diff --git a/docling/cli/main.py b/docling/cli/main.py index c87e311..6830c7f 100644 --- a/docling/cli/main.py +++ b/docling/cli/main.py @@ -154,6 +154,7 @@ def export_documents( output_dir: Path, export_json: bool, export_html: bool, + export_html_split_page: bool, export_md: bool, export_txt: bool, export_doctags: bool, @@ -181,7 +182,15 @@ def export_documents( fname = output_dir / f"{doc_filename}.html" _log.info(f"writing HTML output to {fname}") conv_res.document.save_as_html( - filename=fname, image_mode=image_export_mode + filename=fname, image_mode=image_export_mode, split_page_view=False + ) + + # Export HTML format: + if export_html_split_page: + fname = output_dir / f"{doc_filename}.html" + _log.info(f"writing HTML output to {fname}") + conv_res.document.save_as_html( + filename=fname, image_mode=image_export_mode, split_page_view=True ) # Export Text format: @@ -472,6 +481,7 @@ def convert( export_json = OutputFormat.JSON in to_formats export_html = OutputFormat.HTML in to_formats + export_html_split_page = OutputFormat.HTML_SPLIT_PAGE in to_formats export_md = OutputFormat.MARKDOWN in to_formats export_txt = OutputFormat.TEXT in to_formats export_doctags = OutputFormat.DOCTAGS in to_formats @@ -585,6 +595,7 @@ def convert( output_dir=output, export_json=export_json, export_html=export_html, + export_html_split_page=export_html_split_page, export_md=export_md, export_txt=export_txt, export_doctags=export_doctags, diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py index 7dcf89c..8ee53d6 100644 --- a/docling/datamodel/base_models.py +++ b/docling/datamodel/base_models.py @@ -50,6 +50,7 @@ class OutputFormat(str, Enum): MARKDOWN = "md" JSON = "json" HTML = "html" + HTML_SPLIT_PAGE = "html_split_page" TEXT = "text" DOCTAGS = "doctags" diff --git a/docs/examples/export_tables.py b/docs/examples/export_tables.py index 2985127..8f09292 100644 --- a/docs/examples/export_tables.py +++ b/docs/examples/export_tables.py @@ -40,7 +40,7 @@ def main(): element_html_filename = output_dir / f"{doc_filename}-table-{table_ix+1}.html" _log.info(f"Saving HTML table to {element_html_filename}") with element_html_filename.open("w") as fp: - fp.write(table.export_to_html()) + fp.write(table.export_to_html(doc=conv_res.document)) end_time = time.time() - start_time diff --git a/poetry.lock b/poetry.lock index 1ac246f..6c0017a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -870,13 +870,13 @@ files = [ [[package]] name = "docling-core" -version = "2.25.0" +version = "2.26.2" description = "A python library to define and validate data types in Docling." optional = false python-versions = "<4.0,>=3.9" files = [ - {file = "docling_core-2.25.0-py3-none-any.whl", hash = "sha256:24fe431005518df8e554b69c33178bca903bcf91c230cdcd31a905369f53a461"}, - {file = "docling_core-2.25.0.tar.gz", hash = "sha256:a2019392592840b2829082ef0c1d1a9096fb3512ae44c3a93dc04a5eaef81b2f"}, + {file = "docling_core-2.26.2-py3-none-any.whl", hash = "sha256:3f35627352e2311676af2e484646b9ccd1ecb3012f8a87a127692c1d862ea9f5"}, + {file = "docling_core-2.26.2.tar.gz", hash = "sha256:6d080acf5d37012ee6f4e47bc103049091480d9de708bd2cb899c2ac923f5616"}, ] [package.dependencies] @@ -7859,4 +7859,4 @@ vlm = ["accelerate", "transformers", "transformers"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "2c4da8d08fa1f95af87348179c20747e85749df670dea38f18f586c4397a358d" +content-hash = "a9830e60f15bd80e7776c5dbba86dd477ae62408e55406c3f91a127bc553e173" diff --git a/pyproject.toml b/pyproject.toml index ee3e067..abd0365 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,7 +46,7 @@ packages = [{ include = "docling" }] ###################### python = "^3.9" pydantic = "^2.0.0" -docling-core = {extras = ["chunking"], version = "^2.24.1"} +docling-core = {version = "^2.26.0", extras = ["chunking"]} docling-ibm-models = "^3.4.0" docling-parse = "^4.0.0" filetype = "^1.2.0" diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json index e3f173b..6f1d389 100644 --- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json +++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json @@ -1 +1 @@ -{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "2305.03393v1-pg9.pdf", "filename-prov": null, "document-hash": "1a36870a3e6aa062b563b50c1eaed40685b651ee03e0538453de65e7013b742f", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "8a5a8d9a1ae6cbd1dcedcad02ed10195aa71d1ac3e4d56be4ab72c858d7f543e", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [134.765, 639.09302, 480.59665, 675.53699], "page": 1, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 612.79181, 318.45145, 625.29486], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 536.57599, 480.59567, 608.88495], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.765, 464.01782, 480.59890999999993, 519.20526], "page": 1, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 273.82581000000005, 264.40829, 286.32889], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 173.70000000000005, 480.72003, 269.91995], "page": 1, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 125.88, 480.59857000000005, 174.27795000000003], "page": 1, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [{"prov": [{"bbox": [139.6674041748047, 322.5054626464844, 475.00927734375, 454.4546203613281], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 6, "data": [[{"bbox": [144.592, 339.45749, 183.82895, 363.69717], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.19501, 339.45749, 231.42303, 363.69717], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.79799999999997, 344.93649, 278.3338, 356.22519000000005], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": 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1}]}, "main-text": [{"prov": [{"bbox": [134.765, 639.09302, 480.59665, 675.53699], "page": 1, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 612.79181, 318.45145, 625.29486], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 536.57599, 480.59567, 608.88495], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.765, 464.01782, 480.59890999999993, 519.20526], "page": 1, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 273.82581000000005, 264.40829, 286.32889], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 173.70000000000005, 480.72003, 269.91995], "page": 1, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 125.88, 480.59857000000005, 174.27795000000003], "page": 1, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [{"prov": [{"bbox": [139.66741943359375, 322.5054626464844, 475.00927734375, 454.45458984375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 6, "data": [[{"bbox": [144.592, 339.45749, 183.82895, 363.69717], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.19501, 339.45749, 231.42303, 363.69717], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.79799999999997, 344.93649, 278.3338, 356.22519000000005], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "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": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "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": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "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": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "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": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "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": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "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": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller 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"r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "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": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "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": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "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": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "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": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller 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"coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "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": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "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": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533725738525, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 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"r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533725738525, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt index 9f02379..debd722 100644 --- a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt +++ b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt @@ -106,10 +106,10 @@ Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. ModelSimpleTEDS ComplexAllTabula78.057.867.9Traprange60.849.955.4Camelot80.066.073.0Acrobat Pro68.961.865.3EDD91.285.488.3TableFormer95.490.193.6Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. 7 - a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells + Japanese language (previously unseen by TableFormer): Example table from FinTabNet: @@ -127,7 +127,6 @@ [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to- 8 - end object detection with transformers. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020 , pages 213-229, Cham, 2020. Springer International Publishing. 5 [2] Zewen Chi, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao. Complicated table structure recognition. arXiv preprint arXiv:1908.04729 , 2019. 3 [3] Bertrand Couasnon and Aurelie Lemaitre. Recognition of Tables and Forms , pages 647-677. Springer London, London, 2014. 2 @@ -154,6 +153,7 @@ [24] Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. Rethinking table recognition using graph neural networks. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 142-147. IEEE, 2019. 3 [25] Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF Conference on + 9 Computer Vision and Pattern Recognition , pages 658-666, 2019. 6 diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json index 4e3cb2c..536513c 100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json +++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.478, "t": 700.50647, "r": 447.54476999999997, "b": 689.21777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.98441, "t": 700.50647, "r": 480.59314, "b": 689.21777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 675.53699, "r": 480.59665, "b": 639.09302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 625.29486, "r": 318.45145, "b": 612.79181, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 608.88495, "r": 480.59567, "b": 536.57599, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 519.20526, "r": 480.59890999999993, "b": 464.01782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 286.32889, "r": 264.40829, "b": 273.82581000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 269.91995, "r": 480.72003, "b": 173.70000000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 174.27795000000003, "r": 480.59857000000005, "b": 125.88, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.6674041748047, "t": 454.4546203613281, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.14801, "t": 350.41647, "r": 470.76955999999996, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 352.40848, "r": 312.32812, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 352.40848, "r": 353.71539, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 352.40848, "r": 379.02914, "b": 363.69717, "coord_origin": "TOPLEFT"}, 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"start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.90601, "t": 450.14248999999995, "r": 166.51474, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 444.66248, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.71701, "t": 444.66248, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 352.40848, "r": 312.32812, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 352.40848, "r": 353.71539, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 352.40848, "r": 379.02914, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.14801, "t": 350.41647, "r": 470.76955999999996, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.90601, "t": 371.23849, "r": 166.51474, "b": 382.52719, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 371.23849, "r": 214.11774, "b": 382.52719, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 365.75848, "r": 272.94495, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 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"filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.478, "t": 700.50647, "r": 447.54476999999997, "b": 689.21777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.98441, "t": 700.50647, "r": 480.59314, "b": 689.21777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 675.53699, "r": 480.59665, "b": 639.09302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 625.29486, "r": 318.45145, "b": 612.79181, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 608.88495, "r": 480.59567, "b": 536.57599, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 519.20526, "r": 480.59890999999993, "b": 464.01782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 286.32889, "r": 264.40829, "b": 273.82581000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 269.91995, "r": 480.72003, "b": 173.70000000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 174.27795000000003, "r": 480.59857000000005, "b": 125.88, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.66741943359375, "t": 454.45458984375, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 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"end_row_offset_idx": 4, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.853 0.843", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 391.99771, "r": 458.38336, "b": 416.30017, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.97 3.77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.90601, "t": 423.84048, "r": 166.51474, "b": 435.12918, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 423.84048, "r": 214.11774, "b": 435.12918, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, 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"start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.08801, "t": 418.3614799999999, "r": 386.24799, "b": 442.65497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.915 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 418.29871, "r": 418.77798, "b": 442.60117, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.859 0.834", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 418.29871, "r": 458.38336, "b": 442.60117, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.90601, "t": 450.14248999999995, "r": 166.51474, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 444.66248, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.71701, "t": 444.66248, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 144.592, "t": 339.45749, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.19501, "t": 339.45749, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 352.40848, "r": 312.32812, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 352.40848, "r": 353.71539, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 352.40848, "r": 379.02914, "b": 363.69717, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.14801, "t": 350.41647, "r": 470.76955999999996, "b": 361.70517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.90601, "t": 371.23849, "r": 166.51474, "b": 382.52719, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 371.23849, "r": 214.11774, "b": 382.52719, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.17598999999998, "t": 365.75848, "r": 272.94495, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 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100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json +++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "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": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "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": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "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": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "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": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "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": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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"from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "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": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller 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"r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "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": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "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": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "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": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "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": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "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": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "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": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "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": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "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": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533725738525, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 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"r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.99597000000006, "r_x1": 480.59067, "r_y1": 128.99597000000006, "r_x2": 480.59067, "r_y2": 116.46301000000005, "r_x3": 134.765, "r_y3": 116.46301000000005, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "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": 134.765, "r_y0": 140.95099000000005, "r_x1": 480.59665, "r_y1": 140.95099000000005, "r_x2": 480.59665, "r_y2": 128.41803000000004, "r_x3": 134.765, "r_y3": 128.41803000000004, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "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": 134.765, "r_y0": 152.90697999999998, "r_x1": 210.78761, "r_y1": 152.90697999999998, "r_x2": 210.78761, "r_y2": 140.37401999999997, "r_x3": 134.765, "r_y3": 140.37401999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.20818999999995, "r_x1": 149.40306, "r_y1": 179.20818999999995, "r_x2": 149.40306, "r_y2": 166.70514000000003, "r_x3": 134.765, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "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": 160.85905, "r_y0": 179.20818999999995, "r_x1": 318.45145, "r_y1": 179.20818999999995, "r_x2": 318.45145, "r_y2": 166.70514000000003, "r_x3": 160.85905, "r_y3": 166.70514000000003, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 195.64801, "r_x1": 479.74982000000006, "r_y1": 195.64801, "r_x2": 479.74982000000006, "r_y2": 183.11505, "r_x3": 134.765, "r_y3": 183.11505, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "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": 134.765, "r_y0": 207.60303, "r_x1": 480.58765, "r_y1": 207.60303, "r_x2": 480.58765, "r_y2": 195.07007, "r_x3": 134.765, "r_y3": 195.07007, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "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": 134.765, "r_y0": 219.55798000000004, "r_x1": 480.58859000000007, "r_y1": 219.55798000000004, "r_x2": 480.58859000000007, "r_y2": 207.02502000000004, "r_x3": 134.765, "r_y3": 207.02502000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "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": 134.765, "r_y0": 231.51300000000003, "r_x1": 480.59567, "r_y1": 231.51300000000003, "r_x2": 480.59567, "r_y2": 218.98004000000003, "r_x3": 134.765, "r_y3": 218.98004000000003, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "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": 134.765, "r_y0": 243.46802000000002, "r_x1": 440.9425, "r_y1": 243.46802000000002, "r_x2": 440.9425, "r_y2": 230.93506000000002, "r_x3": 134.765, "r_y3": 230.93506000000002, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "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": 444.86800999999997, "r_y0": 243.46802000000002, "r_x1": 480.58792, "r_y1": 243.46802000000002, "r_x2": 480.58792, "r_y2": 230.98486000000003, "r_x3": 444.86800999999997, "r_y3": 230.98486000000003, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "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": 134.765, "r_y0": 255.42400999999995, "r_x1": 145.19585, "r_y1": 255.42400999999995, "r_x2": 145.19585, "r_y2": 242.94086000000004, "r_x3": 134.765, "r_y3": 242.94086000000004, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "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": 149.149, "r_y0": 255.42400999999995, "r_x1": 311.22256, "r_y1": 255.42400999999995, "r_x2": 311.22256, "r_y2": 242.89104999999995, "r_x3": 149.149, "r_y3": 242.89104999999995, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 284.1999799999999, "r_x1": 159.22983, "r_y1": 284.1999799999999, "r_x2": 159.22983, "r_y2": 272.79474000000005, "r_x3": 134.765, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "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": 167.34442, "r_y0": 284.1999799999999, "r_x1": 174.71301, "r_y1": 284.1999799999999, "r_x2": 174.71301, "r_y2": 272.79474000000005, "r_x3": 167.34442, "r_y3": 272.79474000000005, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "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": 188.133, "r_y0": 284.14618, "r_x1": 480.58101999999997, "r_y1": 284.14618, "r_x2": 480.58101999999997, "r_y2": 272.85748, "r_x3": 188.133, "r_y3": 272.85748, "coord_origin": "TOPLEFT"}, "text": "HPO performed in OTSL and HTML representation on the same", "orig": "HPO performed in OTSL and HTML representation on the same", "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": 134.765, "r_y0": 295.10516000000007, "r_x1": 480.59890999999993, "r_y1": 295.10516000000007, "r_x2": 480.59890999999993, "r_y2": 283.81647, "r_x3": 134.765, "r_y3": 283.81647, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "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": 134.765, "r_y0": 306.06418, "r_x1": 480.59887999999995, "r_y1": 306.06418, "r_x2": 480.59887999999995, "r_y2": 294.77547999999996, "r_x3": 134.765, "r_y3": 294.77547999999996, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "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": 134.765, "r_y0": 317.02316, "r_x1": 480.59180000000003, "r_y1": 317.02316, "r_x2": 480.59180000000003, "r_y2": 305.73447, "r_x3": 134.765, "r_y3": 305.73447, "coord_origin": "TOPLEFT"}, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "orig": "smaller models trained on OTSL perform better, especially in recognizing complex", "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": 134.765, "r_y0": 327.98218, "r_x1": 480.58471999999995, "r_y1": 327.98218, "r_x2": 480.58471999999995, "r_y2": 316.69348, "r_x3": 134.765, "r_y3": 316.69348, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.74619, "r_x1": 168.04523, "r_y1": 350.74619, "r_x2": 168.04523, "r_y2": 339.45749, "r_x3": 160.37, "r_y3": 339.45749, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "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": 144.592, "r_y0": 363.69717, "r_x1": 183.82895, "r_y1": 363.69717, "r_x2": 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"r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 518.17419, "r_x1": 149.40306, "r_y1": 518.17419, "r_x2": 149.40306, "r_y2": 505.67111, "r_x3": 134.765, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "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": 160.85905, "r_y0": 518.17419, "r_x1": 264.40829, "r_y1": 518.17419, "r_x2": 264.40829, "r_y2": 505.67111, "r_x3": 160.85905, "r_y3": 505.67111, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.61301, "r_x1": 479.72983, "r_y1": 534.61301, "r_x2": 479.72983, "r_y2": 522.08005, "r_x3": 134.765, "r_y3": 522.08005, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "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": 134.765, "r_y0": 546.569, "r_x1": 480.5897499999999, "r_y1": 546.569, "r_x2": 480.5897499999999, "r_y2": 534.03604, "r_x3": 134.765, "r_y3": 534.03604, "coord_origin": "TOPLEFT"}, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "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": 134.765, "r_y0": 558.524, "r_x1": 480.72003, "r_y1": 558.524, "r_x2": 480.72003, "r_y2": 545.99104, "r_x3": 134.765, "r_y3": 545.99104, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "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": 134.765, "r_y0": 570.479, "r_x1": 480.60577, "r_y1": 570.479, "r_x2": 480.60577, "r_y2": 557.94604, "r_x3": 134.765, "r_y3": 557.94604, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "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": 134.765, "r_y0": 582.43399, "r_x1": 480.5936899999999, "r_y1": 582.43399, "r_x2": 480.5936899999999, "r_y2": 569.90103, "r_x3": 134.765, "r_y3": 569.90103, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "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": 134.765, "r_y0": 594.38899, "r_x1": 480.59158, "r_y1": 594.38899, "r_x2": 480.59158, "r_y2": 581.85603, "r_x3": 134.765, "r_y3": 581.85603, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "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": 134.765, "r_y0": 606.345, "r_x1": 480.58080999999993, "r_y1": 606.345, "r_x2": 480.58080999999993, "r_y2": 593.81204, "r_x3": 134.765, "r_y3": 593.81204, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "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": 134.765, "r_y0": 618.3, "r_x1": 206.79959, "r_y1": 618.3, "r_x2": 206.79959, "r_y2": 605.76704, "r_x3": 134.765, "r_y3": 605.76704, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 630.255, "r_x1": 480.59479, "r_y1": 630.255, "r_x2": 480.59479, "r_y2": 617.72205, "r_x3": 149.709, "r_y3": 617.72205, "coord_origin": "TOPLEFT"}, "text": "Additionally, the results show that OTSL has an advantage over HTML", "orig": "Additionally, the results show that OTSL has an advantage over HTML", "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": 134.765, "r_y0": 642.2099900000001, "r_x1": 480.59857000000005, "r_y1": 642.2099900000001, "r_x2": 480.59857000000005, "r_y2": 629.6770300000001, "r_x3": 134.765, "r_y3": 629.6770300000001, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "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": 134.765, "r_y0": 654.16499, "r_x1": 480.59384000000006, "r_y1": 654.16499, "r_x2": 480.59384000000006, "r_y2": 641.63203, "r_x3": 134.765, "r_y3": 641.63203, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "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": 134.765, "r_y0": 666.12, "r_x1": 405.7995, "r_y1": 666.12, "r_x2": 405.7995, "r_y2": 653.58704, "r_x3": 134.765, "r_y3": 653.58704, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533725738525, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78223000000003, "r_x1": 447.54476999999997, "r_y1": 102.78223000000003, "r_x2": 447.54476999999997, "r_y2": 91.49352999999996, "r_x3": 194.478, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.98441, "r_y0": 102.78223000000003, "r_x1": 480.59314, "r_y1": 102.78223000000003, "r_x2": 480.59314, "r_y2": 91.49352999999996, "r_x3": 475.98441, "r_y3": 91.49352999999996, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt index 083f5b3..463e568 100644 --- a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt +++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt @@ -182,9 +182,9 @@ <_unknown_>CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;Example 3-9 Creating a mask on the TAX_ID column Chapter 3. Row and Column Access Control 27 - 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA. + Figure 3-10 Column masks shown in System i Navigator 3.6.6 Activating RCAC Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: @@ -203,10 +203,10 @@ Figure 3-11 Selecting the EMPLOYEES table from System i Navigator 28 Row and Column Access Control Support in IBM DB2 for i - 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. + Figure 4-68 Visual Explain with RCAC enabled Figure 4-69 Index advice with no RCAC Chapter 4. Implementing Row and Column Access Control: Banking example diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.html b/tests/data/groundtruth/docling_v2/word_tables.docx.html index 527fc82..32c2251 100644 --- a/tests/data/groundtruth/docling_v2/word_tables.docx.html +++ b/tests/data/groundtruth/docling_v2/word_tables.docx.html @@ -1,74 +1,129 @@ - + - - - - Powered by Docling - - - + .page-break { + page-break-after: always; + border-top: 1px dashed #ccc; + margin: 2em 0; + } + .key-value-region { + background-color: #f9f9f9; + padding: 1em; + border-radius: 4px; + margin: 1em 0; + } + .key-value-region dt { + font-weight: bold; + } + .key-value-region dd { + margin-left: 1em; + margin-bottom: 0.5em; + } + .form-container { + border: 1px solid #ddd; + padding: 1em; + border-radius: 4px; + margin: 1em 0; + } + .form-item { + margin-bottom: 0.5em; + } + .image-classification { + font-size: 0.9em; + color: #666; + margin-top: 0.5em; + } + + + +

Test with tables

A uniform table

Header 0.0Header 0.1Header 0.2
Cell 1.0Cell 1.1Cell 1.2
Cell 2.0Cell 2.1Cell 2.2
@@ -86,4 +141,6 @@
Header 0.0Header 0.1Header 0.2
Cell 1.0Merged Cell 1.1 2.1Cell 1.2
Cell 2.0Cell 2.2
Cell 3.0Merged Cell 3.1 4.1Cell 3.2
Cell 4.0Cell 4.2Merged Cell 4.4 5.4
Cell 8.4

+
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