.67517, "r_x2": 294.04712, "r_y2": 667.30055, "r_x3": 53.79800000000001, "r_y3": 667.30055, "coord_origin": "TOPLEFT" }, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 686.63417, "r_x1": 294.25174, "r_y1": 686.63417, "r_x2": 294.25174, "r_y2": 678.25955, "r_x3": 53.466999, "r_y3": 678.25955, "coord_origin": "TOPLEFT" }, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.59317, "r_x1": 294.04639, "r_y1": 697.59317, "r_x2": 294.04639, "r_y2": 689.21855, "r_x3": 53.79800000000001, "r_y3": 689.21855, "coord_origin": "TOPLEFT" }, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.55217, "r_x1": 294.04712, "r_y1": 708.55217, "r_x2": 294.04712, "r_y2": 700.177551, "r_x3": 53.79800000000001, "r_y3": 700.177551, "coord_origin": "TOPLEFT" }, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.659, "r_y0": 95.34625000000017, "r_x1": 558.20068, "r_y1": 95.34625000000017, "r_x2": 558.20068, "r_y2": 86.87298999999996, "r_x3": 317.659, "r_y3": 86.87298999999996, "coord_origin": "TOPLEFT" }, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.30524000000003, "r_x1": 559.73401, "r_y1": 106.30524000000003, "r_x2": 559.73401, "r_y2": 97.83196999999996, "r_x3": 317.95499, "r_y3": 97.83196999999996, "coord_origin": "TOPLEFT" }, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.26422000000014, "r_x1": 467.72089, "r_y1": 117.26422000000014, "r_x2": 467.72089, "r_y2": 108.79094999999995, "r_x3": 317.95499, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "Naive page-wise split will result in", "orig": "Naive page-wise split will result in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 471.90900000000005, "r_y0": 115.19768999999997, "r_x1": 477.53900000000004, "r_y1": 115.19768999999997, "r_x2": 477.53900000000004, "r_y2": 107.12798999999995, "r_x3": 471.90900000000005, "r_y3": 107.12798999999995, "coord_origin": "TOPLEFT" }, "text": "GLYPH", "orig": "GLYPH", "text_direction": "left_to_right", "confidence": 1.0, 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"orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.70401, "r_y0": 361.70514, "r_x1": 502.86761, "r_y1": 361.70514, "r_x2": 502.86761, "r_y2": 353.36639, "r_x3": 487.70401, "r_y3": 353.36639, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 506.05499, "r_y0": 361.69617000000005, "r_x1": 559.58496, "r_y1": 361.69617000000005, "r_x2": 559.58496, "r_y2": 353.32153, "r_x3": 506.05499, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 372.65518, "r_x1": 558.2002, "r_y1": 372.65518, "r_x2": 558.2002, "r_y2": 364.28054999999995, "r_x3": 317.686, "r_y3": 364.28054999999995, "coord_origin": "TOPLEFT" }, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 383.61316, "r_x1": 558.36877, "r_y1": 383.61316, "r_x2": 558.36877, "r_y2": 375.23853, "r_x3": 317.95499, "r_y3": 375.23853, "coord_origin": "TOPLEFT" }, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 394.57217, "r_x1": 558.19958, "r_y1": 394.57217, "r_x2": 558.19958, "r_y2": 386.19754, "r_x3": 317.95499, "r_y3": 386.19754, "coord_origin": "TOPLEFT" }, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 405.53116000000006, "r_x1": 439.49454, "r_y1": 405.53116000000006, "r_x2": 439.49454, "r_y2": 397.15652, "r_x3": 317.95499, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 441.728, "r_y0": 405.54013, "r_x1": 481.8616, "r_y1": 405.54013, "r_x2": 481.8616, "r_y2": 397.20139, "r_x3": 441.728, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.74298, "r_y0": 405.53116000000006, "r_x1": 498.23743, "r_y1": 405.53116000000006, "r_x2": 498.23743, "r_y2": 397.15652, "r_x3": 484.74298, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.47299, "r_y0": 405.54013, "r_x1": 543.95105, "r_y1": 405.54013, "r_x2": 543.95105, "r_y2": 397.20139, "r_x3": 500.47299, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 546.83197, "r_y0": 405.53116000000006, "r_x1": 558.20142, "r_y1": 405.53116000000006, "r_x2": 558.20142, "r_y2": 397.15652, "r_x3": 546.83197, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 416.49017, "r_x1": 352.37698, "r_y1": 416.49017, "r_x2": 352.37698, "r_y2": 408.11553999999995, "r_x3": 317.95496, "r_y3": 408.11553999999995, "coord_origin": "TOPLEFT" }, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 439.70398, "r_x1": 549.8606, "r_y1": 439.70398, "r_x2": 549.8606, "r_y2": 429.3949, "r_x3": 317.95496, "r_y3": 429.3949, "coord_origin": "TOPLEFT" }, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 452.97116, "r_x1": 558.36884, "r_y1": 452.97116, "r_x2": 558.36884, "r_y2": 444.59653, "r_x3": 317.95499, "r_y3": 444.59653, "coord_origin": "TOPLEFT" }, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 463.93018, "r_x1": 559.19189, "r_y1": 463.93018, "r_x2": 559.19189, "r_y2": 455.55554, "r_x3": 317.95499, "r_y3": 455.55554, "coord_origin": "TOPLEFT" }, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 474.88916, "r_x1": 559.58185, "r_y1": 474.88916, "r_x2": 559.58185, "r_y2": 466.51453, "r_x3": 317.95499, "r_y3": 466.51453, "coord_origin": "TOPLEFT" }, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 485.84818, "r_x1": 558.20605, "r_y1": 485.84818, "r_x2": 558.20605, "r_y2": 477.47354, "r_x3": 317.95499, "r_y3": 477.47354, "coord_origin": "TOPLEFT" }, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 496.80716, "r_x1": 559.58203, "r_y1": 496.80716, "r_x2": 559.58203, "r_y2": 488.43253, "r_x3": 317.95499, "r_y3": 488.43253, "coord_origin": "TOPLEFT" }, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 507.76617, "r_x1": 558.20032, "r_y1": 507.76617, "r_x2": 558.20032, "r_y2": 499.39154, "r_x3": 317.686, "r_y3": 499.39154, "coord_origin": "TOPLEFT" }, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 518.72516, "r_x1": 559.5849, "r_y1": 518.72516, "r_x2": 559.5849, "r_y2": 510.35052, "r_x3": 317.95499, "r_y3": 510.35052, "coord_origin": "TOPLEFT" }, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 529.68417, "r_x1": 559.71381, "r_y1": 529.68417, "r_x2": 559.71381, "r_y2": 521.30954, "r_x3": 317.95499, "r_y3": 521.30954, "coord_origin": "TOPLEFT" }, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 540.64317, "r_x1": 559.18707, "r_y1": 540.64317, "r_x2": 559.18707, "r_y2": 532.26855, "r_x3": 317.95499, "r_y3": 532.26855, "coord_origin": "TOPLEFT" }, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.60117, "r_x1": 559.71313, "r_y1": 551.60117, "r_x2": 559.71313, "r_y2": 543.22655, "r_x3": 317.95499, "r_y3": 543.22655, "coord_origin": "TOPLEFT" }, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 562.56017, "r_x1": 388.35168, "r_y1": 562.56017, "r_x2": 388.35168, "r_y2": 554.18555, "r_x3": 317.62299, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "wise splitting gains", "orig": "wise splitting gains", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 391.36499, "r_y0": 560.87517, "r_x1": 393.98318, "r_y1": 560.87517, "r_x2": 393.98318, "r_y2": 552.50055, "r_x3": 391.36499, "r_y3": 552.50055, "coord_origin": "TOPLEFT" }, "text": "\u02dc", "orig": "\u02dc", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 200, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 390.59, "r_y0": 562.56017, "r_x1": 559.58191, "r_y1": 562.56017, "r_x2": 559.58191, "r_y2": 554.18555, "r_x3": 390.59, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "10% in mAP over the document-wise splitting.", "orig": "10% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 201, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 573.51917, "r_x1": 558.20032, "r_y1": 573.51917, "r_x2": 558.20032, "r_y2": 565.14455, "r_x3": 317.686, "r_y3": 565.14455, "coord_origin": "TOPLEFT" }, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 202, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 584.4781800000001, "r_x1": 558.20508, "r_y1": 584.4781800000001, "r_x2": 558.20508, "r_y2": 576.10356, "r_x3": 317.95499, "r_y3": 576.10356, "coord_origin": "TOPLEFT" }, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 203, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 595.43718, "r_x1": 348.50354, "r_y1": 595.43718, "r_x2": 348.50354, "r_y2": 587.06256, "r_x3": 317.95499, "r_y3": 587.06256, "coord_origin": "TOPLEFT" }, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 204, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 618.65102, "r_x1": 418.54776, "r_y1": 618.65102, "r_x2": 418.54776, "r_y2": 608.34192, "r_x3": 317.95499, "r_y3": 608.34192, "coord_origin": "TOPLEFT" }, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 205, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 631.91817, "r_x1": 558.20575, "r_y1": 631.91817, "r_x2": 558.20575, "r_y2": 623.54355, "r_x3": 317.686, "r_y3": 623.54355, "coord_origin": "TOPLEFT" }, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "orig": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 206, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.87717, "r_x1": 558.20624, "r_y1": 642.87717, "r_x2": 558.20624, "r_y2": 634.50255, "r_x3": 317.95499, "r_y3": 634.50255, "coord_origin": "TOPLEFT" }, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 207, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 653.83617, "r_x1": 558.20319, "r_y1": 653.83617, "r_x2": 558.20319, "r_y2": 645.46155, "r_x3": 317.686, "r_y3": 645.46155, "coord_origin": "TOPLEFT" }, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 208, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 664.79517, "r_x1": 558.74377, "r_y1": 664.79517, "r_x2": 558.74377, "r_y2": 656.42055, "r_x3": 317.95499, "r_y3": 656.42055, "coord_origin": "TOPLEFT" }, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 209, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.7541699999999, "r_x1": 558.20398, "r_y1": 675.7541699999999, "r_x2": 558.20398, "r_y2": 667.37955, "r_x3": 317.95499, "r_y3": 667.37955, "coord_origin": "TOPLEFT" }, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 210, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 686.71317, "r_x1": 558.20062, "r_y1": 686.71317, "r_x2": 558.20062, "r_y2": 678.3385499999999, "r_x3": 317.95499, "r_y3": 678.3385499999999, "coord_origin": "TOPLEFT" }, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 211, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.672173, "r_x1": 558.20343, "r_y1": 697.672173, "r_x2": 558.20343, "r_y2": 689.29755, "r_x3": 317.95499, "r_y3": 689.29755, "coord_origin": "TOPLEFT" }, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 212, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.631172, "r_x1": 531.07666, "r_y1": 708.631172, "r_x2": 531.07666, "r_y2": 700.256554, "r_x3": 317.95499, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. 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The reduced label sets were obtained by ei-", "orig": "class label sets. 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319.56992, "coord_origin": "TOPLEFT" }, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 5, "label": "text", "bbox": { "l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT" }, "confidence": 0.9857739210128784, "cells": [ { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 343.14618, "r_x1": 294.04153, "r_y1": 343.14618, "r_x2": 294.04153, "r_y2": 334.77155, "r_x3": 53.79800000000001, "r_y3": 334.77155, "coord_origin": "TOPLEFT" }, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 52.785, "r_y0": 354.10516000000007, "r_x1": 295.55835, "r_y1": 354.10516000000007, "r_x2": 295.55835, "r_y2": 345.73053, "r_x3": 52.785, "r_y3": 345.73053, "coord_origin": "TOPLEFT" }, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "orig": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 365.06418, "r_x1": 294.04535, "r_y1": 365.06418, "r_x2": 294.04535, "r_y2": 356.6895400000001, "r_x3": 53.79800000000001, "r_y3": 356.6895400000001, "coord_origin": "TOPLEFT" }, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 376.02316, "r_x1": 295.4281, "r_y1": 376.02316, "r_x2": 295.4281, "r_y2": 367.64853, "r_x3": 53.79800000000001, "r_y3": 367.64853, "coord_origin": "TOPLEFT" }, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 386.98218, "r_x1": 295.55667, "r_y1": 386.98218, "r_x2": 295.55667, "r_y2": 378.60754, "r_x3": 53.484001, "r_y3": 378.60754, "coord_origin": "TOPLEFT" }, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 397.94116, "r_x1": 294.04865, "r_y1": 397.94116, "r_x2": 294.04865, "r_y2": 389.56653, "r_x3": 53.79800000000001, "r_y3": 389.56653, "coord_origin": "TOPLEFT" }, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 408.90018, "r_x1": 294.04376, "r_y1": 408.90018, "r_x2": 294.04376, "r_y2": 400.52554000000003, "r_x3": 53.79800000000001, "r_y3": 400.52554000000003, "coord_origin": "TOPLEFT" }, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 419.85919, "r_x1": 295.42459, "r_y1": 419.85919, "r_x2": 295.42459, "r_y2": 411.48456, "r_x3": 53.79800000000001, "r_y3": 411.48456, "coord_origin": "TOPLEFT" }, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 430.81818, "r_x1": 294.04709, "r_y1": 430.81818, "r_x2": 294.04709, "r_y2": 422.44354, "r_x3": 53.79800000000001, "r_y3": 422.44354, "coord_origin": "TOPLEFT" }, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 441.7771599999999, "r_x1": 294.04535, "r_y1": 441.7771599999999, "r_x2": 294.04535, "r_y2": 433.40253000000007, "r_x3": 53.466999, "r_y3": 433.40253000000007, "coord_origin": "TOPLEFT" }, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 452.73517, "r_x1": 295.42902, "r_y1": 452.73517, "r_x2": 295.42902, "r_y2": 444.36053000000004, "r_x3": 53.79800000000001, "r_y3": 444.36053000000004, "coord_origin": "TOPLEFT" }, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 463.69418, "r_x1": 295.03122, "r_y1": 463.69418, "r_x2": 295.03122, "r_y2": 455.31955, "r_x3": 53.528999, "r_y3": 455.31955, "coord_origin": "TOPLEFT" }, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 474.65317, "r_x1": 295.42154, "r_y1": 474.65317, "r_x2": 295.42154, "r_y2": 466.27853, "r_x3": 53.466999, "r_y3": 466.27853, "coord_origin": "TOPLEFT" }, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 485.61218, "r_x1": 294.04553, "r_y1": 485.61218, "r_x2": 294.04553, "r_y2": 477.23755, "r_x3": 53.528999, "r_y3": 477.23755, "coord_origin": "TOPLEFT" }, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 496.57117, "r_x1": 295.55646, "r_y1": 496.57117, "r_x2": 295.55646, "r_y2": 488.19653, "r_x3": 53.79800000000001, "r_y3": 488.19653, "coord_origin": "TOPLEFT" }, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 507.53018, "r_x1": 294.04715, "r_y1": 507.53018, "r_x2": 294.04715, "r_y2": 499.15555, "r_x3": 53.79800000000001, "r_y3": 499.15555, "coord_origin": "TOPLEFT" }, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 518.4891700000001, "r_x1": 294.04245, "r_y1": 518.4891700000001, "r_x2": 294.04245, "r_y2": 510.11453, "r_x3": 53.79800000000001, "r_y3": 510.11453, "coord_origin": "TOPLEFT" }, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 529.44818, "r_x1": 191.47707, "r_y1": 529.44818, "r_x2": 191.47707, "r_y2": 521.0735500000001, "r_x3": 53.79800000000001, "r_y3": 521.0735500000001, "coord_origin": "TOPLEFT" }, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT" }, "confidence": 0.9572026133537292, "cells": [ { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.81902, "r_x1": 164.32898, "r_y1": 552.81902, "r_x2": 164.32898, "r_y2": 542.50992, "r_x3": 53.79800000000001, "r_y3": 542.50992, "coord_origin": "TOPLEFT" }, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT" }, "confidence": 0.9876301884651184, "cells": [ { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 566.08617, "r_x1": 294.04333, "r_y1": 566.08617, "r_x2": 294.04333, "r_y2": 557.71155, "r_x3": 53.528999, "r_y3": 557.71155, "coord_origin": "TOPLEFT" }, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 577.04517, "r_x1": 294.04712, "r_y1": 577.04517, "r_x2": 294.04712, "r_y2": 568.6705499999999, "r_x3": 53.79800000000001, "r_y3": 568.6705499999999, "coord_origin": "TOPLEFT" }, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 588.0041699999999, "r_x1": 294.04538, "r_y1": 588.0041699999999, "r_x2": 294.04538, "r_y2": 579.62955, "r_x3": 53.79800000000001, "r_y3": 579.62955, "coord_origin": "TOPLEFT" }, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.96317, "r_x1": 294.04535, "r_y1": 598.96317, "r_x2": 294.04535, "r_y2": 590.5885499999999, "r_x3": 53.79800000000001, "r_y3": 590.5885499999999, "coord_origin": "TOPLEFT" }, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 609.92216, "r_x1": 294.04266, "r_y1": 609.92216, "r_x2": 294.04266, "r_y2": 601.54755, "r_x3": 53.79800000000001, "r_y3": 601.54755, "coord_origin": "TOPLEFT" }, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 620.88118, "r_x1": 163.59247, "r_y1": 620.88118, "r_x2": 163.59247, "r_y2": 612.50656, "r_x3": 53.79800000000001, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 166.26401, "r_y0": 620.89015, "r_x1": 194.97244, "r_y1": 620.89015, "r_x2": 194.97244, "r_y2": 612.55139, "r_x3": 166.26401, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 194.994, "r_y0": 620.15491, "r_x1": 204.1756, "r_y1": 620.15491, "r_x2": 204.1756, "r_y2": 612.45276, "r_x3": 194.994, "r_y3": 612.45276, "coord_origin": "TOPLEFT" }, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.17599, "r_y0": 620.89015, "r_x1": 219.33961000000002, "r_y1": 620.89015, "r_x2": 219.33961000000002, "r_y2": 612.55139, "r_x3": 204.17599, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.849, "r_y0": 620.88118, "r_x1": 294.04828, "r_y1": 620.88118, "r_x2": 294.04828, "r_y2": 612.50656, "r_x3": 219.849, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 631.84018, "r_x1": 294.04709, "r_y1": 631.84018, "r_x2": 294.04709, "r_y2": 623.46556, "r_x3": 53.79800000000001, "r_y3": 623.46556, "coord_origin": "TOPLEFT" }, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 642.79817, "r_x1": 295.55679, "r_y1": 642.79817, "r_x2": 295.55679, "r_y2": 634.42355, "r_x3": 53.79800000000001, "r_y3": 634.42355, "coord_origin": "TOPLEFT" }, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.75717, "r_x1": 294.04715, "r_y1": 653.75717, "r_x2": 294.04715, "r_y2": 645.38255, "r_x3": 53.79800000000001, "r_y3": 645.38255, "coord_origin": "TOPLEFT" }, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.71617, "r_x1": 294.04715, "r_y1": 664.71617, "r_x2": 294.04715, "r_y2": 656.34155, "r_x3": 53.79800000000001, "r_y3": 656.34155, "coord_origin": "TOPLEFT" }, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 675.67517, "r_x1": 294.04712, "r_y1": 675.67517, "r_x2": 294.04712, "r_y2": 667.30055, "r_x3": 53.79800000000001, "r_y3": 667.30055, "coord_origin": "TOPLEFT" }, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 686.63417, "r_x1": 294.25174, "r_y1": 686.63417, "r_x2": 294.25174, "r_y2": 678.25955, "r_x3": 53.466999, "r_y3": 678.25955, "coord_origin": "TOPLEFT" }, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.59317, "r_x1": 294.04639, "r_y1": 697.59317, "r_x2": 294.04639, "r_y2": 689.21855, "r_x3": 53.79800000000001, "r_y3": 689.21855, "coord_origin": "TOPLEFT" }, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.55217, "r_x1": 294.04712, "r_y1": 708.55217, "r_x2": 294.04712, "r_y2": 700.177551, "r_x3": 53.79800000000001, "r_y3": 700.177551, "coord_origin": "TOPLEFT" }, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 13, "label": "text", "bbox": { "l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT" }, "confidence": 0.8736215829849243, "cells": [ { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.659, "r_y0": 95.34625000000017, "r_x1": 558.20068, "r_y1": 95.34625000000017, "r_x2": 558.20068, "r_y2": 86.87298999999996, "r_x3": 317.659, "r_y3": 86.87298999999996, "coord_origin": "TOPLEFT" }, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.30524000000003, "r_x1": 559.73401, "r_y1": 106.30524000000003, "r_x2": 559.73401, "r_y2": 97.83196999999996, "r_x3": 317.95499, "r_y3": 97.83196999999996, "coord_origin": "TOPLEFT" }, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.26422000000014, "r_x1": 467.72089, "r_y1": 117.26422000000014, "r_x2": 467.72089, "r_y2": 108.79094999999995, "r_x3": 317.95499, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "Naive page-wise split will result in", "orig": "Naive page-wise split will result in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 471.90900000000005, "r_y0": 115.19768999999997, "r_x1": 477.53900000000004, "r_y1": 115.19768999999997, "r_x2": 477.53900000000004, "r_y2": 107.12798999999995, "r_x3": 471.90900000000005, "r_y3": 107.12798999999995, "coord_origin": "TOPLEFT" }, "text": "GLYPH", "orig": "GLYPH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.54001000000005, "r_y0": 117.26422000000014, "r_x1": 559.80682, "r_y1": 117.26422000000014, "r_x2": 559.80682, "r_y2": 108.79094999999995, "r_x3": 477.54001000000005, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 128.22321, "r_x1": 341.37524, "r_y1": 128.22321, "r_x2": 341.37524, "r_y2": 119.74993999999992, "r_x3": 317.95502, "r_y3": 119.74993999999992, "coord_origin": "TOPLEFT" }, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 0, "label": "table", "bbox": { "l": 352.97747802734375, "t": 150.791259765625, "r": 522.9158935546875, "b": 306.2658386230469, "coord_origin": "TOPLEFT" }, "confidence": 0.9879696369171143, "cells": [ { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.63901, "r_y0": 161.47515999999996, "r_x1": 401.73154, "r_y1": 161.47515999999996, "r_x2": 401.73154, "r_y2": 153.10051999999996, "r_x3": 358.63901, "r_y3": 153.10051999999996, "coord_origin": "TOPLEFT" }, "text": "Class-count", "orig": "Class-count", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 440.22501, "r_y0": 161.47515999999996, "r_x1": 448.56375, "r_y1": 161.47515999999996, "r_x2": 448.56375, "r_y2": 153.10051999999996, "r_x3": 440.22501, "r_y3": 153.10051999999996, "coord_origin": "TOPLEFT" }, "text": "11", "orig": "11", 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331.40353, "coord_origin": "TOPLEFT" }, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 350.73718, "r_x1": 558.2005, "r_y1": 350.73718, "r_x2": 558.2005, "r_y2": 342.36255, "r_x3": 317.95499, "r_y3": 342.36255, "coord_origin": "TOPLEFT" }, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 361.69617000000005, "r_x1": 393.16028, "r_y1": 361.69617000000005, "r_x2": 393.16028, "r_y2": 353.32153, "r_x3": 317.95499, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 395.84201, "r_y0": 361.70514, "r_x1": 409.14905, "r_y1": 361.70514, "r_x2": 409.14905, "r_y2": 353.36639, "r_x3": 395.84201, "r_y3": 353.36639, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 412.33301, "r_y0": 361.69617000000005, "r_x1": 485.02324999999996, "r_y1": 361.69617000000005, "r_x2": 485.02324999999996, "r_y2": 353.32153, "r_x3": 412.33301, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, 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in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 383.61316, "r_x1": 558.36877, "r_y1": 383.61316, "r_x2": 558.36877, "r_y2": 375.23853, "r_x3": 317.95499, "r_y3": 375.23853, "coord_origin": "TOPLEFT" }, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 394.57217, "r_x1": 558.19958, "r_y1": 394.57217, "r_x2": 558.19958, "r_y2": 386.19754, "r_x3": 317.95499, "r_y3": 386.19754, "coord_origin": "TOPLEFT" }, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 405.53116000000006, "r_x1": 439.49454, "r_y1": 405.53116000000006, "r_x2": 439.49454, "r_y2": 397.15652, "r_x3": 317.95499, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 441.728, "r_y0": 405.54013, "r_x1": 481.8616, "r_y1": 405.54013, "r_x2": 481.8616, "r_y2": 397.20139, "r_x3": 441.728, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.74298, "r_y0": 405.53116000000006, "r_x1": 498.23743, "r_y1": 405.53116000000006, "r_x2": 498.23743, "r_y2": 397.15652, "r_x3": 484.74298, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.47299, "r_y0": 405.54013, "r_x1": 543.95105, "r_y1": 405.54013, "r_x2": 543.95105, "r_y2": 397.20139, "r_x3": 500.47299, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 546.83197, "r_y0": 405.53116000000006, "r_x1": 558.20142, "r_y1": 405.53116000000006, "r_x2": 558.20142, "r_y2": 397.15652, "r_x3": 546.83197, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 416.49017, "r_x1": 352.37698, "r_y1": 416.49017, "r_x2": 352.37698, "r_y2": 408.11553999999995, "r_x3": 317.95496, "r_y3": 408.11553999999995, "coord_origin": "TOPLEFT" }, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 9, "label": "section_header", "bbox": { "l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT" }, "confidence": 0.9562516808509827, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 439.70398, "r_x1": 549.8606, "r_y1": 439.70398, "r_x2": 549.8606, "r_y2": 429.3949, "r_x3": 317.95496, "r_y3": 429.3949, "coord_origin": "TOPLEFT" }, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT" }, "confidence": 0.9869834184646606, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 452.97116, "r_x1": 558.36884, "r_y1": 452.97116, "r_x2": 558.36884, "r_y2": 444.59653, "r_x3": 317.95499, "r_y3": 444.59653, "coord_origin": "TOPLEFT" }, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 463.93018, "r_x1": 559.19189, "r_y1": 463.93018, "r_x2": 559.19189, "r_y2": 455.55554, "r_x3": 317.95499, "r_y3": 455.55554, "coord_origin": "TOPLEFT" }, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 474.88916, "r_x1": 559.58185, "r_y1": 474.88916, "r_x2": 559.58185, "r_y2": 466.51453, "r_x3": 317.95499, "r_y3": 466.51453, "coord_origin": "TOPLEFT" }, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 485.84818, "r_x1": 558.20605, "r_y1": 485.84818, "r_x2": 558.20605, "r_y2": 477.47354, "r_x3": 317.95499, "r_y3": 477.47354, "coord_origin": "TOPLEFT" }, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 496.80716, "r_x1": 559.58203, "r_y1": 496.80716, "r_x2": 559.58203, "r_y2": 488.43253, "r_x3": 317.95499, "r_y3": 488.43253, "coord_origin": "TOPLEFT" }, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 507.76617, "r_x1": 558.20032, "r_y1": 507.76617, "r_x2": 558.20032, "r_y2": 499.39154, "r_x3": 317.686, "r_y3": 499.39154, "coord_origin": "TOPLEFT" }, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 518.72516, "r_x1": 559.5849, "r_y1": 518.72516, "r_x2": 559.5849, "r_y2": 510.35052, "r_x3": 317.95499, "r_y3": 510.35052, "coord_origin": "TOPLEFT" }, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 529.68417, "r_x1": 559.71381, "r_y1": 529.68417, "r_x2": 559.71381, "r_y2": 521.30954, "r_x3": 317.95499, "r_y3": 521.30954, "coord_origin": "TOPLEFT" }, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 540.64317, "r_x1": 559.18707, "r_y1": 540.64317, "r_x2": 559.18707, "r_y2": 532.26855, "r_x3": 317.95499, "r_y3": 532.26855, "coord_origin": "TOPLEFT" }, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.60117, "r_x1": 559.71313, "r_y1": 551.60117, "r_x2": 559.71313, "r_y2": 543.22655, "r_x3": 317.95499, "r_y3": 543.22655, "coord_origin": "TOPLEFT" }, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 562.56017, "r_x1": 388.35168, "r_y1": 562.56017, "r_x2": 388.35168, "r_y2": 554.18555, "r_x3": 317.62299, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "wise splitting gains", "orig": "wise splitting gains", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 391.36499, "r_y0": 560.87517, "r_x1": 393.98318, "r_y1": 560.87517, "r_x2": 393.98318, "r_y2": 552.50055, "r_x3": 391.36499, "r_y3": 552.50055, "coord_origin": "TOPLEFT" }, "text": "\u02dc", "orig": "\u02dc", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 200, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 390.59, "r_y0": 562.56017, "r_x1": 559.58191, "r_y1": 562.56017, "r_x2": 559.58191, "r_y2": 554.18555, "r_x3": 390.59, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "10% in mAP over the document-wise splitting.", "orig": "10% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 201, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 573.51917, "r_x1": 558.20032, "r_y1": 573.51917, "r_x2": 558.20032, "r_y2": 565.14455, "r_x3": 317.686, "r_y3": 565.14455, "coord_origin": "TOPLEFT" }, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 202, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 584.4781800000001, "r_x1": 558.20508, "r_y1": 584.4781800000001, "r_x2": 558.20508, "r_y2": 576.10356, "r_x3": 317.95499, "r_y3": 576.10356, "coord_origin": "TOPLEFT" }, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 203, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 595.43718, "r_x1": 348.50354, "r_y1": 595.43718, "r_x2": 348.50354, "r_y2": 587.06256, "r_x3": 317.95499, "r_y3": 587.06256, "coord_origin": "TOPLEFT" }, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 7, "label": "section_header", "bbox": { "l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT" }, "confidence": 0.9589217305183411, "cells": [ { "index": 204, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 618.65102, "r_x1": 418.54776, "r_y1": 618.65102, "r_x2": 418.54776, "r_y2": 608.34192, "r_x3": 317.95499, "r_y3": 608.34192, "coord_origin": "TOPLEFT" }, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT" }, "confidence": 0.9872549772262573, "cells": [ { "index": 205, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 631.91817, "r_x1": 558.20575, "r_y1": 631.91817, "r_x2": 558.20575, "r_y2": 623.54355, "r_x3": 317.686, "r_y3": 623.54355, "coord_origin": "TOPLEFT" }, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "orig": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 206, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.87717, "r_x1": 558.20624, "r_y1": 642.87717, "r_x2": 558.20624, "r_y2": 634.50255, "r_x3": 317.95499, "r_y3": 634.50255, "coord_origin": "TOPLEFT" }, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 207, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 653.83617, "r_x1": 558.20319, "r_y1": 653.83617, "r_x2": 558.20319, "r_y2": 645.46155, "r_x3": 317.686, "r_y3": 645.46155, "coord_origin": "TOPLEFT" }, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 208, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 664.79517, "r_x1": 558.74377, "r_y1": 664.79517, "r_x2": 558.74377, "r_y2": 656.42055, "r_x3": 317.95499, "r_y3": 656.42055, "coord_origin": "TOPLEFT" }, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 209, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.7541699999999, "r_x1": 558.20398, "r_y1": 675.7541699999999, "r_x2": 558.20398, "r_y2": 667.37955, "r_x3": 317.95499, "r_y3": 667.37955, "coord_origin": "TOPLEFT" }, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 210, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 686.71317, "r_x1": 558.20062, "r_y1": 686.71317, "r_x2": 558.20062, "r_y2": 678.3385499999999, "r_x3": 317.95499, "r_y3": 678.3385499999999, "coord_origin": "TOPLEFT" }, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 211, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.672173, "r_x1": 558.20343, "r_y1": 697.672173, "r_x2": 558.20343, "r_y2": 689.29755, "r_x3": 317.95499, "r_y3": 689.29755, "coord_origin": "TOPLEFT" }, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 212, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.631172, "r_x1": 531.07666, "r_y1": 708.631172, "r_x2": 531.07666, "r_y2": 700.256554, "r_x3": 317.95499, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. 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{ "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 329.879, "r_x1": 131.05624, "r_y1": 329.879, "r_x2": 131.05624, "r_y2": 319.56992, "r_x3": 53.79800000000001, "r_y3": 319.56992, "coord_origin": "TOPLEFT" }, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Learning Curve" }, { "label": "text", "id": 5, "page_no": 6, "cluster": { "id": 5, "label": "text", "bbox": { "l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT" }, "confidence": 0.9857739210128784, "cells": [ { "index": 72, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 343.14618, "r_x1": 294.04153, "r_y1": 343.14618, "r_x2": 294.04153, "r_y2": 334.77155, "r_x3": 53.79800000000001, "r_y3": 334.77155, "coord_origin": "TOPLEFT" }, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 52.785, "r_y0": 354.10516000000007, "r_x1": 295.55835, "r_y1": 354.10516000000007, "r_x2": 295.55835, "r_y2": 345.73053, "r_x3": 52.785, "r_y3": 345.73053, "coord_origin": "TOPLEFT" }, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "orig": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 365.06418, "r_x1": 294.04535, "r_y1": 365.06418, "r_x2": 294.04535, "r_y2": 356.6895400000001, "r_x3": 53.79800000000001, "r_y3": 356.6895400000001, "coord_origin": "TOPLEFT" }, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 376.02316, "r_x1": 295.4281, "r_y1": 376.02316, "r_x2": 295.4281, "r_y2": 367.64853, "r_x3": 53.79800000000001, "r_y3": 367.64853, "coord_origin": "TOPLEFT" }, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 386.98218, "r_x1": 295.55667, "r_y1": 386.98218, "r_x2": 295.55667, "r_y2": 378.60754, "r_x3": 53.484001, "r_y3": 378.60754, "coord_origin": "TOPLEFT" }, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 397.94116, "r_x1": 294.04865, "r_y1": 397.94116, "r_x2": 294.04865, "r_y2": 389.56653, "r_x3": 53.79800000000001, "r_y3": 389.56653, "coord_origin": "TOPLEFT" }, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 408.90018, "r_x1": 294.04376, "r_y1": 408.90018, "r_x2": 294.04376, "r_y2": 400.52554000000003, "r_x3": 53.79800000000001, "r_y3": 400.52554000000003, "coord_origin": "TOPLEFT" }, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 419.85919, "r_x1": 295.42459, "r_y1": 419.85919, "r_x2": 295.42459, "r_y2": 411.48456, "r_x3": 53.79800000000001, "r_y3": 411.48456, "coord_origin": "TOPLEFT" }, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 430.81818, "r_x1": 294.04709, "r_y1": 430.81818, "r_x2": 294.04709, "r_y2": 422.44354, "r_x3": 53.79800000000001, "r_y3": 422.44354, "coord_origin": "TOPLEFT" }, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 441.7771599999999, "r_x1": 294.04535, "r_y1": 441.7771599999999, "r_x2": 294.04535, "r_y2": 433.40253000000007, "r_x3": 53.466999, "r_y3": 433.40253000000007, "coord_origin": "TOPLEFT" }, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 452.73517, "r_x1": 295.42902, "r_y1": 452.73517, "r_x2": 295.42902, "r_y2": 444.36053000000004, "r_x3": 53.79800000000001, "r_y3": 444.36053000000004, "coord_origin": "TOPLEFT" }, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 463.69418, "r_x1": 295.03122, "r_y1": 463.69418, "r_x2": 295.03122, "r_y2": 455.31955, "r_x3": 53.528999, "r_y3": 455.31955, "coord_origin": "TOPLEFT" }, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 474.65317, "r_x1": 295.42154, "r_y1": 474.65317, "r_x2": 295.42154, "r_y2": 466.27853, "r_x3": 53.466999, "r_y3": 466.27853, "coord_origin": "TOPLEFT" }, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 485.61218, "r_x1": 294.04553, "r_y1": 485.61218, "r_x2": 294.04553, "r_y2": 477.23755, "r_x3": 53.528999, "r_y3": 477.23755, "coord_origin": "TOPLEFT" }, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 496.57117, "r_x1": 295.55646, "r_y1": 496.57117, "r_x2": 295.55646, "r_y2": 488.19653, "r_x3": 53.79800000000001, "r_y3": 488.19653, "coord_origin": "TOPLEFT" }, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 507.53018, "r_x1": 294.04715, "r_y1": 507.53018, "r_x2": 294.04715, "r_y2": 499.15555, "r_x3": 53.79800000000001, "r_y3": 499.15555, "coord_origin": "TOPLEFT" }, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 518.4891700000001, "r_x1": 294.04245, "r_y1": 518.4891700000001, "r_x2": 294.04245, "r_y2": 510.11453, "r_x3": 53.79800000000001, "r_y3": 510.11453, "coord_origin": "TOPLEFT" }, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 529.44818, "r_x1": 191.47707, "r_y1": 529.44818, "r_x2": 191.47707, "r_y2": 521.0735500000001, "r_x3": 53.79800000000001, "r_y3": 521.0735500000001, "coord_origin": "TOPLEFT" }, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles." }, { "label": "section_header", "id": 8, "page_no": 6, "cluster": { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT" }, "confidence": 0.9572026133537292, "cells": [ { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.81902, "r_x1": 164.32898, "r_y1": 552.81902, "r_x2": 164.32898, "r_y2": 542.50992, "r_x3": 53.79800000000001, "r_y3": 542.50992, "coord_origin": "TOPLEFT" }, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Impact of Class Labels" }, { "label": "text", "id": 1, "page_no": 6, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT" }, "confidence": 0.9876301884651184, "cells": [ { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 566.08617, "r_x1": 294.04333, "r_y1": 566.08617, "r_x2": 294.04333, "r_y2": 557.71155, "r_x3": 53.528999, "r_y3": 557.71155, "coord_origin": "TOPLEFT" }, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 577.04517, "r_x1": 294.04712, "r_y1": 577.04517, "r_x2": 294.04712, "r_y2": 568.6705499999999, "r_x3": 53.79800000000001, "r_y3": 568.6705499999999, "coord_origin": "TOPLEFT" }, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 588.0041699999999, "r_x1": 294.04538, "r_y1": 588.0041699999999, "r_x2": 294.04538, "r_y2": 579.62955, "r_x3": 53.79800000000001, "r_y3": 579.62955, "coord_origin": "TOPLEFT" }, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.96317, "r_x1": 294.04535, "r_y1": 598.96317, "r_x2": 294.04535, "r_y2": 590.5885499999999, "r_x3": 53.79800000000001, "r_y3": 590.5885499999999, "coord_origin": "TOPLEFT" }, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 609.92216, "r_x1": 294.04266, "r_y1": 609.92216, "r_x2": 294.04266, "r_y2": 601.54755, "r_x3": 53.79800000000001, "r_y3": 601.54755, "coord_origin": "TOPLEFT" }, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 620.88118, "r_x1": 163.59247, "r_y1": 620.88118, "r_x2": 163.59247, "r_y2": 612.50656, "r_x3": 53.79800000000001, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 166.26401, "r_y0": 620.89015, "r_x1": 194.97244, "r_y1": 620.89015, "r_x2": 194.97244, "r_y2": 612.55139, "r_x3": 166.26401, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 194.994, "r_y0": 620.15491, "r_x1": 204.1756, "r_y1": 620.15491, "r_x2": 204.1756, "r_y2": 612.45276, "r_x3": 194.994, "r_y3": 612.45276, "coord_origin": "TOPLEFT" }, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.17599, "r_y0": 620.89015, "r_x1": 219.33961000000002, "r_y1": 620.89015, "r_x2": 219.33961000000002, "r_y2": 612.55139, "r_x3": 204.17599, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.849, "r_y0": 620.88118, "r_x1": 294.04828, "r_y1": 620.88118, "r_x2": 294.04828, "r_y2": 612.50656, "r_x3": 219.849, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 631.84018, "r_x1": 294.04709, "r_y1": 631.84018, "r_x2": 294.04709, "r_y2": 623.46556, "r_x3": 53.79800000000001, "r_y3": 623.46556, "coord_origin": "TOPLEFT" }, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 642.79817, "r_x1": 295.55679, "r_y1": 642.79817, "r_x2": 295.55679, "r_y2": 634.42355, "r_x3": 53.79800000000001, "r_y3": 634.42355, "coord_origin": "TOPLEFT" }, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.75717, "r_x1": 294.04715, "r_y1": 653.75717, "r_x2": 294.04715, "r_y2": 645.38255, "r_x3": 53.79800000000001, "r_y3": 645.38255, "coord_origin": "TOPLEFT" }, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.71617, "r_x1": 294.04715, "r_y1": 664.71617, "r_x2": 294.04715, "r_y2": 656.34155, "r_x3": 53.79800000000001, "r_y3": 656.34155, "coord_origin": "TOPLEFT" }, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 675.67517, "r_x1": 294.04712, "r_y1": 675.67517, "r_x2": 294.04712, "r_y2": 667.30055, "r_x3": 53.79800000000001, "r_y3": 667.30055, "coord_origin": "TOPLEFT" }, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 686.63417, "r_x1": 294.25174, "r_y1": 686.63417, "r_x2": 294.25174, "r_y2": 678.25955, "r_x3": 53.466999, "r_y3": 678.25955, "coord_origin": "TOPLEFT" }, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.59317, "r_x1": 294.04639, "r_y1": 697.59317, "r_x2": 294.04639, "r_y2": 689.21855, "r_x3": 53.79800000000001, "r_y3": 689.21855, "coord_origin": "TOPLEFT" }, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.55217, "r_x1": 294.04712, "r_y1": 708.55217, "r_x2": 294.04712, "r_y2": 700.177551, "r_x3": 53.79800000000001, "r_y3": 700.177551, "coord_origin": "TOPLEFT" }, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of" }, { "label": "text", "id": 13, "page_no": 6, "cluster": { "id": 13, "label": "text", "bbox": { "l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT" }, "confidence": 0.8736215829849243, "cells": [ { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.659, "r_y0": 95.34625000000017, "r_x1": 558.20068, "r_y1": 95.34625000000017, "r_x2": 558.20068, "r_y2": 86.87298999999996, "r_x3": 317.659, "r_y3": 86.87298999999996, "coord_origin": "TOPLEFT" }, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.30524000000003, "r_x1": 559.73401, "r_y1": 106.30524000000003, "r_x2": 559.73401, "r_y2": 97.83196999999996, "r_x3": 317.95499, "r_y3": 97.83196999999996, "coord_origin": "TOPLEFT" }, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.26422000000014, "r_x1": 467.72089, "r_y1": 117.26422000000014, "r_x2": 467.72089, "r_y2": 108.79094999999995, "r_x3": 317.95499, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "Naive page-wise split will result in", "orig": "Naive page-wise split will result in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 471.90900000000005, "r_y0": 115.19768999999997, "r_x1": 477.53900000000004, "r_y1": 115.19768999999997, "r_x2": 477.53900000000004, "r_y2": 107.12798999999995, "r_x3": 471.90900000000005, "r_y3": 107.12798999999995, "coord_origin": "TOPLEFT" }, "text": "GLYPH", "orig": "GLYPH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.54001000000005, "r_y0": 117.26422000000014, "r_x1": 559.80682, "r_y1": 117.26422000000014, "r_x2": 559.80682, "r_y2": 108.79094999999995, "r_x3": 477.54001000000005, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 128.22321, "r_x1": 341.37524, "r_y1": 128.22321, "r_x2": 341.37524, "r_y2": 119.74993999999992, "r_x3": 317.95502, "r_y3": 119.74993999999992, "coord_origin": "TOPLEFT" }, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH 10% point improvement." }, { "label": "table", "id": 0, "page_no": 6, "cluster": { "id": 0, "label": "table", "bbox": { "l": 352.97747802734375, "t": 150.791259765625, "r": 522.9158935546875, "b": 306.2658386230469, "coord_origin": "TOPLEFT" }, "confidence": 0.9879696369171143, "cells": [ { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 358.63901, "r_y0": 161.47515999999996, "r_x1": 401.73154, "r_y1": 161.47515999999996, "r_x2": 401.73154, "r_y2": 153.10051999999996, "r_x3": 358.63901, "r_y3": 153.10051999999996, "coord_origin": "TOPLEFT" }, "text": "Class-count", "orig": "Class-count", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 440.22501, "r_y0": 161.47515999999996, "r_x1": 448.56375, "r_y1": 161.47515999999996, "r_x2": 448.56375, "r_y2": 153.10051999999996, "r_x3": 440.22501, "r_y3": 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"r_x3": 317.95499, "r_y3": 331.40353, "coord_origin": "TOPLEFT" }, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 172, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 350.73718, "r_x1": 558.2005, "r_y1": 350.73718, "r_x2": 558.2005, "r_y2": 342.36255, "r_x3": 317.95499, "r_y3": 342.36255, "coord_origin": "TOPLEFT" }, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 173, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 361.69617000000005, "r_x1": 393.16028, "r_y1": 361.69617000000005, "r_x2": 393.16028, "r_y2": 353.32153, "r_x3": 317.95499, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 174, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 395.84201, "r_y0": 361.70514, "r_x1": 409.14905, "r_y1": 361.70514, "r_x2": 409.14905, "r_y2": 353.36639, "r_x3": 395.84201, "r_y3": 353.36639, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 175, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 412.33301, "r_y0": 361.69617000000005, "r_x1": 485.02324999999996, "r_y1": 361.69617000000005, "r_x2": 485.02324999999996, "r_y2": 353.32153, "r_x3": 412.33301, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.70401, "r_y0": 361.70514, "r_x1": 502.86761, "r_y1": 361.70514, "r_x2": 502.86761, "r_y2": 353.36639, "r_x3": 487.70401, "r_y3": 353.36639, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 506.05499, "r_y0": 361.69617000000005, "r_x1": 559.58496, "r_y1": 361.69617000000005, "r_x2": 559.58496, "r_y2": 353.32153, "r_x3": 506.05499, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 372.65518, "r_x1": 558.2002, "r_y1": 372.65518, "r_x2": 558.2002, "r_y2": 364.28054999999995, "r_x3": 317.686, "r_y3": 364.28054999999995, "coord_origin": "TOPLEFT" }, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 383.61316, "r_x1": 558.36877, "r_y1": 383.61316, "r_x2": 558.36877, "r_y2": 375.23853, "r_x3": 317.95499, "r_y3": 375.23853, "coord_origin": "TOPLEFT" }, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 394.57217, "r_x1": 558.19958, "r_y1": 394.57217, "r_x2": 558.19958, "r_y2": 386.19754, "r_x3": 317.95499, "r_y3": 386.19754, "coord_origin": "TOPLEFT" }, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 405.53116000000006, "r_x1": 439.49454, "r_y1": 405.53116000000006, "r_x2": 439.49454, "r_y2": 397.15652, "r_x3": 317.95499, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 441.728, "r_y0": 405.54013, "r_x1": 481.8616, "r_y1": 405.54013, "r_x2": 481.8616, "r_y2": 397.20139, "r_x3": 441.728, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.74298, "r_y0": 405.53116000000006, "r_x1": 498.23743, "r_y1": 405.53116000000006, "r_x2": 498.23743, "r_y2": 397.15652, "r_x3": 484.74298, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.47299, "r_y0": 405.54013, "r_x1": 543.95105, "r_y1": 405.54013, "r_x2": 543.95105, "r_y2": 397.20139, "r_x3": 500.47299, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 546.83197, "r_y0": 405.53116000000006, "r_x1": 558.20142, "r_y1": 405.53116000000006, "r_x2": 558.20142, "r_y2": 397.15652, "r_x3": 546.83197, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 416.49017, "r_x1": 352.37698, "r_y1": 416.49017, "r_x2": 352.37698, "r_y2": 408.11553999999995, "r_x3": 317.95496, "r_y3": 408.11553999999995, "coord_origin": "TOPLEFT" }, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded." }, { "label": "section_header", "id": 9, "page_no": 6, "cluster": { "id": 9, "label": "section_header", "bbox": { "l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT" }, "confidence": 0.9562516808509827, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 439.70398, "r_x1": 549.8606, "r_y1": 439.70398, "r_x2": 549.8606, "r_y2": 429.3949, "r_x3": 317.95496, "r_y3": 429.3949, "coord_origin": "TOPLEFT" }, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Impact of Document Split in Train and Test Set" }, { "label": "text", "id": 3, "page_no": 6, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT" }, "confidence": 0.9869834184646606, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 452.97116, "r_x1": 558.36884, "r_y1": 452.97116, "r_x2": 558.36884, "r_y2": 444.59653, "r_x3": 317.95499, "r_y3": 444.59653, "coord_origin": "TOPLEFT" }, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 463.93018, "r_x1": 559.19189, "r_y1": 463.93018, "r_x2": 559.19189, "r_y2": 455.55554, "r_x3": 317.95499, "r_y3": 455.55554, "coord_origin": "TOPLEFT" }, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 474.88916, "r_x1": 559.58185, "r_y1": 474.88916, "r_x2": 559.58185, "r_y2": 466.51453, "r_x3": 317.95499, "r_y3": 466.51453, "coord_origin": "TOPLEFT" }, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 485.84818, "r_x1": 558.20605, "r_y1": 485.84818, "r_x2": 558.20605, "r_y2": 477.47354, "r_x3": 317.95499, "r_y3": 477.47354, "coord_origin": "TOPLEFT" }, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 496.80716, "r_x1": 559.58203, "r_y1": 496.80716, "r_x2": 559.58203, "r_y2": 488.43253, "r_x3": 317.95499, "r_y3": 488.43253, "coord_origin": "TOPLEFT" }, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 507.76617, "r_x1": 558.20032, "r_y1": 507.76617, "r_x2": 558.20032, "r_y2": 499.39154, "r_x3": 317.686, "r_y3": 499.39154, "coord_origin": "TOPLEFT" }, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 518.72516, "r_x1": 559.5849, "r_y1": 518.72516, "r_x2": 559.5849, "r_y2": 510.35052, "r_x3": 317.95499, "r_y3": 510.35052, "coord_origin": "TOPLEFT" }, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 529.68417, "r_x1": 559.71381, "r_y1": 529.68417, "r_x2": 559.71381, "r_y2": 521.30954, "r_x3": 317.95499, "r_y3": 521.30954, "coord_origin": "TOPLEFT" }, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 540.64317, "r_x1": 559.18707, "r_y1": 540.64317, "r_x2": 559.18707, "r_y2": 532.26855, "r_x3": 317.95499, "r_y3": 532.26855, "coord_origin": "TOPLEFT" }, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.60117, "r_x1": 559.71313, "r_y1": 551.60117, "r_x2": 559.71313, "r_y2": 543.22655, "r_x3": 317.95499, "r_y3": 543.22655, "coord_origin": "TOPLEFT" }, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 562.56017, "r_x1": 388.35168, "r_y1": 562.56017, "r_x2": 388.35168, "r_y2": 554.18555, "r_x3": 317.62299, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "wise splitting gains", "orig": "wise splitting gains", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 391.36499, "r_y0": 560.87517, "r_x1": 393.98318, "r_y1": 560.87517, "r_x2": 393.98318, "r_y2": 552.50055, "r_x3": 391.36499, "r_y3": 552.50055, "coord_origin": "TOPLEFT" }, "text": "\u02dc", "orig": "\u02dc", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 200, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 390.59, "r_y0": 562.56017, "r_x1": 559.58191, "r_y1": 562.56017, "r_x2": 559.58191, "r_y2": 554.18555, "r_x3": 390.59, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "10% in mAP over the document-wise splitting.", "orig": "10% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 201, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 573.51917, "r_x1": 558.20032, "r_y1": 573.51917, "r_x2": 558.20032, "r_y2": 565.14455, "r_x3": 317.686, "r_y3": 565.14455, "coord_origin": "TOPLEFT" }, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 202, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 584.4781800000001, "r_x1": 558.20508, "r_y1": 584.4781800000001, "r_x2": 558.20508, "r_y2": 576.10356, "r_x3": 317.95499, "r_y3": 576.10356, "coord_origin": "TOPLEFT" }, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 203, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 595.43718, "r_x1": 348.50354, "r_y1": 595.43718, "r_x2": 348.50354, "r_y2": 587.06256, "r_x3": 317.95499, "r_y3": 587.06256, "coord_origin": "TOPLEFT" }, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided." }, { "label": "section_header", "id": 7, "page_no": 6, "cluster": { "id": 7, "label": "section_header", "bbox": { "l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT" }, "confidence": 0.9589217305183411, "cells": [ { "index": 204, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 618.65102, "r_x1": 418.54776, "r_y1": 618.65102, "r_x2": 418.54776, "r_y2": 608.34192, "r_x3": 317.95499, "r_y3": 608.34192, "coord_origin": "TOPLEFT" }, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Dataset Comparison" }, { "label": "text", "id": 2, "page_no": 6, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT" }, "confidence": 0.9872549772262573, "cells": [ { "index": 205, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 631.91817, "r_x1": 558.20575, "r_y1": 631.91817, "r_x2": 558.20575, "r_y2": 623.54355, "r_x3": 317.686, "r_y3": 623.54355, "coord_origin": "TOPLEFT" }, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "orig": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 206, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.87717, "r_x1": 558.20624, "r_y1": 642.87717, "r_x2": 558.20624, "r_y2": 634.50255, "r_x3": 317.95499, "r_y3": 634.50255, "coord_origin": "TOPLEFT" }, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 207, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 653.83617, "r_x1": 558.20319, "r_y1": 653.83617, "r_x2": 558.20319, "r_y2": 645.46155, "r_x3": 317.686, "r_y3": 645.46155, "coord_origin": "TOPLEFT" }, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 208, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 664.79517, "r_x1": 558.74377, "r_y1": 664.79517, "r_x2": 558.74377, "r_y2": 656.42055, "r_x3": 317.95499, "r_y3": 656.42055, "coord_origin": "TOPLEFT" }, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 209, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.7541699999999, "r_x1": 558.20398, "r_y1": 675.7541699999999, "r_x2": 558.20398, "r_y2": 667.37955, "r_x3": 317.95499, "r_y3": 667.37955, "coord_origin": "TOPLEFT" }, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 210, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 686.71317, "r_x1": 558.20062, "r_y1": 686.71317, "r_x2": 558.20062, "r_y2": 678.3385499999999, "r_x3": 317.95499, "r_y3": 678.3385499999999, "coord_origin": "TOPLEFT" }, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 211, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.672173, "r_x1": 558.20343, "r_y1": 697.672173, "r_x2": 558.20343, "r_y2": 689.29755, "r_x3": 317.95499, "r_y3": 689.29755, "coord_origin": "TOPLEFT" }, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 212, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.631172, "r_x1": 531.07666, "r_y1": 708.631172, "r_x2": 531.07666, "r_y2": 700.256554, "r_x3": 317.95499, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 213, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 533.16199, "r_y0": 708.640137, "r_x1": 557.2561, "r_y1": 708.640137, "r_x2": 557.2561, "r_y2": 700.301384, "r_x3": 533.16199, "r_y3": 700.301384, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 214, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 557.255, "r_y0": 708.631172, "r_x1": 559.18817, "r_y1": 708.631172, "r_x2": 559.18817, "r_y2": 700.256554, "r_x3": 557.255, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ," } ], "body": [ { "label": "text", "id": 14, "page_no": 6, "cluster": { "id": 14, "label": "text", "bbox": { "l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT" }, "confidence": 0.8296934366226196, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.0437, "r_y1": 95.34618999999998, "r_x2": 294.0437, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 294.04376, "r_y1": 106.30517999999995, "r_x2": 294.04376, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 117.26415999999995, "r_x1": 295.64865, "r_y1": 117.26415999999995, "r_x2": 295.64865, "r_y2": 108.79088999999999, "r_x3": 53.79800000000001, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "class label sets. The reduced label sets were obtained by ei-", "orig": "class label sets. The reduced label sets were obtained by ei-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 213.23856, "r_y1": 128.22313999999994, "r_x2": 213.23856, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "ther down-mapping or dropping labels.", "orig": "ther down-mapping or dropping labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. 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"One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 73, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 52.785, "r_y0": 354.10516000000007, "r_x1": 295.55835, "r_y1": 354.10516000000007, "r_x2": 295.55835, "r_y2": 345.73053, "r_x3": 52.785, "r_y3": 345.73053, "coord_origin": "TOPLEFT" }, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "orig": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 74, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 365.06418, "r_x1": 294.04535, "r_y1": 365.06418, "r_x2": 294.04535, "r_y2": 356.6895400000001, "r_x3": 53.79800000000001, "r_y3": 356.6895400000001, "coord_origin": "TOPLEFT" }, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 376.02316, "r_x1": 295.4281, "r_y1": 376.02316, "r_x2": 295.4281, "r_y2": 367.64853, "r_x3": 53.79800000000001, "r_y3": 367.64853, "coord_origin": "TOPLEFT" }, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.484001, "r_y0": 386.98218, "r_x1": 295.55667, "r_y1": 386.98218, "r_x2": 295.55667, "r_y2": 378.60754, "r_x3": 53.484001, "r_y3": 378.60754, "coord_origin": "TOPLEFT" }, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 397.94116, "r_x1": 294.04865, "r_y1": 397.94116, "r_x2": 294.04865, "r_y2": 389.56653, "r_x3": 53.79800000000001, "r_y3": 389.56653, "coord_origin": "TOPLEFT" }, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 408.90018, "r_x1": 294.04376, "r_y1": 408.90018, "r_x2": 294.04376, "r_y2": 400.52554000000003, "r_x3": 53.79800000000001, "r_y3": 400.52554000000003, "coord_origin": "TOPLEFT" }, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 419.85919, "r_x1": 295.42459, "r_y1": 419.85919, "r_x2": 295.42459, "r_y2": 411.48456, "r_x3": 53.79800000000001, "r_y3": 411.48456, "coord_origin": "TOPLEFT" }, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 430.81818, "r_x1": 294.04709, "r_y1": 430.81818, "r_x2": 294.04709, "r_y2": 422.44354, "r_x3": 53.79800000000001, "r_y3": 422.44354, "coord_origin": "TOPLEFT" }, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 441.7771599999999, "r_x1": 294.04535, "r_y1": 441.7771599999999, "r_x2": 294.04535, "r_y2": 433.40253000000007, "r_x3": 53.466999, "r_y3": 433.40253000000007, "coord_origin": "TOPLEFT" }, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 452.73517, "r_x1": 295.42902, "r_y1": 452.73517, "r_x2": 295.42902, "r_y2": 444.36053000000004, "r_x3": 53.79800000000001, "r_y3": 444.36053000000004, "coord_origin": "TOPLEFT" }, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 463.69418, "r_x1": 295.03122, "r_y1": 463.69418, "r_x2": 295.03122, "r_y2": 455.31955, "r_x3": 53.528999, "r_y3": 455.31955, "coord_origin": "TOPLEFT" }, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 474.65317, "r_x1": 295.42154, "r_y1": 474.65317, "r_x2": 295.42154, "r_y2": 466.27853, "r_x3": 53.466999, "r_y3": 466.27853, "coord_origin": "TOPLEFT" }, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 485.61218, "r_x1": 294.04553, "r_y1": 485.61218, "r_x2": 294.04553, "r_y2": 477.23755, "r_x3": 53.528999, "r_y3": 477.23755, "coord_origin": "TOPLEFT" }, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 496.57117, "r_x1": 295.55646, "r_y1": 496.57117, "r_x2": 295.55646, "r_y2": 488.19653, "r_x3": 53.79800000000001, "r_y3": 488.19653, "coord_origin": "TOPLEFT" }, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 507.53018, "r_x1": 294.04715, "r_y1": 507.53018, "r_x2": 294.04715, "r_y2": 499.15555, "r_x3": 53.79800000000001, "r_y3": 499.15555, "coord_origin": "TOPLEFT" }, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 518.4891700000001, "r_x1": 294.04245, "r_y1": 518.4891700000001, "r_x2": 294.04245, "r_y2": 510.11453, "r_x3": 53.79800000000001, "r_y3": 510.11453, "coord_origin": "TOPLEFT" }, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 529.44818, "r_x1": 191.47707, "r_y1": 529.44818, "r_x2": 191.47707, "r_y2": 521.0735500000001, "r_x3": 53.79800000000001, "r_y3": 521.0735500000001, "coord_origin": "TOPLEFT" }, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles." }, { "label": "section_header", "id": 8, "page_no": 6, "cluster": { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT" }, "confidence": 0.9572026133537292, "cells": [ { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.81902, "r_x1": 164.32898, "r_y1": 552.81902, "r_x2": 164.32898, "r_y2": 542.50992, "r_x3": 53.79800000000001, "r_y3": 542.50992, "coord_origin": "TOPLEFT" }, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Impact of Class Labels" }, { "label": "text", "id": 1, "page_no": 6, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT" }, "confidence": 0.9876301884651184, "cells": [ { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 566.08617, "r_x1": 294.04333, "r_y1": 566.08617, "r_x2": 294.04333, "r_y2": 557.71155, "r_x3": 53.528999, "r_y3": 557.71155, "coord_origin": "TOPLEFT" }, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 577.04517, "r_x1": 294.04712, "r_y1": 577.04517, "r_x2": 294.04712, "r_y2": 568.6705499999999, "r_x3": 53.79800000000001, "r_y3": 568.6705499999999, "coord_origin": "TOPLEFT" }, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 588.0041699999999, "r_x1": 294.04538, "r_y1": 588.0041699999999, "r_x2": 294.04538, "r_y2": 579.62955, "r_x3": 53.79800000000001, "r_y3": 579.62955, "coord_origin": "TOPLEFT" }, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 598.96317, "r_x1": 294.04535, "r_y1": 598.96317, "r_x2": 294.04535, "r_y2": 590.5885499999999, "r_x3": 53.79800000000001, "r_y3": 590.5885499999999, "coord_origin": "TOPLEFT" }, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 609.92216, "r_x1": 294.04266, "r_y1": 609.92216, "r_x2": 294.04266, "r_y2": 601.54755, "r_x3": 53.79800000000001, "r_y3": 601.54755, "coord_origin": "TOPLEFT" }, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 620.88118, "r_x1": 163.59247, "r_y1": 620.88118, "r_x2": 163.59247, "r_y2": 612.50656, "r_x3": 53.79800000000001, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 166.26401, "r_y0": 620.89015, "r_x1": 194.97244, "r_y1": 620.89015, "r_x2": 194.97244, "r_y2": 612.55139, "r_x3": 166.26401, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 194.994, "r_y0": 620.15491, "r_x1": 204.1756, "r_y1": 620.15491, "r_x2": 204.1756, "r_y2": 612.45276, "r_x3": 194.994, "r_y3": 612.45276, "coord_origin": "TOPLEFT" }, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 204.17599, "r_y0": 620.89015, "r_x1": 219.33961000000002, "r_y1": 620.89015, "r_x2": 219.33961000000002, "r_y2": 612.55139, "r_x3": 204.17599, "r_y3": 612.55139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 219.849, "r_y0": 620.88118, "r_x1": 294.04828, "r_y1": 620.88118, "r_x2": 294.04828, "r_y2": 612.50656, "r_x3": 219.849, "r_y3": 612.50656, "coord_origin": "TOPLEFT" }, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 631.84018, "r_x1": 294.04709, "r_y1": 631.84018, "r_x2": 294.04709, "r_y2": 623.46556, "r_x3": 53.79800000000001, "r_y3": 623.46556, "coord_origin": "TOPLEFT" }, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 642.79817, "r_x1": 295.55679, "r_y1": 642.79817, "r_x2": 295.55679, "r_y2": 634.42355, "r_x3": 53.79800000000001, "r_y3": 634.42355, "coord_origin": "TOPLEFT" }, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 653.75717, "r_x1": 294.04715, "r_y1": 653.75717, "r_x2": 294.04715, "r_y2": 645.38255, "r_x3": 53.79800000000001, "r_y3": 645.38255, "coord_origin": "TOPLEFT" }, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 664.71617, "r_x1": 294.04715, "r_y1": 664.71617, "r_x2": 294.04715, "r_y2": 656.34155, "r_x3": 53.79800000000001, "r_y3": 656.34155, "coord_origin": "TOPLEFT" }, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 675.67517, "r_x1": 294.04712, "r_y1": 675.67517, "r_x2": 294.04712, "r_y2": 667.30055, "r_x3": 53.79800000000001, "r_y3": 667.30055, "coord_origin": "TOPLEFT" }, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 686.63417, "r_x1": 294.25174, "r_y1": 686.63417, "r_x2": 294.25174, "r_y2": 678.25955, "r_x3": 53.466999, "r_y3": 678.25955, "coord_origin": "TOPLEFT" }, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 697.59317, "r_x1": 294.04639, "r_y1": 697.59317, "r_x2": 294.04639, "r_y2": 689.21855, "r_x3": 53.79800000000001, "r_y3": 689.21855, "coord_origin": "TOPLEFT" }, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 708.55217, "r_x1": 294.04712, "r_y1": 708.55217, "r_x2": 294.04712, "r_y2": 700.177551, "r_x3": 53.79800000000001, "r_y3": 700.177551, "coord_origin": "TOPLEFT" }, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of" }, { "label": "text", "id": 13, "page_no": 6, "cluster": { "id": 13, "label": "text", "bbox": { "l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT" }, "confidence": 0.8736215829849243, "cells": [ { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.659, "r_y0": 95.34625000000017, "r_x1": 558.20068, "r_y1": 95.34625000000017, "r_x2": 558.20068, "r_y2": 86.87298999999996, "r_x3": 317.659, "r_y3": 86.87298999999996, "coord_origin": "TOPLEFT" }, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 106.30524000000003, "r_x1": 559.73401, "r_y1": 106.30524000000003, "r_x2": 559.73401, "r_y2": 97.83196999999996, "r_x3": 317.95499, "r_y3": 97.83196999999996, "coord_origin": "TOPLEFT" }, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 117.26422000000014, "r_x1": 467.72089, "r_y1": 117.26422000000014, "r_x2": 467.72089, "r_y2": 108.79094999999995, "r_x3": 317.95499, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "Naive page-wise split will result in", "orig": "Naive page-wise split will result in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 471.90900000000005, "r_y0": 115.19768999999997, "r_x1": 477.53900000000004, "r_y1": 115.19768999999997, "r_x2": 477.53900000000004, "r_y2": 107.12798999999995, "r_x3": 471.90900000000005, "r_y3": 107.12798999999995, "coord_origin": "TOPLEFT" }, "text": "GLYPH", "orig": "GLYPH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 477.54001000000005, "r_y0": 117.26422000000014, "r_x1": 559.80682, "r_y1": 117.26422000000014, "r_x2": 559.80682, "r_y2": 108.79094999999995, "r_x3": 477.54001000000005, "r_y3": 108.79094999999995, "coord_origin": "TOPLEFT" }, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 128.22321, "r_x1": 341.37524, "r_y1": 128.22321, "r_x2": 341.37524, "r_y2": 119.74993999999992, "r_x3": 317.95502, "r_y3": 119.74993999999992, "coord_origin": "TOPLEFT" }, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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176, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 487.70401, "r_y0": 361.70514, "r_x1": 502.86761, "r_y1": 361.70514, "r_x2": 502.86761, "r_y2": 353.36639, "r_x3": 487.70401, "r_y3": 353.36639, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 177, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 506.05499, "r_y0": 361.69617000000005, "r_x1": 559.58496, "r_y1": 361.69617000000005, "r_x2": 559.58496, "r_y2": 353.32153, "r_x3": 506.05499, "r_y3": 353.32153, "coord_origin": "TOPLEFT" }, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 178, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 372.65518, "r_x1": 558.2002, "r_y1": 372.65518, "r_x2": 558.2002, "r_y2": 364.28054999999995, "r_x3": 317.686, "r_y3": 364.28054999999995, "coord_origin": "TOPLEFT" }, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 179, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 383.61316, "r_x1": 558.36877, "r_y1": 383.61316, "r_x2": 558.36877, "r_y2": 375.23853, "r_x3": 317.95499, "r_y3": 375.23853, "coord_origin": "TOPLEFT" }, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 180, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 394.57217, "r_x1": 558.19958, "r_y1": 394.57217, "r_x2": 558.19958, "r_y2": 386.19754, "r_x3": 317.95499, "r_y3": 386.19754, "coord_origin": "TOPLEFT" }, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 181, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 405.53116000000006, "r_x1": 439.49454, "r_y1": 405.53116000000006, "r_x2": 439.49454, "r_y2": 397.15652, "r_x3": 317.95499, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 182, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 441.728, "r_y0": 405.54013, "r_x1": 481.8616, "r_y1": 405.54013, "r_x2": 481.8616, "r_y2": 397.20139, "r_x3": 441.728, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 183, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 484.74298, "r_y0": 405.53116000000006, "r_x1": 498.23743, "r_y1": 405.53116000000006, "r_x2": 498.23743, "r_y2": 397.15652, "r_x3": 484.74298, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 184, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 500.47299, "r_y0": 405.54013, "r_x1": 543.95105, "r_y1": 405.54013, "r_x2": 543.95105, "r_y2": 397.20139, "r_x3": 500.47299, "r_y3": 397.20139, "coord_origin": "TOPLEFT" }, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 185, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 546.83197, "r_y0": 405.53116000000006, "r_x1": 558.20142, "r_y1": 405.53116000000006, "r_x2": 558.20142, "r_y2": 397.15652, "r_x3": 546.83197, "r_y3": 397.15652, "coord_origin": "TOPLEFT" }, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 186, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 416.49017, "r_x1": 352.37698, "r_y1": 416.49017, "r_x2": 352.37698, "r_y2": 408.11553999999995, "r_x3": 317.95496, "r_y3": 408.11553999999995, "coord_origin": "TOPLEFT" }, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded." }, { "label": "section_header", "id": 9, "page_no": 6, "cluster": { "id": 9, "label": "section_header", "bbox": { "l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT" }, "confidence": 0.9562516808509827, "cells": [ { "index": 187, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95496, "r_y0": 439.70398, "r_x1": 549.8606, "r_y1": 439.70398, "r_x2": 549.8606, "r_y2": 429.3949, "r_x3": 317.95496, "r_y3": 429.3949, "coord_origin": "TOPLEFT" }, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Impact of Document Split in Train and Test Set" }, { "label": "text", "id": 3, "page_no": 6, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT" }, "confidence": 0.9869834184646606, "cells": [ { "index": 188, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 452.97116, "r_x1": 558.36884, "r_y1": 452.97116, "r_x2": 558.36884, "r_y2": 444.59653, "r_x3": 317.95499, "r_y3": 444.59653, "coord_origin": "TOPLEFT" }, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 189, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 463.93018, "r_x1": 559.19189, "r_y1": 463.93018, "r_x2": 559.19189, "r_y2": 455.55554, "r_x3": 317.95499, "r_y3": 455.55554, "coord_origin": "TOPLEFT" }, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 190, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 474.88916, "r_x1": 559.58185, "r_y1": 474.88916, "r_x2": 559.58185, "r_y2": 466.51453, "r_x3": 317.95499, "r_y3": 466.51453, "coord_origin": "TOPLEFT" }, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 191, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 485.84818, "r_x1": 558.20605, "r_y1": 485.84818, "r_x2": 558.20605, "r_y2": 477.47354, "r_x3": 317.95499, "r_y3": 477.47354, "coord_origin": "TOPLEFT" }, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 192, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 496.80716, "r_x1": 559.58203, "r_y1": 496.80716, "r_x2": 559.58203, "r_y2": 488.43253, "r_x3": 317.95499, "r_y3": 488.43253, "coord_origin": "TOPLEFT" }, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 193, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 507.76617, "r_x1": 558.20032, "r_y1": 507.76617, "r_x2": 558.20032, "r_y2": 499.39154, "r_x3": 317.686, "r_y3": 499.39154, "coord_origin": "TOPLEFT" }, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 194, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 518.72516, "r_x1": 559.5849, "r_y1": 518.72516, "r_x2": 559.5849, "r_y2": 510.35052, "r_x3": 317.95499, "r_y3": 510.35052, "coord_origin": "TOPLEFT" }, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 195, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 529.68417, "r_x1": 559.71381, "r_y1": 529.68417, "r_x2": 559.71381, "r_y2": 521.30954, "r_x3": 317.95499, "r_y3": 521.30954, "coord_origin": "TOPLEFT" }, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 196, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 540.64317, "r_x1": 559.18707, "r_y1": 540.64317, "r_x2": 559.18707, "r_y2": 532.26855, "r_x3": 317.95499, "r_y3": 532.26855, "coord_origin": "TOPLEFT" }, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 197, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 551.60117, "r_x1": 559.71313, "r_y1": 551.60117, "r_x2": 559.71313, "r_y2": 543.22655, "r_x3": 317.95499, "r_y3": 543.22655, "coord_origin": "TOPLEFT" }, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 198, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 562.56017, "r_x1": 388.35168, "r_y1": 562.56017, "r_x2": 388.35168, "r_y2": 554.18555, "r_x3": 317.62299, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "wise splitting gains", "orig": "wise splitting gains", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 199, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 391.36499, "r_y0": 560.87517, "r_x1": 393.98318, "r_y1": 560.87517, "r_x2": 393.98318, "r_y2": 552.50055, "r_x3": 391.36499, "r_y3": 552.50055, "coord_origin": "TOPLEFT" }, "text": "\u02dc", "orig": "\u02dc", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 200, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 390.59, "r_y0": 562.56017, "r_x1": 559.58191, "r_y1": 562.56017, "r_x2": 559.58191, "r_y2": 554.18555, "r_x3": 390.59, "r_y3": 554.18555, "coord_origin": "TOPLEFT" }, "text": "10% in mAP over the document-wise splitting.", "orig": "10% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 201, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 573.51917, "r_x1": 558.20032, "r_y1": 573.51917, "r_x2": 558.20032, "r_y2": 565.14455, "r_x3": 317.686, "r_y3": 565.14455, "coord_origin": "TOPLEFT" }, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 202, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 584.4781800000001, "r_x1": 558.20508, "r_y1": 584.4781800000001, "r_x2": 558.20508, "r_y2": 576.10356, "r_x3": 317.95499, "r_y3": 576.10356, "coord_origin": "TOPLEFT" }, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 203, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 595.43718, "r_x1": 348.50354, "r_y1": 595.43718, "r_x2": 348.50354, "r_y2": 587.06256, "r_x3": 317.95499, "r_y3": 587.06256, "coord_origin": "TOPLEFT" }, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided." }, { "label": "section_header", "id": 7, "page_no": 6, "cluster": { "id": 7, "label": "section_header", "bbox": { "l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT" }, "confidence": 0.9589217305183411, "cells": [ { "index": 204, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 618.65102, "r_x1": 418.54776, "r_y1": 618.65102, "r_x2": 418.54776, "r_y2": 608.34192, "r_x3": 317.95499, "r_y3": 608.34192, "coord_origin": "TOPLEFT" }, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Dataset Comparison" }, { "label": "text", "id": 2, "page_no": 6, "cluster": { "id": 2, "label": "text", "bbox": { "l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT" }, "confidence": 0.9872549772262573, "cells": [ { "index": 205, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 631.91817, "r_x1": 558.20575, "r_y1": 631.91817, "r_x2": 558.20575, "r_y2": 623.54355, "r_x3": 317.686, "r_y3": 623.54355, "coord_origin": "TOPLEFT" }, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "orig": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 206, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 642.87717, "r_x1": 558.20624, "r_y1": 642.87717, "r_x2": 558.20624, "r_y2": 634.50255, "r_x3": 317.95499, "r_y3": 634.50255, "coord_origin": "TOPLEFT" }, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 207, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 653.83617, "r_x1": 558.20319, "r_y1": 653.83617, "r_x2": 558.20319, "r_y2": 645.46155, "r_x3": 317.686, "r_y3": 645.46155, "coord_origin": "TOPLEFT" }, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 208, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 664.79517, "r_x1": 558.74377, "r_y1": 664.79517, "r_x2": 558.74377, "r_y2": 656.42055, "r_x3": 317.95499, "r_y3": 656.42055, "coord_origin": "TOPLEFT" }, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 209, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 675.7541699999999, "r_x1": 558.20398, "r_y1": 675.7541699999999, "r_x2": 558.20398, "r_y2": 667.37955, "r_x3": 317.95499, "r_y3": 667.37955, "coord_origin": "TOPLEFT" }, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 210, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 686.71317, "r_x1": 558.20062, "r_y1": 686.71317, "r_x2": 558.20062, "r_y2": 678.3385499999999, "r_x3": 317.95499, "r_y3": 678.3385499999999, "coord_origin": "TOPLEFT" }, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 211, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 697.672173, "r_x1": 558.20343, "r_y1": 697.672173, "r_x2": 558.20343, "r_y2": 689.29755, "r_x3": 317.95499, "r_y3": 689.29755, "coord_origin": "TOPLEFT" }, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 212, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 708.631172, "r_x1": 531.07666, "r_y1": 708.631172, "r_x2": 531.07666, "r_y2": 700.256554, "r_x3": 317.95499, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 213, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 533.16199, "r_y0": 708.640137, "r_x1": 557.2561, "r_y1": 708.640137, "r_x2": 557.2561, "r_y2": 700.301384, "r_x3": 533.16199, "r_y3": 700.301384, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 214, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 557.255, "r_y0": 708.631172, "r_x1": 559.18817, "r_y1": 708.631172, "r_x2": 559.18817, "r_y2": 700.256554, "r_x3": 557.255, "r_y3": 700.256554, "coord_origin": "TOPLEFT" }, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ," } ], "headers": [ { "label": "page_header", "id": 12, "page_no": 6, "cluster": { "id": 12, "label": "page_header", "bbox": { "l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.9316118955612183, "cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 347.01724, "r_y1": 68.57605000000012, "r_x2": 347.01724, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" }, { "label": "page_header", "id": 11, "page_no": 6, "cluster": { "id": 11, "label": "page_header", "bbox": { "l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT" }, "confidence": 0.9318180084228516, "cells": [ { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 365.75702, "r_y0": 68.57605000000012, "r_x1": 558.20282, "r_y1": 68.57605000000012, "r_x2": 558.20282, "r_y2": 60.30902000000003, "r_x3": 365.75702, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA" } ] } }, { "page_no": 7, "size": { "width": 612.0, "height": 792.0 }, "parsed_page": { "dimension": { "angle": 0.0, "rect": { "r_x0": 0.0, "r_y0": 0.0, "r_x1": 612.0, "r_y1": 0.0, "r_x2": 612.0, "r_y2": 792.0, "r_x3": 0.0, "r_y3": 792.0, "coord_origin": "BOTTOMLEFT" }, "boundary_type": "crop_box", "art_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "bleed_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "crop_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "media_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" }, "trim_bbox": { "l": 0.0, "t": 792.0, "r": 612.0, "b": 0.0, "coord_origin": "BOTTOMLEFT" } }, "bitmap_resources": [], "char_cells": [], "word_cells": [], "textline_cells": [ { "index": 0, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 68.57605000000012, "r_x1": 246.24382, "r_y1": 68.57605000000012, "r_x2": 246.24382, "r_y2": 60.30902000000003, "r_x3": 53.79800000000001, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 1, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 253.13897999999998, "r_y0": 68.57605000000012, "r_x1": 558.20288, "r_y1": 68.57605000000012, "r_x2": 558.20288, "r_y2": 60.30902000000003, "r_x3": 253.13897999999998, "r_y3": 60.30902000000003, "coord_origin": "TOPLEFT" }, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.32153, "r_y1": 95.34618999999998, "r_x2": 294.32153, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 295.64868, "r_y1": 106.30517999999995, "r_x2": 295.64868, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.79800000000001, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. 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Before training, we either mapped", "orig": ". Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.25418, "r_x1": 294.04712, "r_y1": 410.25418, "r_x2": 294.04712, "r_y2": 401.87954999999994, "r_x3": 53.79800000000001, "r_y3": 401.87954999999994, "coord_origin": "TOPLEFT" }, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "orig": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.21317, "r_x1": 117.12856, "r_y1": 421.21317, "r_x2": 117.12856, "r_y2": 412.83853, "r_x3": 53.79800000000001, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "also PubLayNet\u2019s", "orig": "also PubLayNet\u2019s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 119.362, "r_y0": 421.22214, "r_x1": 132.4342, "r_y1": 421.22214, "r_x2": 132.4342, "r_y2": 412.88339, "r_x3": 119.362, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 135.177, "r_y0": 421.21317, "r_x1": 142.54416, "r_y1": 421.21317, "r_x2": 142.54416, "r_y2": 412.83853, "r_x3": 135.177, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.77901, "r_y0": 421.22214, "r_x1": 159.66605, "r_y1": 421.22214, "r_x2": 159.66605, "r_y2": 412.88339, "r_x3": 144.77901, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 159.66701, "r_y0": 421.21317, "r_x1": 294.04562, "r_y1": 421.21317, "r_x2": 294.04562, "r_y2": 412.83853, "r_x3": 159.66701, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": ". Note that the different clustering of", "orig": ". Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.17217999999997, "r_x1": 294.04614, "r_y1": 432.17217999999997, "r_x2": 294.04614, "r_y2": 423.79755, "r_x3": 53.79800000000001, "r_y3": 423.79755, "coord_origin": "TOPLEFT" }, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.13116, "r_x1": 106.2066, "r_y1": 443.13116, "r_x2": 106.2066, "r_y2": 434.75653, "r_x3": 53.79800000000001, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 108.448, "r_y0": 443.14014, "r_x1": 123.30533, "r_y1": 443.14014, "r_x2": 123.30533, "r_y2": 434.80139, "r_x3": 108.448, "r_y3": 434.80139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 123.305, "r_y0": 443.13116, "r_x1": 125.27761000000001, "r_y1": 443.13116, "r_x2": 125.27761000000001, "r_y2": 434.75653, "r_x3": 123.305, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 454.08917, "r_x1": 294.27582, "r_y1": 454.08917, "r_x2": 294.27582, "r_y2": 445.71453999999994, "r_x3": 63.76100199999999, "r_y3": 445.71453999999994, "coord_origin": "TOPLEFT" }, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.04819, "r_x1": 62.989277, "r_y1": 465.04819, "r_x2": 62.989277, "r_y2": 456.67355, "r_x3": 53.79800000000001, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.852997, "r_y0": 465.05716, "r_x1": 88.947144, "r_y1": 465.05716, "r_x2": 88.947144, "r_y2": 456.71841, "r_x3": 64.852997, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 91.019997, "r_y0": 465.04819, "r_x1": 104.24454, "r_y1": 465.04819, "r_x2": 104.24454, "r_y2": 456.67355, "r_x3": 91.019997, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 106.108, "r_y0": 465.05716, "r_x1": 124.78053, "r_y1": 465.05716, "r_x2": 124.78053, "r_y2": 456.71841, "r_x3": 106.108, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 126.85500000000002, "r_y0": 465.04819, "r_x1": 277.63235, "r_y1": 465.04819, "r_x2": 277.63235, "r_y2": 456.67355, "r_x3": 126.85500000000002, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 279.49701, "r_y0": 465.05716, "r_x1": 294.0484, "r_y1": 465.05716, "r_x2": 294.0484, "r_y2": 456.71841, "r_x3": 279.49701, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 476.00717, "r_x1": 294.04709, "r_y1": 476.00717, "r_x2": 294.04709, "r_y2": 467.63254, "r_x3": 53.79800000000001, "r_y3": 467.63254, "coord_origin": "TOPLEFT" }, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 486.96619, "r_x1": 294.04532, "r_y1": 486.96619, "r_x2": 294.04532, "r_y2": 478.59155, "r_x3": 53.79800000000001, "r_y3": 478.59155, "coord_origin": "TOPLEFT" }, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 497.92517, "r_x1": 294.04538, "r_y1": 497.92517, "r_x2": 294.04538, "r_y2": 489.55054, "r_x3": 53.466999, "r_y3": 489.55054, "coord_origin": "TOPLEFT" }, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 508.88419, "r_x1": 295.55908, "r_y1": 508.88419, "r_x2": 295.55908, "r_y2": 500.50955, "r_x3": 53.528999, "r_y3": 500.50955, "coord_origin": "TOPLEFT" }, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 519.84317, "r_x1": 294.04712, "r_y1": 519.84317, "r_x2": 294.04712, "r_y2": 511.46854, "r_x3": 53.79800000000001, "r_y3": 511.46854, "coord_origin": "TOPLEFT" }, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 530.80219, "r_x1": 294.21179, "r_y1": 530.80219, "r_x2": 294.21179, "r_y2": 522.42755, "r_x3": 53.79800000000001, "r_y3": 522.42755, "coord_origin": "TOPLEFT" }, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 541.76117, "r_x1": 294.04712, "r_y1": 541.76117, "r_x2": 294.04712, "r_y2": 533.38654, "r_x3": 53.79800000000001, "r_y3": 533.38654, "coord_origin": "TOPLEFT" }, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.72017, "r_x1": 294.04715, "r_y1": 552.72017, "r_x2": 294.04715, "r_y2": 544.34555, "r_x3": 53.79800000000001, "r_y3": 544.34555, "coord_origin": "TOPLEFT" }, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.67917, "r_x1": 294.04712, "r_y1": 563.67917, "r_x2": 294.04712, "r_y2": 555.3045500000001, "r_x3": 53.79800000000001, "r_y3": 555.3045500000001, "coord_origin": "TOPLEFT" }, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.63718, "r_x1": 294.21411, "r_y1": 574.63718, "r_x2": 294.21411, "r_y2": 566.26256, "r_x3": 53.79800000000001, "r_y3": 566.26256, "coord_origin": "TOPLEFT" }, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 585.59618, "r_x1": 157.52132, "r_y1": 585.59618, "r_x2": 157.52132, "r_y2": 577.22156, "r_x3": 53.79800000000001, "r_y3": 577.22156, "coord_origin": "TOPLEFT" }, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 615.37001, "r_x1": 156.00534, "r_y1": 615.37001, "r_x2": 156.00534, "r_y2": 605.06091, "r_x3": 53.79800000000001, "r_y3": 605.06091, "coord_origin": "TOPLEFT" }, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 628.63817, "r_x1": 295.5571, "r_y1": 628.63817, "r_x2": 295.5571, "r_y2": 620.26355, "r_x3": 53.528999, "r_y3": 620.26355, "coord_origin": "TOPLEFT" }, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.59717, "r_x1": 294.04532, "r_y1": 639.59717, "r_x2": 294.04532, "r_y2": 631.22255, "r_x3": 53.79800000000001, "r_y3": 631.22255, "coord_origin": "TOPLEFT" }, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.5561700000001, "r_x1": 295.55618, "r_y1": 650.5561700000001, "r_x2": 295.55618, "r_y2": 642.18155, "r_x3": 53.79800000000001, "r_y3": 642.18155, "coord_origin": "TOPLEFT" }, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.51517, "r_x1": 294.04541, "r_y1": 661.51517, "r_x2": 294.04541, "r_y2": 653.14055, "r_x3": 53.79800000000001, "r_y3": 653.14055, "coord_origin": "TOPLEFT" }, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.47417, "r_x1": 295.55844, "r_y1": 672.47417, "r_x2": 295.55844, "r_y2": 664.09956, "r_x3": 53.79800000000001, "r_y3": 664.09956, "coord_origin": "TOPLEFT" }, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.43217, "r_x1": 294.04712, "r_y1": 683.43217, "r_x2": 294.04712, "r_y2": 675.05756, "r_x3": 53.79800000000001, "r_y3": 675.05756, "coord_origin": "TOPLEFT" }, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.391174, "r_x1": 294.04535, "r_y1": 694.391174, "r_x2": 294.04535, "r_y2": 686.01656, "r_x3": 53.79800000000001, "r_y3": 686.01656, "coord_origin": "TOPLEFT" }, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.350174, "r_x1": 136.07368, "r_y1": 705.350174, "r_x2": 136.07368, "r_y2": 696.975555, "r_x3": 53.79800000000001, "r_y3": 696.975555, "coord_origin": "TOPLEFT" }, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 96.16900999999996, "r_x1": 323.56229, "r_y1": 96.16900999999996, "r_x2": 323.56229, "r_y2": 85.85986000000003, "r_x3": 317.95502, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47137, "r_y0": 96.16900999999996, "r_x1": 405.72961, "r_y1": 96.16900999999996, "r_x2": 405.72961, "r_y2": 85.85986000000003, "r_x3": 334.47137, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 109.43615999999997, "r_x1": 558.2038, "r_y1": 109.43615999999997, "r_x2": 558.2038, "r_y2": 101.06151999999997, "r_x3": 317.95499, "r_y3": 101.06151999999997, "coord_origin": "TOPLEFT" }, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 120.39520000000005, "r_x1": 558.2041, "r_y1": 120.39520000000005, "r_x2": 558.2041, "r_y2": 112.02057000000002, "r_x3": 317.95499, "r_y3": 112.02057000000002, "coord_origin": "TOPLEFT" }, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 131.35419000000002, "r_x1": 558.43274, "r_y1": 131.35419000000002, "r_x2": 558.43274, "r_y2": 122.97955000000002, "r_x3": 317.95499, "r_y3": 122.97955000000002, "coord_origin": "TOPLEFT" }, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 142.31317, "r_x1": 558.20416, "r_y1": 142.31317, "r_x2": 558.20416, "r_y2": 133.93854, "r_x3": 317.95499, "r_y3": 133.93854, "coord_origin": "TOPLEFT" }, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 153.27215999999999, "r_x1": 558.20422, "r_y1": 153.27215999999999, "r_x2": 558.20422, "r_y2": 144.89752, "r_x3": 317.95499, "r_y3": 144.89752, "coord_origin": "TOPLEFT" }, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 164.23119999999994, "r_x1": 559.71313, "r_y1": 164.23119999999994, "r_x2": 559.71313, "r_y2": 155.85657000000003, "r_x3": 317.95499, "r_y3": 155.85657000000003, "coord_origin": "TOPLEFT" }, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 175.19019000000003, "r_x1": 559.71375, "r_y1": 175.19019000000003, "r_x2": 559.71375, "r_y2": 166.81555000000003, "r_x3": 317.95499, "r_y3": 166.81555000000003, "coord_origin": "TOPLEFT" }, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 186.14917000000003, "r_x1": 540.04382, "r_y1": 186.14917000000003, "r_x2": 540.04382, "r_y2": 177.77454, "r_x3": 317.95499, "r_y3": 177.77454, "coord_origin": "TOPLEFT" }, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 197.10815000000002, "r_x1": 558.19836, "r_y1": 197.10815000000002, "r_x2": 558.19836, "r_y2": 188.73352, "r_x3": 327.918, "r_y3": 188.73352, "coord_origin": "TOPLEFT" }, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 208.06719999999996, "r_x1": 558.20404, "r_y1": 208.06719999999996, "r_x2": 558.20404, "r_y2": 199.69257000000005, "r_x3": 317.95499, "r_y3": 199.69257000000005, "coord_origin": "TOPLEFT" }, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 219.02617999999995, "r_x1": 559.71704, "r_y1": 219.02617999999995, "r_x2": 559.71704, "r_y2": 210.65155000000004, "r_x3": 317.686, "r_y3": 210.65155000000004, "coord_origin": "TOPLEFT" }, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 229.98419, "r_x1": 558.20245, "r_y1": 229.98419, "r_x2": 558.20245, "r_y2": 221.60956, "r_x3": 317.95499, "r_y3": 221.60956, "coord_origin": "TOPLEFT" }, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 240.94317999999998, "r_x1": 558.20502, "r_y1": 240.94317999999998, "r_x2": 558.20502, "r_y2": 232.56853999999998, "r_x3": 317.95499, "r_y3": 232.56853999999998, "coord_origin": "TOPLEFT" }, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 251.90215999999998, "r_x1": 559.18408, "r_y1": 251.90215999999998, "r_x2": 559.18408, "r_y2": 243.52752999999996, "r_x3": 317.95499, "r_y3": 243.52752999999996, "coord_origin": "TOPLEFT" }, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 262.86114999999995, "r_x1": 558.20227, "r_y1": 262.86114999999995, "r_x2": 558.20227, "r_y2": 254.48650999999995, "r_x3": 317.95499, "r_y3": 254.48650999999995, "coord_origin": "TOPLEFT" }, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 273.82019, "r_x1": 558.20349, "r_y1": 273.82019, "r_x2": 558.20349, "r_y2": 265.44556, "r_x3": 317.95499, "r_y3": 265.44556, "coord_origin": "TOPLEFT" }, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 284.77917, "r_x1": 540.99426, "r_y1": 284.77917, "r_x2": 540.99426, "r_y2": 276.40454, "r_x3": 317.95499, "r_y3": 276.40454, "coord_origin": "TOPLEFT" }, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 295.73816, "r_x1": 558.43469, "r_y1": 295.73816, "r_x2": 558.43469, "r_y2": 287.36353, "r_x3": 327.918, "r_y3": 287.36353, "coord_origin": "TOPLEFT" }, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 306.69717, "r_x1": 558.20013, "r_y1": 306.69717, "r_x2": 558.20013, "r_y2": 298.32254, "r_x3": 317.95499, "r_y3": 298.32254, "coord_origin": "TOPLEFT" }, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 317.65616000000006, "r_x1": 535.65015, "r_y1": 317.65616000000006, "r_x2": 535.65015, "r_y2": 309.28152, "r_x3": 317.62299, "r_y3": 309.28152, "coord_origin": "TOPLEFT" }, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 345.40097, "r_x1": 387.3696, "r_y1": 345.40097, "r_x2": 387.3696, "r_y2": 335.09189, "r_x3": 317.95499, "r_y3": 335.09189, "coord_origin": "TOPLEFT" }, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 321.198, "r_y0": 355.21588, "r_x1": 329.72415, "r_y1": 355.21588, "r_x2": 329.72415, "r_y2": 348.70233, "r_x3": 321.198, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.53516, "r_y0": 355.21588, "r_x1": 558.19904, "r_y1": 355.21588, "r_x2": 558.19904, "r_y2": 348.70233, "r_x3": 331.53516, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "orig": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 363.18591, "r_x1": 379.36414, "r_y1": 363.18591, "r_x2": 379.36414, "r_y2": 356.67236, "r_x3": 333.39099, "r_y3": 356.67236, "coord_origin": "TOPLEFT" }, "text": "competition. In", "orig": "competition. 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Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.25418, "r_x1": 294.04712, "r_y1": 410.25418, "r_x2": 294.04712, "r_y2": 401.87954999999994, "r_x3": 53.79800000000001, "r_y3": 401.87954999999994, "coord_origin": "TOPLEFT" }, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "orig": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.21317, "r_x1": 117.12856, "r_y1": 421.21317, "r_x2": 117.12856, "r_y2": 412.83853, "r_x3": 53.79800000000001, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "also PubLayNet\u2019s", "orig": "also PubLayNet\u2019s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 119.362, "r_y0": 421.22214, "r_x1": 132.4342, "r_y1": 421.22214, "r_x2": 132.4342, "r_y2": 412.88339, "r_x3": 119.362, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 135.177, "r_y0": 421.21317, "r_x1": 142.54416, "r_y1": 421.21317, "r_x2": 142.54416, "r_y2": 412.83853, "r_x3": 135.177, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.77901, "r_y0": 421.22214, "r_x1": 159.66605, "r_y1": 421.22214, "r_x2": 159.66605, "r_y2": 412.88339, "r_x3": 144.77901, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 159.66701, "r_y0": 421.21317, "r_x1": 294.04562, "r_y1": 421.21317, "r_x2": 294.04562, "r_y2": 412.83853, "r_x3": 159.66701, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": ". Note that the different clustering of", "orig": ". Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.17217999999997, "r_x1": 294.04614, "r_y1": 432.17217999999997, "r_x2": 294.04614, "r_y2": 423.79755, "r_x3": 53.79800000000001, "r_y3": 423.79755, "coord_origin": "TOPLEFT" }, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.13116, "r_x1": 106.2066, "r_y1": 443.13116, "r_x2": 106.2066, "r_y2": 434.75653, "r_x3": 53.79800000000001, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 108.448, "r_y0": 443.14014, "r_x1": 123.30533, "r_y1": 443.14014, "r_x2": 123.30533, "r_y2": 434.80139, "r_x3": 108.448, "r_y3": 434.80139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 123.305, "r_y0": 443.13116, "r_x1": 125.27761000000001, "r_y1": 443.13116, "r_x2": 125.27761000000001, "r_y2": 434.75653, "r_x3": 123.305, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT" }, "confidence": 0.9868056774139404, "cells": [ { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 454.08917, "r_x1": 294.27582, "r_y1": 454.08917, "r_x2": 294.27582, "r_y2": 445.71453999999994, "r_x3": 63.76100199999999, "r_y3": 445.71453999999994, "coord_origin": "TOPLEFT" }, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.04819, "r_x1": 62.989277, "r_y1": 465.04819, "r_x2": 62.989277, "r_y2": 456.67355, "r_x3": 53.79800000000001, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.852997, "r_y0": 465.05716, "r_x1": 88.947144, "r_y1": 465.05716, "r_x2": 88.947144, "r_y2": 456.71841, "r_x3": 64.852997, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 91.019997, "r_y0": 465.04819, "r_x1": 104.24454, "r_y1": 465.04819, "r_x2": 104.24454, "r_y2": 456.67355, "r_x3": 91.019997, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 106.108, "r_y0": 465.05716, "r_x1": 124.78053, "r_y1": 465.05716, "r_x2": 124.78053, "r_y2": 456.71841, "r_x3": 106.108, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 126.85500000000002, "r_y0": 465.04819, "r_x1": 277.63235, "r_y1": 465.04819, "r_x2": 277.63235, "r_y2": 456.67355, "r_x3": 126.85500000000002, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 279.49701, "r_y0": 465.05716, "r_x1": 294.0484, "r_y1": 465.05716, "r_x2": 294.0484, "r_y2": 456.71841, "r_x3": 279.49701, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 476.00717, "r_x1": 294.04709, "r_y1": 476.00717, "r_x2": 294.04709, "r_y2": 467.63254, "r_x3": 53.79800000000001, "r_y3": 467.63254, "coord_origin": "TOPLEFT" }, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 486.96619, "r_x1": 294.04532, "r_y1": 486.96619, "r_x2": 294.04532, "r_y2": 478.59155, "r_x3": 53.79800000000001, "r_y3": 478.59155, "coord_origin": "TOPLEFT" }, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 497.92517, "r_x1": 294.04538, "r_y1": 497.92517, "r_x2": 294.04538, "r_y2": 489.55054, "r_x3": 53.466999, "r_y3": 489.55054, "coord_origin": "TOPLEFT" }, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 508.88419, "r_x1": 295.55908, "r_y1": 508.88419, "r_x2": 295.55908, "r_y2": 500.50955, "r_x3": 53.528999, "r_y3": 500.50955, "coord_origin": "TOPLEFT" }, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 519.84317, "r_x1": 294.04712, "r_y1": 519.84317, "r_x2": 294.04712, "r_y2": 511.46854, "r_x3": 53.79800000000001, "r_y3": 511.46854, "coord_origin": "TOPLEFT" }, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 530.80219, "r_x1": 294.21179, "r_y1": 530.80219, "r_x2": 294.21179, "r_y2": 522.42755, "r_x3": 53.79800000000001, "r_y3": 522.42755, "coord_origin": "TOPLEFT" }, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 541.76117, "r_x1": 294.04712, "r_y1": 541.76117, "r_x2": 294.04712, "r_y2": 533.38654, "r_x3": 53.79800000000001, "r_y3": 533.38654, "coord_origin": "TOPLEFT" }, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.72017, "r_x1": 294.04715, "r_y1": 552.72017, "r_x2": 294.04715, "r_y2": 544.34555, "r_x3": 53.79800000000001, "r_y3": 544.34555, "coord_origin": "TOPLEFT" }, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.67917, "r_x1": 294.04712, "r_y1": 563.67917, "r_x2": 294.04712, "r_y2": 555.3045500000001, "r_x3": 53.79800000000001, "r_y3": 555.3045500000001, "coord_origin": "TOPLEFT" }, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.63718, "r_x1": 294.21411, "r_y1": 574.63718, "r_x2": 294.21411, "r_y2": 566.26256, "r_x3": 53.79800000000001, "r_y3": 566.26256, "coord_origin": "TOPLEFT" }, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 585.59618, "r_x1": 157.52132, "r_y1": 585.59618, "r_x2": 157.52132, "r_y2": 577.22156, "r_x3": 53.79800000000001, "r_y3": 577.22156, "coord_origin": "TOPLEFT" }, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT" }, "confidence": 0.9574698805809021, "cells": [ { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 615.37001, "r_x1": 156.00534, "r_y1": 615.37001, "r_x2": 156.00534, "r_y2": 605.06091, "r_x3": 53.79800000000001, "r_y3": 605.06091, "coord_origin": "TOPLEFT" }, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 2, "label": "text", "bbox": { "l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT" }, "confidence": 0.9859886169433594, "cells": [ { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 628.63817, "r_x1": 295.5571, "r_y1": 628.63817, "r_x2": 295.5571, "r_y2": 620.26355, "r_x3": 53.528999, "r_y3": 620.26355, "coord_origin": "TOPLEFT" }, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.59717, "r_x1": 294.04532, "r_y1": 639.59717, "r_x2": 294.04532, "r_y2": 631.22255, "r_x3": 53.79800000000001, "r_y3": 631.22255, "coord_origin": "TOPLEFT" }, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.5561700000001, "r_x1": 295.55618, "r_y1": 650.5561700000001, "r_x2": 295.55618, "r_y2": 642.18155, "r_x3": 53.79800000000001, "r_y3": 642.18155, "coord_origin": "TOPLEFT" }, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.51517, "r_x1": 294.04541, "r_y1": 661.51517, "r_x2": 294.04541, "r_y2": 653.14055, "r_x3": 53.79800000000001, "r_y3": 653.14055, "coord_origin": "TOPLEFT" }, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.47417, "r_x1": 295.55844, "r_y1": 672.47417, "r_x2": 295.55844, "r_y2": 664.09956, "r_x3": 53.79800000000001, "r_y3": 664.09956, "coord_origin": "TOPLEFT" }, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.43217, "r_x1": 294.04712, "r_y1": 683.43217, "r_x2": 294.04712, "r_y2": 675.05756, "r_x3": 53.79800000000001, "r_y3": 675.05756, "coord_origin": "TOPLEFT" }, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.391174, "r_x1": 294.04535, "r_y1": 694.391174, "r_x2": 294.04535, "r_y2": 686.01656, "r_x3": 53.79800000000001, "r_y3": 686.01656, "coord_origin": "TOPLEFT" }, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.350174, "r_x1": 136.07368, "r_y1": 705.350174, "r_x2": 136.07368, "r_y2": 696.975555, "r_x3": 53.79800000000001, "r_y3": 696.975555, "coord_origin": "TOPLEFT" }, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 13, "label": "section_header", "bbox": { "l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.931998074054718, "cells": [ { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 96.16900999999996, "r_x1": 323.56229, "r_y1": 96.16900999999996, "r_x2": 323.56229, "r_y2": 85.85986000000003, "r_x3": 317.95502, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47137, "r_y0": 96.16900999999996, "r_x1": 405.72961, "r_y1": 96.16900999999996, "r_x2": 405.72961, "r_y2": 85.85986000000003, "r_x3": 334.47137, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 3, "label": "text", "bbox": { "l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT" }, "confidence": 0.985493004322052, "cells": [ { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 109.43615999999997, "r_x1": 558.2038, "r_y1": 109.43615999999997, "r_x2": 558.2038, "r_y2": 101.06151999999997, "r_x3": 317.95499, "r_y3": 101.06151999999997, "coord_origin": "TOPLEFT" }, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 120.39520000000005, "r_x1": 558.2041, "r_y1": 120.39520000000005, "r_x2": 558.2041, "r_y2": 112.02057000000002, "r_x3": 317.95499, "r_y3": 112.02057000000002, "coord_origin": "TOPLEFT" }, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 131.35419000000002, "r_x1": 558.43274, "r_y1": 131.35419000000002, "r_x2": 558.43274, "r_y2": 122.97955000000002, "r_x3": 317.95499, "r_y3": 122.97955000000002, "coord_origin": "TOPLEFT" }, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 142.31317, "r_x1": 558.20416, "r_y1": 142.31317, "r_x2": 558.20416, "r_y2": 133.93854, "r_x3": 317.95499, "r_y3": 133.93854, "coord_origin": "TOPLEFT" }, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 153.27215999999999, "r_x1": 558.20422, "r_y1": 153.27215999999999, "r_x2": 558.20422, "r_y2": 144.89752, "r_x3": 317.95499, "r_y3": 144.89752, "coord_origin": "TOPLEFT" }, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 164.23119999999994, "r_x1": 559.71313, "r_y1": 164.23119999999994, "r_x2": 559.71313, "r_y2": 155.85657000000003, "r_x3": 317.95499, "r_y3": 155.85657000000003, "coord_origin": "TOPLEFT" }, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 175.19019000000003, "r_x1": 559.71375, "r_y1": 175.19019000000003, "r_x2": 559.71375, "r_y2": 166.81555000000003, "r_x3": 317.95499, "r_y3": 166.81555000000003, "coord_origin": "TOPLEFT" }, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 186.14917000000003, "r_x1": 540.04382, "r_y1": 186.14917000000003, "r_x2": 540.04382, "r_y2": 177.77454, "r_x3": 317.95499, "r_y3": 177.77454, "coord_origin": "TOPLEFT" }, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 6, "label": "text", "bbox": { "l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT" }, "confidence": 0.9800531268119812, "cells": [ { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 197.10815000000002, "r_x1": 558.19836, "r_y1": 197.10815000000002, "r_x2": 558.19836, "r_y2": 188.73352, "r_x3": 327.918, "r_y3": 188.73352, "coord_origin": "TOPLEFT" }, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 208.06719999999996, "r_x1": 558.20404, "r_y1": 208.06719999999996, "r_x2": 558.20404, "r_y2": 199.69257000000005, "r_x3": 317.95499, "r_y3": 199.69257000000005, "coord_origin": "TOPLEFT" }, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 219.02617999999995, "r_x1": 559.71704, "r_y1": 219.02617999999995, "r_x2": 559.71704, "r_y2": 210.65155000000004, "r_x3": 317.686, "r_y3": 210.65155000000004, "coord_origin": "TOPLEFT" }, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 229.98419, "r_x1": 558.20245, "r_y1": 229.98419, "r_x2": 558.20245, "r_y2": 221.60956, "r_x3": 317.95499, "r_y3": 221.60956, "coord_origin": "TOPLEFT" }, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 240.94317999999998, "r_x1": 558.20502, "r_y1": 240.94317999999998, "r_x2": 558.20502, "r_y2": 232.56853999999998, "r_x3": 317.95499, "r_y3": 232.56853999999998, "coord_origin": "TOPLEFT" }, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 251.90215999999998, "r_x1": 559.18408, "r_y1": 251.90215999999998, "r_x2": 559.18408, "r_y2": 243.52752999999996, "r_x3": 317.95499, "r_y3": 243.52752999999996, "coord_origin": "TOPLEFT" }, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 262.86114999999995, "r_x1": 558.20227, "r_y1": 262.86114999999995, "r_x2": 558.20227, "r_y2": 254.48650999999995, "r_x3": 317.95499, "r_y3": 254.48650999999995, "coord_origin": "TOPLEFT" }, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 273.82019, "r_x1": 558.20349, "r_y1": 273.82019, "r_x2": 558.20349, "r_y2": 265.44556, "r_x3": 317.95499, "r_y3": 265.44556, "coord_origin": "TOPLEFT" }, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 284.77917, "r_x1": 540.99426, "r_y1": 284.77917, "r_x2": 540.99426, "r_y2": 276.40454, "r_x3": 317.95499, "r_y3": 276.40454, "coord_origin": "TOPLEFT" }, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 7, "label": "text", "bbox": { "l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9676101207733154, "cells": [ { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 295.73816, "r_x1": 558.43469, "r_y1": 295.73816, "r_x2": 558.43469, "r_y2": 287.36353, "r_x3": 327.918, "r_y3": 287.36353, "coord_origin": "TOPLEFT" }, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 306.69717, "r_x1": 558.20013, "r_y1": 306.69717, "r_x2": 558.20013, "r_y2": 298.32254, "r_x3": 317.95499, "r_y3": 298.32254, "coord_origin": "TOPLEFT" }, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 317.65616000000006, "r_x1": 535.65015, "r_y1": 317.65616000000006, "r_x2": 535.65015, "r_y2": 309.28152, "r_x3": 317.62299, "r_y3": 309.28152, "coord_origin": "TOPLEFT" }, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 11, "label": "section_header", "bbox": { "l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT" }, "confidence": 0.9378752708435059, "cells": [ { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 345.40097, "r_x1": 387.3696, "r_y1": 345.40097, "r_x2": 387.3696, "r_y2": 335.09189, "r_x3": 317.95499, "r_y3": 335.09189, "coord_origin": "TOPLEFT" }, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, { "id": 12, "label": "list_item", "bbox": { "l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT" }, "confidence": 0.9372857213020325, "cells": [ { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 321.198, "r_y0": 355.21588, "r_x1": 329.72415, "r_y1": 355.21588, "r_x2": 329.72415, "r_y2": 348.70233, "r_x3": 321.198, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.53516, "r_y0": 355.21588, "r_x1": 558.19904, "r_y1": 355.21588, "r_x2": 558.19904, "r_y2": 348.70233, "r_x3": 331.53516, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "orig": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 137, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 363.18591, "r_x1": 379.36414, "r_y1": 363.18591, "r_x2": 379.36414, "r_y2": 356.67236, "r_x3": 333.39099, "r_y3": 356.67236, "coord_origin": "TOPLEFT" }, "text": "competition. In", "orig": "competition. 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Before training, we either mapped", "orig": ". Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.25418, "r_x1": 294.04712, "r_y1": 410.25418, "r_x2": 294.04712, "r_y2": 401.87954999999994, "r_x3": 53.79800000000001, "r_y3": 401.87954999999994, "coord_origin": "TOPLEFT" }, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "orig": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.21317, "r_x1": 117.12856, "r_y1": 421.21317, "r_x2": 117.12856, "r_y2": 412.83853, "r_x3": 53.79800000000001, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "also PubLayNet\u2019s", "orig": "also PubLayNet\u2019s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 119.362, "r_y0": 421.22214, "r_x1": 132.4342, "r_y1": 421.22214, "r_x2": 132.4342, "r_y2": 412.88339, "r_x3": 119.362, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 135.177, "r_y0": 421.21317, "r_x1": 142.54416, "r_y1": 421.21317, "r_x2": 142.54416, "r_y2": 412.83853, "r_x3": 135.177, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.77901, "r_y0": 421.22214, "r_x1": 159.66605, "r_y1": 421.22214, "r_x2": 159.66605, "r_y2": 412.88339, "r_x3": 144.77901, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 159.66701, "r_y0": 421.21317, "r_x1": 294.04562, "r_y1": 421.21317, "r_x2": 294.04562, "r_y2": 412.83853, "r_x3": 159.66701, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": ". Note that the different clustering of", "orig": ". Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.17217999999997, "r_x1": 294.04614, "r_y1": 432.17217999999997, "r_x2": 294.04614, "r_y2": 423.79755, "r_x3": 53.79800000000001, "r_y3": 423.79755, "coord_origin": "TOPLEFT" }, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.13116, "r_x1": 106.2066, "r_y1": 443.13116, "r_x2": 106.2066, "r_y2": 434.75653, "r_x3": 53.79800000000001, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 108.448, "r_y0": 443.14014, "r_x1": 123.30533, "r_y1": 443.14014, "r_x2": 123.30533, "r_y2": 434.80139, "r_x3": 108.448, "r_y3": 434.80139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 123.305, "r_y0": 443.13116, "r_x1": 125.27761000000001, "r_y1": 443.13116, "r_x2": 125.27761000000001, "r_y2": 434.75653, "r_x3": 123.305, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ." }, { "label": "text", "id": 1, "page_no": 7, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT" }, "confidence": 0.9868056774139404, "cells": [ { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 454.08917, "r_x1": 294.27582, "r_y1": 454.08917, "r_x2": 294.27582, "r_y2": 445.71453999999994, "r_x3": 63.76100199999999, "r_y3": 445.71453999999994, "coord_origin": "TOPLEFT" }, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.04819, "r_x1": 62.989277, "r_y1": 465.04819, "r_x2": 62.989277, "r_y2": 456.67355, "r_x3": 53.79800000000001, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.852997, "r_y0": 465.05716, "r_x1": 88.947144, "r_y1": 465.05716, "r_x2": 88.947144, "r_y2": 456.71841, "r_x3": 64.852997, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 91.019997, "r_y0": 465.04819, "r_x1": 104.24454, "r_y1": 465.04819, "r_x2": 104.24454, "r_y2": 456.67355, "r_x3": 91.019997, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 106.108, "r_y0": 465.05716, "r_x1": 124.78053, "r_y1": 465.05716, "r_x2": 124.78053, "r_y2": 456.71841, "r_x3": 106.108, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 126.85500000000002, "r_y0": 465.04819, "r_x1": 277.63235, "r_y1": 465.04819, "r_x2": 277.63235, "r_y2": 456.67355, "r_x3": 126.85500000000002, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 279.49701, "r_y0": 465.05716, "r_x1": 294.0484, "r_y1": 465.05716, "r_x2": 294.0484, "r_y2": 456.71841, "r_x3": 279.49701, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 476.00717, "r_x1": 294.04709, "r_y1": 476.00717, "r_x2": 294.04709, "r_y2": 467.63254, "r_x3": 53.79800000000001, "r_y3": 467.63254, "coord_origin": "TOPLEFT" }, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 486.96619, "r_x1": 294.04532, "r_y1": 486.96619, "r_x2": 294.04532, "r_y2": 478.59155, "r_x3": 53.79800000000001, "r_y3": 478.59155, "coord_origin": "TOPLEFT" }, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 497.92517, "r_x1": 294.04538, "r_y1": 497.92517, "r_x2": 294.04538, "r_y2": 489.55054, "r_x3": 53.466999, "r_y3": 489.55054, "coord_origin": "TOPLEFT" }, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 508.88419, "r_x1": 295.55908, "r_y1": 508.88419, "r_x2": 295.55908, "r_y2": 500.50955, "r_x3": 53.528999, "r_y3": 500.50955, "coord_origin": "TOPLEFT" }, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 519.84317, "r_x1": 294.04712, "r_y1": 519.84317, "r_x2": 294.04712, "r_y2": 511.46854, "r_x3": 53.79800000000001, "r_y3": 511.46854, "coord_origin": "TOPLEFT" }, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 530.80219, "r_x1": 294.21179, "r_y1": 530.80219, "r_x2": 294.21179, "r_y2": 522.42755, "r_x3": 53.79800000000001, "r_y3": 522.42755, "coord_origin": "TOPLEFT" }, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 541.76117, "r_x1": 294.04712, "r_y1": 541.76117, "r_x2": 294.04712, "r_y2": 533.38654, "r_x3": 53.79800000000001, "r_y3": 533.38654, "coord_origin": "TOPLEFT" }, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.72017, "r_x1": 294.04715, "r_y1": 552.72017, "r_x2": 294.04715, "r_y2": 544.34555, "r_x3": 53.79800000000001, "r_y3": 544.34555, "coord_origin": "TOPLEFT" }, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.67917, "r_x1": 294.04712, "r_y1": 563.67917, "r_x2": 294.04712, "r_y2": 555.3045500000001, "r_x3": 53.79800000000001, "r_y3": 555.3045500000001, "coord_origin": "TOPLEFT" }, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.63718, "r_x1": 294.21411, "r_y1": 574.63718, "r_x2": 294.21411, "r_y2": 566.26256, "r_x3": 53.79800000000001, "r_y3": 566.26256, "coord_origin": "TOPLEFT" }, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 585.59618, "r_x1": 157.52132, "r_y1": 585.59618, "r_x2": 157.52132, "r_y2": 577.22156, "r_x3": 53.79800000000001, "r_y3": 577.22156, "coord_origin": "TOPLEFT" }, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts." }, { "label": "section_header", "id": 8, "page_no": 7, "cluster": { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT" }, "confidence": 0.9574698805809021, "cells": [ { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 615.37001, "r_x1": 156.00534, "r_y1": 615.37001, "r_x2": 156.00534, "r_y2": 605.06091, "r_x3": 53.79800000000001, "r_y3": 605.06091, "coord_origin": "TOPLEFT" }, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Example Predictions" }, { "label": "text", "id": 2, "page_no": 7, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT" }, "confidence": 0.9859886169433594, "cells": [ { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 628.63817, "r_x1": 295.5571, "r_y1": 628.63817, "r_x2": 295.5571, "r_y2": 620.26355, "r_x3": 53.528999, "r_y3": 620.26355, "coord_origin": "TOPLEFT" }, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.59717, "r_x1": 294.04532, "r_y1": 639.59717, "r_x2": 294.04532, "r_y2": 631.22255, "r_x3": 53.79800000000001, "r_y3": 631.22255, "coord_origin": "TOPLEFT" }, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.5561700000001, "r_x1": 295.55618, "r_y1": 650.5561700000001, "r_x2": 295.55618, "r_y2": 642.18155, "r_x3": 53.79800000000001, "r_y3": 642.18155, "coord_origin": "TOPLEFT" }, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.51517, "r_x1": 294.04541, "r_y1": 661.51517, "r_x2": 294.04541, "r_y2": 653.14055, "r_x3": 53.79800000000001, "r_y3": 653.14055, "coord_origin": "TOPLEFT" }, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.47417, "r_x1": 295.55844, "r_y1": 672.47417, "r_x2": 295.55844, "r_y2": 664.09956, "r_x3": 53.79800000000001, "r_y3": 664.09956, "coord_origin": "TOPLEFT" }, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.43217, "r_x1": 294.04712, "r_y1": 683.43217, "r_x2": 294.04712, "r_y2": 675.05756, "r_x3": 53.79800000000001, "r_y3": 675.05756, "coord_origin": "TOPLEFT" }, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.391174, "r_x1": 294.04535, "r_y1": 694.391174, "r_x2": 294.04535, "r_y2": 686.01656, "r_x3": 53.79800000000001, "r_y3": 686.01656, "coord_origin": "TOPLEFT" }, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.350174, "r_x1": 136.07368, "r_y1": 705.350174, "r_x2": 136.07368, "r_y2": 696.975555, "r_x3": 53.79800000000001, "r_y3": 696.975555, "coord_origin": "TOPLEFT" }, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence." }, { "label": "section_header", "id": 13, "page_no": 7, "cluster": { "id": 13, "label": "section_header", "bbox": { "l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.931998074054718, "cells": [ { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 96.16900999999996, "r_x1": 323.56229, "r_y1": 96.16900999999996, "r_x2": 323.56229, "r_y2": 85.85986000000003, "r_x3": 317.95502, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47137, "r_y0": 96.16900999999996, "r_x1": 405.72961, "r_y1": 96.16900999999996, "r_x2": 405.72961, "r_y2": 85.85986000000003, "r_x3": 334.47137, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "6 CONCLUSION" }, { "label": "text", "id": 3, "page_no": 7, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT" }, "confidence": 0.985493004322052, "cells": [ { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 109.43615999999997, "r_x1": 558.2038, "r_y1": 109.43615999999997, "r_x2": 558.2038, "r_y2": 101.06151999999997, "r_x3": 317.95499, "r_y3": 101.06151999999997, "coord_origin": "TOPLEFT" }, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 120.39520000000005, "r_x1": 558.2041, "r_y1": 120.39520000000005, "r_x2": 558.2041, "r_y2": 112.02057000000002, "r_x3": 317.95499, "r_y3": 112.02057000000002, "coord_origin": "TOPLEFT" }, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 131.35419000000002, "r_x1": 558.43274, "r_y1": 131.35419000000002, "r_x2": 558.43274, "r_y2": 122.97955000000002, "r_x3": 317.95499, "r_y3": 122.97955000000002, "coord_origin": "TOPLEFT" }, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 142.31317, "r_x1": 558.20416, "r_y1": 142.31317, "r_x2": 558.20416, "r_y2": 133.93854, "r_x3": 317.95499, "r_y3": 133.93854, "coord_origin": "TOPLEFT" }, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 153.27215999999999, "r_x1": 558.20422, "r_y1": 153.27215999999999, "r_x2": 558.20422, "r_y2": 144.89752, "r_x3": 317.95499, "r_y3": 144.89752, "coord_origin": "TOPLEFT" }, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 164.23119999999994, "r_x1": 559.71313, "r_y1": 164.23119999999994, "r_x2": 559.71313, "r_y2": 155.85657000000003, "r_x3": 317.95499, "r_y3": 155.85657000000003, "coord_origin": "TOPLEFT" }, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 175.19019000000003, "r_x1": 559.71375, "r_y1": 175.19019000000003, "r_x2": 559.71375, "r_y2": 166.81555000000003, "r_x3": 317.95499, "r_y3": 166.81555000000003, "coord_origin": "TOPLEFT" }, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 186.14917000000003, "r_x1": 540.04382, "r_y1": 186.14917000000003, "r_x2": 540.04382, "r_y2": 177.77454, "r_x3": 317.95499, "r_y3": 177.77454, "coord_origin": "TOPLEFT" }, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect." }, { "label": "text", "id": 6, "page_no": 7, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT" }, "confidence": 0.9800531268119812, "cells": [ { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 197.10815000000002, "r_x1": 558.19836, "r_y1": 197.10815000000002, "r_x2": 558.19836, "r_y2": 188.73352, "r_x3": 327.918, "r_y3": 188.73352, "coord_origin": "TOPLEFT" }, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 208.06719999999996, "r_x1": 558.20404, "r_y1": 208.06719999999996, "r_x2": 558.20404, "r_y2": 199.69257000000005, "r_x3": 317.95499, "r_y3": 199.69257000000005, "coord_origin": "TOPLEFT" }, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 219.02617999999995, "r_x1": 559.71704, "r_y1": 219.02617999999995, "r_x2": 559.71704, "r_y2": 210.65155000000004, "r_x3": 317.686, "r_y3": 210.65155000000004, "coord_origin": "TOPLEFT" }, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 229.98419, "r_x1": 558.20245, "r_y1": 229.98419, "r_x2": 558.20245, "r_y2": 221.60956, "r_x3": 317.95499, "r_y3": 221.60956, "coord_origin": "TOPLEFT" }, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 240.94317999999998, "r_x1": 558.20502, "r_y1": 240.94317999999998, "r_x2": 558.20502, "r_y2": 232.56853999999998, "r_x3": 317.95499, "r_y3": 232.56853999999998, "coord_origin": "TOPLEFT" }, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 251.90215999999998, "r_x1": 559.18408, "r_y1": 251.90215999999998, "r_x2": 559.18408, "r_y2": 243.52752999999996, "r_x3": 317.95499, "r_y3": 243.52752999999996, "coord_origin": "TOPLEFT" }, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 262.86114999999995, "r_x1": 558.20227, "r_y1": 262.86114999999995, "r_x2": 558.20227, "r_y2": 254.48650999999995, "r_x3": 317.95499, "r_y3": 254.48650999999995, "coord_origin": "TOPLEFT" }, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 273.82019, "r_x1": 558.20349, "r_y1": 273.82019, "r_x2": 558.20349, "r_y2": 265.44556, "r_x3": 317.95499, "r_y3": 265.44556, "coord_origin": "TOPLEFT" }, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 284.77917, "r_x1": 540.99426, "r_y1": 284.77917, "r_x2": 540.99426, "r_y2": 276.40454, "r_x3": 317.95499, "r_y3": 276.40454, "coord_origin": "TOPLEFT" }, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust." }, { "label": "text", "id": 7, "page_no": 7, "cluster": { "id": 7, "label": "text", "bbox": { "l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9676101207733154, "cells": [ { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 295.73816, "r_x1": 558.43469, "r_y1": 295.73816, "r_x2": 558.43469, "r_y2": 287.36353, "r_x3": 327.918, "r_y3": 287.36353, "coord_origin": "TOPLEFT" }, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 306.69717, "r_x1": 558.20013, "r_y1": 306.69717, "r_x2": 558.20013, "r_y2": 298.32254, "r_x3": 317.95499, "r_y3": 298.32254, "coord_origin": "TOPLEFT" }, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 317.65616000000006, "r_x1": 535.65015, "r_y1": 317.65616000000006, "r_x2": 535.65015, "r_y2": 309.28152, "r_x3": 317.62299, "r_y3": 309.28152, "coord_origin": "TOPLEFT" }, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap." }, { "label": "section_header", "id": 11, "page_no": 7, "cluster": { "id": 11, "label": "section_header", "bbox": { "l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT" }, "confidence": 0.9378752708435059, "cells": [ { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 345.40097, "r_x1": 387.3696, "r_y1": 345.40097, "r_x2": 387.3696, "r_y2": 335.09189, "r_x3": 317.95499, "r_y3": 335.09189, "coord_origin": "TOPLEFT" }, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "REFERENCES" }, { "label": "list_item", "id": 12, "page_no": 7, "cluster": { "id": 12, "label": "list_item", "bbox": { "l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT" }, "confidence": 0.9372857213020325, "cells": [ { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 321.198, "r_y0": 355.21588, "r_x1": 329.72415, "r_y1": 355.21588, "r_x2": 329.72415, "r_y2": 348.70233, "r_x3": 321.198, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.53516, "r_y0": 355.21588, "r_x1": 558.19904, "r_y1": 355.21588, "r_x2": 558.19904, "r_y2": 348.70233, "r_x3": 331.53516, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Ic-", "orig": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 387.09592, "r_x1": 559.37982, "r_y1": 387.09592, "r_x2": 559.37982, "r_y2": 380.58237, "r_x3": 333.39099, "r_y3": 380.58237, "coord_origin": "TOPLEFT" }, "text": "dar2017 competition on recognition of documents with complex layouts -", "orig": "dar2017 competition on recognition of documents with complex layouts -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 395.06592, "r_x1": 367.4339, "r_y1": 395.06592, "r_x2": 367.4339, "r_y2": 388.55236999999994, "r_x3": 333.39099, "r_y3": 388.55236999999994, "coord_origin": "TOPLEFT" }, "text": "rdcl2017. In", "orig": "rdcl2017. 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IEEE Computer Society, Oct 2017." }, { "label": "list_item", "id": 23, "page_no": 7, "cluster": { "id": 23, "label": "list_item", "bbox": { "l": 317.95499, "t": 675.47636, "r": 558.97156, "b": 705.900894, "coord_origin": "TOPLEFT" }, "confidence": 0.889543354511261, "cells": [ { "index": 209, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 681.9898900000001, "r_x1": 330.11407, "r_y1": 681.9898900000001, "r_x2": 330.11407, "r_y2": 675.47636, "r_x3": 317.95499, "r_y3": 675.47636, "coord_origin": "TOPLEFT" }, "text": "[13]", "orig": "[13]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 210, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.96533, "r_y0": 681.9898900000001, "r_x1": 558.96716, "r_y1": 681.9898900000001, "r_x2": 558.96716, "r_y2": 675.47636, "r_x3": 331.96533, "r_y3": 675.47636, "coord_origin": "TOPLEFT" }, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "orig": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 211, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.18201, "r_y0": 689.95989, "r_x1": 558.96661, "r_y1": 689.95989, "r_x2": 558.96661, "r_y2": 683.44637, "r_x3": 333.18201, "r_y3": 683.44637, "coord_origin": "TOPLEFT" }, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "orig": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 212, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 697.930893, "r_x1": 558.97156, "r_y1": 697.930893, "r_x2": 558.97156, "r_y2": 691.41737, "r_x3": 333.39099, "r_y3": 691.41737, "coord_origin": "TOPLEFT" }, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "orig": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 213, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 705.900894, "r_x1": 558.20001, "r_y1": 705.900894, "r_x2": 558.20001, "r_y2": 699.387367, "r_x3": 333.39099, "r_y3": 699.387367, "coord_origin": "TOPLEFT" }, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "orig": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu" } ], "body": [ { "label": "text", "id": 5, "page_no": 7, "cluster": { "id": 5, "label": "text", "bbox": { "l": 53.501999, "t": 86.87292000000002, "r": 295.64868, "b": 150.14111000000003, "coord_origin": "TOPLEFT" }, "confidence": 0.9824112057685852, "cells": [ { "index": 2, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.501999, "r_y0": 95.34618999999998, "r_x1": 294.32153, "r_y1": 95.34618999999998, "r_x2": 294.32153, "r_y2": 86.87292000000002, "r_x3": 53.501999, "r_y3": 86.87292000000002, "coord_origin": "TOPLEFT" }, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 3, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 106.30517999999995, "r_x1": 295.64868, "r_y1": 106.30517999999995, "r_x2": 295.64868, "r_y2": 97.83191, "r_x3": 53.79800000000001, "r_y3": 97.83191, "coord_origin": "TOPLEFT" }, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 4, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 117.26415999999995, "r_x1": 294.0437, "r_y1": 117.26415999999995, "r_x2": 294.0437, "r_y2": 108.79088999999999, "r_x3": 53.79800000000001, "r_y3": 108.79088999999999, "coord_origin": "TOPLEFT" }, "text": "cLayNet data-sets. 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By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 5, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 128.22313999999994, "r_x1": 294.04373, "r_y1": 128.22313999999994, "r_x2": 294.04373, "r_y2": 119.74987999999996, "r_x3": 53.79800000000001, "r_y3": 119.74987999999996, "coord_origin": "TOPLEFT" }, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 6, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 139.18213000000003, "r_x1": 294.0437, "r_y1": 139.18213000000003, "r_x2": 294.0437, "r_y2": 130.70885999999996, "r_x3": 53.79800000000001, "r_y3": 130.70885999999996, "coord_origin": "TOPLEFT" }, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 7, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 150.14111000000003, "r_x1": 101.15852, "r_y1": 150.14111000000003, "r_x2": 101.15852, "r_y2": 141.66785000000004, "r_x3": 53.79800000000001, "r_y3": 141.66785000000004, "coord_origin": "TOPLEFT" }, "text": "all datasets.", "orig": "all datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. 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Before training, we either mapped", "orig": ". Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 75, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 410.25418, "r_x1": 294.04712, "r_y1": 410.25418, "r_x2": 294.04712, "r_y2": 401.87954999999994, "r_x3": 53.79800000000001, "r_y3": 401.87954999999994, "coord_origin": "TOPLEFT" }, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "orig": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 76, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 421.21317, "r_x1": 117.12856, "r_y1": 421.21317, "r_x2": 117.12856, "r_y2": 412.83853, "r_x3": 53.79800000000001, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "also PubLayNet\u2019s", "orig": "also PubLayNet\u2019s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 77, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 119.362, "r_y0": 421.22214, "r_x1": 132.4342, "r_y1": 421.22214, "r_x2": 132.4342, "r_y2": 412.88339, "r_x3": 119.362, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 78, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 135.177, "r_y0": 421.21317, "r_x1": 142.54416, "r_y1": 421.21317, "r_x2": 142.54416, "r_y2": 412.83853, "r_x3": 135.177, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 79, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 144.77901, "r_y0": 421.22214, "r_x1": 159.66605, "r_y1": 421.22214, "r_x2": 159.66605, "r_y2": 412.88339, "r_x3": 144.77901, "r_y3": 412.88339, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 80, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 159.66701, "r_y0": 421.21317, "r_x1": 294.04562, "r_y1": 421.21317, "r_x2": 294.04562, "r_y2": 412.83853, "r_x3": 159.66701, "r_y3": 412.83853, "coord_origin": "TOPLEFT" }, "text": ". Note that the different clustering of", "orig": ". Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 81, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 432.17217999999997, "r_x1": 294.04614, "r_y1": 432.17217999999997, "r_x2": 294.04614, "r_y2": 423.79755, "r_x3": 53.79800000000001, "r_y3": 423.79755, "coord_origin": "TOPLEFT" }, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 82, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 443.13116, "r_x1": 106.2066, "r_y1": 443.13116, "r_x2": 106.2066, "r_y2": 434.75653, "r_x3": 53.79800000000001, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 83, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 108.448, "r_y0": 443.14014, "r_x1": 123.30533, "r_y1": 443.14014, "r_x2": 123.30533, "r_y2": 434.80139, "r_x3": 108.448, "r_y3": 434.80139, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 84, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 123.305, "r_y0": 443.13116, "r_x1": 125.27761000000001, "r_y1": 443.13116, "r_x2": 125.27761000000001, "r_y2": 434.75653, "r_x3": 123.305, "r_y3": 434.75653, "coord_origin": "TOPLEFT" }, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ." }, { "label": "text", "id": 1, "page_no": 7, "cluster": { "id": 1, "label": "text", "bbox": { "l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT" }, "confidence": 0.9868056774139404, "cells": [ { "index": 85, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 63.76100199999999, "r_y0": 454.08917, "r_x1": 294.27582, "r_y1": 454.08917, "r_x2": 294.27582, "r_y2": 445.71453999999994, "r_x3": 63.76100199999999, "r_y3": 445.71453999999994, "coord_origin": "TOPLEFT" }, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 86, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 465.04819, "r_x1": 62.989277, "r_y1": 465.04819, "r_x2": 62.989277, "r_y2": 456.67355, "r_x3": 53.79800000000001, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 87, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 64.852997, "r_y0": 465.05716, "r_x1": 88.947144, "r_y1": 465.05716, "r_x2": 88.947144, "r_y2": 456.71841, "r_x3": 64.852997, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 88, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 91.019997, "r_y0": 465.04819, "r_x1": 104.24454, "r_y1": 465.04819, "r_x2": 104.24454, "r_y2": 456.67355, "r_x3": 91.019997, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 89, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 106.108, "r_y0": 465.05716, "r_x1": 124.78053, "r_y1": 465.05716, "r_x2": 124.78053, "r_y2": 456.71841, "r_x3": 106.108, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 90, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 126.85500000000002, "r_y0": 465.04819, "r_x1": 277.63235, "r_y1": 465.04819, "r_x2": 277.63235, "r_y2": 456.67355, "r_x3": 126.85500000000002, "r_y3": 456.67355, "coord_origin": "TOPLEFT" }, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 91, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 279.49701, "r_y0": 465.05716, "r_x1": 294.0484, "r_y1": 465.05716, "r_x2": 294.0484, "r_y2": 456.71841, "r_x3": 279.49701, "r_y3": 456.71841, "coord_origin": "TOPLEFT" }, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 92, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 476.00717, "r_x1": 294.04709, "r_y1": 476.00717, "r_x2": 294.04709, "r_y2": 467.63254, "r_x3": 53.79800000000001, "r_y3": 467.63254, "coord_origin": "TOPLEFT" }, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 93, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 486.96619, "r_x1": 294.04532, "r_y1": 486.96619, "r_x2": 294.04532, "r_y2": 478.59155, "r_x3": 53.79800000000001, "r_y3": 478.59155, "coord_origin": "TOPLEFT" }, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 94, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.466999, "r_y0": 497.92517, "r_x1": 294.04538, "r_y1": 497.92517, "r_x2": 294.04538, "r_y2": 489.55054, "r_x3": 53.466999, "r_y3": 489.55054, "coord_origin": "TOPLEFT" }, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 95, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 508.88419, "r_x1": 295.55908, "r_y1": 508.88419, "r_x2": 295.55908, "r_y2": 500.50955, "r_x3": 53.528999, "r_y3": 500.50955, "coord_origin": "TOPLEFT" }, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 96, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 519.84317, "r_x1": 294.04712, "r_y1": 519.84317, "r_x2": 294.04712, "r_y2": 511.46854, "r_x3": 53.79800000000001, "r_y3": 511.46854, "coord_origin": "TOPLEFT" }, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 97, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 530.80219, "r_x1": 294.21179, "r_y1": 530.80219, "r_x2": 294.21179, "r_y2": 522.42755, "r_x3": 53.79800000000001, "r_y3": 522.42755, "coord_origin": "TOPLEFT" }, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 98, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 541.76117, "r_x1": 294.04712, "r_y1": 541.76117, "r_x2": 294.04712, "r_y2": 533.38654, "r_x3": 53.79800000000001, "r_y3": 533.38654, "coord_origin": "TOPLEFT" }, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 99, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 552.72017, "r_x1": 294.04715, "r_y1": 552.72017, "r_x2": 294.04715, "r_y2": 544.34555, "r_x3": 53.79800000000001, "r_y3": 544.34555, "coord_origin": "TOPLEFT" }, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 100, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 563.67917, "r_x1": 294.04712, "r_y1": 563.67917, "r_x2": 294.04712, "r_y2": 555.3045500000001, "r_x3": 53.79800000000001, "r_y3": 555.3045500000001, "coord_origin": "TOPLEFT" }, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 101, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 574.63718, "r_x1": 294.21411, "r_y1": 574.63718, "r_x2": 294.21411, "r_y2": 566.26256, "r_x3": 53.79800000000001, "r_y3": 566.26256, "coord_origin": "TOPLEFT" }, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 102, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 585.59618, "r_x1": 157.52132, "r_y1": 585.59618, "r_x2": 157.52132, "r_y2": 577.22156, "r_x3": 53.79800000000001, "r_y3": 577.22156, "coord_origin": "TOPLEFT" }, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts." }, { "label": "section_header", "id": 8, "page_no": 7, "cluster": { "id": 8, "label": "section_header", "bbox": { "l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT" }, "confidence": 0.9574698805809021, "cells": [ { "index": 103, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 615.37001, "r_x1": 156.00534, "r_y1": 615.37001, "r_x2": 156.00534, "r_y2": 605.06091, "r_x3": 53.79800000000001, "r_y3": 605.06091, "coord_origin": "TOPLEFT" }, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Example Predictions" }, { "label": "text", "id": 2, "page_no": 7, "cluster": { "id": 2, "label": "text", "bbox": { "l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT" }, "confidence": 0.9859886169433594, "cells": [ { "index": 104, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.528999, "r_y0": 628.63817, "r_x1": 295.5571, "r_y1": 628.63817, "r_x2": 295.5571, "r_y2": 620.26355, "r_x3": 53.528999, "r_y3": 620.26355, "coord_origin": "TOPLEFT" }, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 105, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 639.59717, "r_x1": 294.04532, "r_y1": 639.59717, "r_x2": 294.04532, "r_y2": 631.22255, "r_x3": 53.79800000000001, "r_y3": 631.22255, "coord_origin": "TOPLEFT" }, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 106, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 650.5561700000001, "r_x1": 295.55618, "r_y1": 650.5561700000001, "r_x2": 295.55618, "r_y2": 642.18155, "r_x3": 53.79800000000001, "r_y3": 642.18155, "coord_origin": "TOPLEFT" }, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 107, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 661.51517, "r_x1": 294.04541, "r_y1": 661.51517, "r_x2": 294.04541, "r_y2": 653.14055, "r_x3": 53.79800000000001, "r_y3": 653.14055, "coord_origin": "TOPLEFT" }, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 108, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 672.47417, "r_x1": 295.55844, "r_y1": 672.47417, "r_x2": 295.55844, "r_y2": 664.09956, "r_x3": 53.79800000000001, "r_y3": 664.09956, "coord_origin": "TOPLEFT" }, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 109, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 683.43217, "r_x1": 294.04712, "r_y1": 683.43217, "r_x2": 294.04712, "r_y2": 675.05756, "r_x3": 53.79800000000001, "r_y3": 675.05756, "coord_origin": "TOPLEFT" }, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 110, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 694.391174, "r_x1": 294.04535, "r_y1": 694.391174, "r_x2": 294.04535, "r_y2": 686.01656, "r_x3": 53.79800000000001, "r_y3": 686.01656, "coord_origin": "TOPLEFT" }, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 111, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 705.350174, "r_x1": 136.07368, "r_y1": 705.350174, "r_x2": 136.07368, "r_y2": 696.975555, "r_x3": 53.79800000000001, "r_y3": 696.975555, "coord_origin": "TOPLEFT" }, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence." }, { "label": "section_header", "id": 13, "page_no": 7, "cluster": { "id": 13, "label": "section_header", "bbox": { "l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT" }, "confidence": 0.931998074054718, "cells": [ { "index": 112, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95502, "r_y0": 96.16900999999996, "r_x1": 323.56229, "r_y1": 96.16900999999996, "r_x2": 323.56229, "r_y2": 85.85986000000003, "r_x3": 317.95502, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 113, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 334.47137, "r_y0": 96.16900999999996, "r_x1": 405.72961, "r_y1": 96.16900999999996, "r_x2": 405.72961, "r_y2": 85.85986000000003, "r_x3": 334.47137, "r_y3": 85.85986000000003, "coord_origin": "TOPLEFT" }, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "6 CONCLUSION" }, { "label": "text", "id": 3, "page_no": 7, "cluster": { "id": 3, "label": "text", "bbox": { "l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT" }, "confidence": 0.985493004322052, "cells": [ { "index": 114, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 109.43615999999997, "r_x1": 558.2038, "r_y1": 109.43615999999997, "r_x2": 558.2038, "r_y2": 101.06151999999997, "r_x3": 317.95499, "r_y3": 101.06151999999997, "coord_origin": "TOPLEFT" }, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 115, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 120.39520000000005, "r_x1": 558.2041, "r_y1": 120.39520000000005, "r_x2": 558.2041, "r_y2": 112.02057000000002, "r_x3": 317.95499, "r_y3": 112.02057000000002, "coord_origin": "TOPLEFT" }, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 116, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 131.35419000000002, "r_x1": 558.43274, "r_y1": 131.35419000000002, "r_x2": 558.43274, "r_y2": 122.97955000000002, "r_x3": 317.95499, "r_y3": 122.97955000000002, "coord_origin": "TOPLEFT" }, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 117, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 142.31317, "r_x1": 558.20416, "r_y1": 142.31317, "r_x2": 558.20416, "r_y2": 133.93854, "r_x3": 317.95499, "r_y3": 133.93854, "coord_origin": "TOPLEFT" }, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 118, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 153.27215999999999, "r_x1": 558.20422, "r_y1": 153.27215999999999, "r_x2": 558.20422, "r_y2": 144.89752, "r_x3": 317.95499, "r_y3": 144.89752, "coord_origin": "TOPLEFT" }, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 119, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 164.23119999999994, "r_x1": 559.71313, "r_y1": 164.23119999999994, "r_x2": 559.71313, "r_y2": 155.85657000000003, "r_x3": 317.95499, "r_y3": 155.85657000000003, "coord_origin": "TOPLEFT" }, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 120, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 175.19019000000003, "r_x1": 559.71375, "r_y1": 175.19019000000003, "r_x2": 559.71375, "r_y2": 166.81555000000003, "r_x3": 317.95499, "r_y3": 166.81555000000003, "coord_origin": "TOPLEFT" }, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 121, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 186.14917000000003, "r_x1": 540.04382, "r_y1": 186.14917000000003, "r_x2": 540.04382, "r_y2": 177.77454, "r_x3": 317.95499, "r_y3": 177.77454, "coord_origin": "TOPLEFT" }, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect." }, { "label": "text", "id": 6, "page_no": 7, "cluster": { "id": 6, "label": "text", "bbox": { "l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT" }, "confidence": 0.9800531268119812, "cells": [ { "index": 122, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 197.10815000000002, "r_x1": 558.19836, "r_y1": 197.10815000000002, "r_x2": 558.19836, "r_y2": 188.73352, "r_x3": 327.918, "r_y3": 188.73352, "coord_origin": "TOPLEFT" }, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 123, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 208.06719999999996, "r_x1": 558.20404, "r_y1": 208.06719999999996, "r_x2": 558.20404, "r_y2": 199.69257000000005, "r_x3": 317.95499, "r_y3": 199.69257000000005, "coord_origin": "TOPLEFT" }, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 124, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.686, "r_y0": 219.02617999999995, "r_x1": 559.71704, "r_y1": 219.02617999999995, "r_x2": 559.71704, "r_y2": 210.65155000000004, "r_x3": 317.686, "r_y3": 210.65155000000004, "coord_origin": "TOPLEFT" }, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 125, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 229.98419, "r_x1": 558.20245, "r_y1": 229.98419, "r_x2": 558.20245, "r_y2": 221.60956, "r_x3": 317.95499, "r_y3": 221.60956, "coord_origin": "TOPLEFT" }, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 126, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 240.94317999999998, "r_x1": 558.20502, "r_y1": 240.94317999999998, "r_x2": 558.20502, "r_y2": 232.56853999999998, "r_x3": 317.95499, "r_y3": 232.56853999999998, "coord_origin": "TOPLEFT" }, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 127, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 251.90215999999998, "r_x1": 559.18408, "r_y1": 251.90215999999998, "r_x2": 559.18408, "r_y2": 243.52752999999996, "r_x3": 317.95499, "r_y3": 243.52752999999996, "coord_origin": "TOPLEFT" }, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 128, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 262.86114999999995, "r_x1": 558.20227, "r_y1": 262.86114999999995, "r_x2": 558.20227, "r_y2": 254.48650999999995, "r_x3": 317.95499, "r_y3": 254.48650999999995, "coord_origin": "TOPLEFT" }, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 129, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 273.82019, "r_x1": 558.20349, "r_y1": 273.82019, "r_x2": 558.20349, "r_y2": 265.44556, "r_x3": 317.95499, "r_y3": 265.44556, "coord_origin": "TOPLEFT" }, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 130, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 284.77917, "r_x1": 540.99426, "r_y1": 284.77917, "r_x2": 540.99426, "r_y2": 276.40454, "r_x3": 317.95499, "r_y3": 276.40454, "coord_origin": "TOPLEFT" }, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust." }, { "label": "text", "id": 7, "page_no": 7, "cluster": { "id": 7, "label": "text", "bbox": { "l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT" }, "confidence": 0.9676101207733154, "cells": [ { "index": 131, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 327.918, "r_y0": 295.73816, "r_x1": 558.43469, "r_y1": 295.73816, "r_x2": 558.43469, "r_y2": 287.36353, "r_x3": 327.918, "r_y3": 287.36353, "coord_origin": "TOPLEFT" }, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 132, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 306.69717, "r_x1": 558.20013, "r_y1": 306.69717, "r_x2": 558.20013, "r_y2": 298.32254, "r_x3": 317.95499, "r_y3": 298.32254, "coord_origin": "TOPLEFT" }, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 133, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.62299, "r_y0": 317.65616000000006, "r_x1": 535.65015, "r_y1": 317.65616000000006, "r_x2": 535.65015, "r_y2": 309.28152, "r_x3": 317.62299, "r_y3": 309.28152, "coord_origin": "TOPLEFT" }, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap." }, { "label": "section_header", "id": 11, "page_no": 7, "cluster": { "id": 11, "label": "section_header", "bbox": { "l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT" }, "confidence": 0.9378752708435059, "cells": [ { "index": 134, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 317.95499, "r_y0": 345.40097, "r_x1": 387.3696, "r_y1": 345.40097, "r_x2": 387.3696, "r_y2": 335.09189, "r_x3": 317.95499, "r_y3": 335.09189, "coord_origin": "TOPLEFT" }, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "REFERENCES" }, { "label": "list_item", "id": 12, "page_no": 7, "cluster": { "id": 12, "label": "list_item", "bbox": { "l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT" }, "confidence": 0.9372857213020325, "cells": [ { "index": 135, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 321.198, "r_y0": 355.21588, "r_x1": 329.72415, "r_y1": 355.21588, "r_x2": 329.72415, "r_y2": 348.70233, "r_x3": 321.198, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 136, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 331.53516, "r_y0": 355.21588, "r_x1": 558.19904, "r_y1": 355.21588, "r_x2": 558.19904, "r_y2": 348.70233, "r_x3": 331.53516, "r_y3": 348.70233, "coord_origin": "TOPLEFT" }, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Ic-", "orig": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Ic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 143, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 387.09592, "r_x1": 559.37982, "r_y1": 387.09592, "r_x2": 559.37982, "r_y2": 380.58237, "r_x3": 333.39099, "r_y3": 380.58237, "coord_origin": "TOPLEFT" }, "text": "dar2017 competition on recognition of documents with complex layouts -", "orig": "dar2017 competition on recognition of documents with complex layouts -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 144, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 333.39099, "r_y0": 395.06592, "r_x1": 367.4339, "r_y1": 395.06592, "r_x2": 367.4339, "r_y2": 388.55236999999994, "r_x3": 333.39099, "r_y3": 388.55236999999994, "coord_origin": "TOPLEFT" }, "text": "rdcl2017. In", "orig": "rdcl2017. 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(A, D) exhibit favourable results on", "orig": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 483.91425, "r_x1": 559.80786, "r_y1": 483.91425, "r_x2": 559.80786, "r_y2": 475.44101, "r_x3": 53.79800000000001, "r_y3": 475.44101, "coord_origin": "TOPLEFT" }, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "orig": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 494.87323, "r_x1": 558.20294, "r_y1": 494.87323, "r_x2": 558.20294, "r_y2": 486.39999, "r_x3": 53.79800000000001, "r_y3": 486.39999, "coord_origin": "TOPLEFT" }, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "orig": "strates good table and figure distinction. 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(A, D) exhibit favourable results on", "orig": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 24, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 483.91425, "r_x1": 559.80786, "r_y1": 483.91425, "r_x2": 559.80786, "r_y2": 475.44101, "r_x3": 53.79800000000001, "r_y3": 475.44101, "coord_origin": "TOPLEFT" }, "text": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "orig": "coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 25, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 494.87323, "r_x1": 558.20294, "r_y1": 494.87323, "r_x2": 558.20294, "r_y2": 486.39999, "r_x3": 53.79800000000001, "r_y3": 486.39999, "coord_origin": "TOPLEFT" }, "text": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "orig": "strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 26, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 505.83124, "r_x1": 130.37105, "r_y1": 505.83124, "r_x2": 130.37105, "r_y2": 497.358, "r_x3": 53.79800000000001, "r_y3": 497.358, "coord_origin": "TOPLEFT" }, "text": "and missing boxes.", "orig": "and missing boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes." }, { "label": "text", "id": 8, "page_no": 8, "cluster": { "id": 8, "label": "text", "bbox": { "l": 69.234001, "t": 527.06635, "r": 295.22406, "b": 549.5199, "coord_origin": "TOPLEFT" }, "confidence": 0.8445900082588196, "cells": [ { "index": 27, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 69.234001, "r_y0": 533.5799, "r_x1": 295.22406, "r_y1": 533.5799, "r_x2": 295.22406, "r_y2": 527.06635, "r_x3": 69.234001, "r_y3": 527.06635, "coord_origin": "TOPLEFT" }, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "orig": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 28, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 69.234001, "r_y0": 541.5499, "r_x1": 294.30612, "r_y1": 541.5499, "r_x2": 294.30612, "r_y2": 535.03638, "r_x3": 69.234001, "r_y3": 535.03638, "coord_origin": "TOPLEFT" }, "text": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "orig": "tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 29, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 69.234001, "r_y0": 549.5199, "r_x1": 190.45259, "r_y1": 549.5199, "r_x2": 190.45259, "r_y2": 543.00638, "r_x3": 69.234001, "r_y3": 543.00638, "coord_origin": "TOPLEFT" }, "text": "export, opencv dnn support, October 2021.", "orig": "export, opencv dnn support, October 2021.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false } ], "children": [] }, "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021." }, { "label": "list_item", "id": 7, "page_no": 8, "cluster": { "id": 7, "label": "list_item", "bbox": { "l": 53.79800000000001, "t": 550.97638, "r": 295.12177, "b": 573.43686, "coord_origin": "TOPLEFT" }, "confidence": 0.8561117053031921, "cells": [ { "index": 30, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 53.79800000000001, "r_y0": 557.4899, "r_x1": 65.286942, "r_y1": 557.4899, "r_x2": 65.286942, "r_y2": 550.97638, "r_x3": 53.79800000000001, "r_y3": 550.97638, "coord_origin": "TOPLEFT" }, "text": "[14]", "orig": "[14]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 31, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 67.036171, "r_y0": 557.4899, "r_x1": 294.17709, "r_y1": 557.4899, "r_x2": 294.17709, "r_y2": 550.97638, "r_x3": 67.036171, "r_y3": 550.97638, "coord_origin": "TOPLEFT" }, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false }, { "index": 32, "rgba": { "r": 0, "g": 0, "b": 0, "a": 255 }, "rect": { "r_x0": 69.234001, "r_y0": 565.4599000000001, "r_x1": 295.12177, "r_y1": 565.4599000000001, "r_x2": 295.12177, "r_y2": 558.94638, "r_x3": 69.234001, "r_y3": 558.94638, "coord_origin": "TOPLEFT" }, "text": "Kirillov, and Sergey Zagoruyko. 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