From cf78d5b7b9f12728270e673857fd299efc01a7db Mon Sep 17 00:00:00 2001 From: Christoph Auer <60343111+cau-git@users.noreply.github.com> Date: Mon, 10 Feb 2025 12:07:49 +0100 Subject: [PATCH] feat: Add content_layer property to items to address body, furniture and other roles (#735) * feat: Pass predicted page-headers and page-footers through to DoclingDocument furniture Signed-off-by: Christoph Auer * chore: Update all test GT Signed-off-by: Christoph Auer * fix: update all test cases Signed-off-by: Christoph Auer * fix: update all test cases again Signed-off-by: Christoph Auer * Update lock Signed-off-by: Christoph Auer * Update lock to final docling-core Signed-off-by: Christoph Auer --------- Signed-off-by: Christoph Auer --- docling/models/ds_glm_model.py | 62 ++- docling/utils/glm_utils.py | 10 + poetry.lock | 312 ++++++------ pyproject.toml | 2 +- .../docling_v1/2305.03393v1-pg9.json | 2 +- .../docling_v1/amt_handbook_sample.json | 2 +- .../groundtruth/docling_v2/2203.01017v2.json | 2 +- .../groundtruth/docling_v2/2206.01062.json | 2 +- .../docling_v2/2305.03393v1-pg9.json | 2 +- .../groundtruth/docling_v2/2305.03393v1.json | 2 +- .../docling_v2/amt_handbook_sample.json | 2 +- .../docling_v2/elife-56337.xml.json | 139 +++++- .../docling_v2/example_01.html.json | 15 +- .../docling_v2/example_02.html.json | 14 +- .../docling_v2/example_03.html.json | 23 +- .../docling_v2/example_04.html.json | 6 +- .../docling_v2/example_05.html.json | 6 +- .../docling_v2/ipa20180000016.json | 188 ++++++- .../docling_v2/ipa20200022300.json | 82 ++- .../docling_v2/lorem_ipsum.docx.json | 13 +- .../groundtruth/docling_v2/pa20010031492.json | 108 +++- .../docling_v2/pftaps057006474.json | 79 ++- .../groundtruth/docling_v2/pg06442728.json | 112 ++++- .../docling_v2/picture_classification.json | 2 +- .../docling_v2/pntd.0008301.xml.json | 128 ++++- .../docling_v2/pone.0234687.xml.json | 168 ++++++- .../docling_v2/powerpoint_sample.pptx.json | 38 +- .../powerpoint_with_image.pptx.json | 8 +- .../docling_v2/redp5110_sampled.json | 2 +- .../docling_v2/tablecell.docx.json | 13 +- .../groundtruth/docling_v2/test-01.xlsx.json | 14 +- .../docling_v2/test_emf_docx.docx.json | 11 +- .../docling_v2/unit_test_01.html.json | 12 +- .../docling_v2/unit_test_headers.docx.json | 51 +- .../unit_test_headers_numbered.docx.json | 55 ++- .../docling_v2/unit_test_lists.docx.json | 64 ++- .../docling_v2/wiki_duck.html.json | 467 +++++++++++++++++- .../docling_v2/word_sample.docx.json | 32 +- .../docling_v2/word_tables.docx.json | 22 +- .../docling_v1/ocr_test.pages.json | 2 +- .../groundtruth/docling_v2/ocr_test.json | 2 +- .../docling_v2/ocr_test.pages.json | 2 +- tests/test_backend_patent_uspto.py | 2 +- 43 files changed, 2082 insertions(+), 198 deletions(-) diff --git a/docling/models/ds_glm_model.py b/docling/models/ds_glm_model.py index 6f7de07..5d4c6ee 100644 --- a/docling/models/ds_glm_model.py +++ b/docling/models/ds_glm_model.py @@ -4,7 +4,12 @@ from pathlib import Path from typing import List, Union from deepsearch_glm.andromeda_nlp import nlp_model -from docling_core.types.doc import BoundingBox, CoordOrigin, DoclingDocument +from docling_core.types.doc import ( + BoundingBox, + CoordOrigin, + DocItemLabel, + DoclingDocument, +) from docling_core.types.legacy_doc.base import BoundingBox as DsBoundingBox from docling_core.types.legacy_doc.base import ( Figure, @@ -71,12 +76,15 @@ class GlmModel: ) main_text: List[Union[Ref, BaseText]] = [] + page_headers: List[Union[Ref, BaseText]] = [] + page_footers: List[Union[Ref, BaseText]] = [] + tables: List[DsSchemaTable] = [] figures: List[Figure] = [] page_no_to_page = {p.page_no: p for p in conv_res.pages} - for element in conv_res.assembled.elements: + for element in conv_res.assembled.body: # Convert bboxes to lower-left origin. target_bbox = DsBoundingBox( element.cluster.bbox.to_bottom_left_origin( @@ -238,6 +246,53 @@ class GlmModel: ) ) + # We can throw in headers and footers at the end of the legacy doc + # since the reading-order will re-sort it later. + for element in conv_res.assembled.headers: + # Convert bboxes to lower-left origin. + target_bbox = DsBoundingBox( + element.cluster.bbox.to_bottom_left_origin( + page_no_to_page[element.page_no].size.height + ).as_tuple() + ) + + if isinstance(element, TextElement): + + tel = BaseText( + text=element.text, + obj_type=layout_label_to_ds_type.get(element.label), + name=element.label, + prov=[ + Prov( + bbox=target_bbox, + page=element.page_no + 1, + span=[0, len(element.text)], + ) + ], + ) + if element.label == DocItemLabel.PAGE_HEADER: + index = len(page_headers) + ref_str = f"#/page-headers/{index}" + main_text.append( + Ref( + name=element.label, + obj_type=layout_label_to_ds_type.get(element.label), + ref=ref_str, + ), + ) + page_headers.append(tel) + elif element.label == DocItemLabel.PAGE_FOOTER: + index = len(page_footers) + ref_str = f"#/page-footers/{index}" + main_text.append( + Ref( + name=element.label, + obj_type=layout_label_to_ds_type.get(element.label), + ref=ref_str, + ), + ) + page_footers.append(tel) + page_dimensions = [ PageDimensions(page=p.page_no + 1, height=p.size.height, width=p.size.width) for p in conv_res.pages @@ -252,6 +307,8 @@ class GlmModel: tables=tables, figures=figures, page_dimensions=page_dimensions, + page_headers=page_headers, + page_footers=page_footers, ) return ds_doc @@ -264,6 +321,7 @@ class GlmModel: glm_doc = self.model.apply_on_doc(ds_doc_dict) docling_doc: DoclingDocument = to_docling_document(glm_doc) # Experimental + 1 == 1 # DEBUG code: def draw_clusters_and_cells(ds_document, page_no, show: bool = False): diff --git a/docling/utils/glm_utils.py b/docling/utils/glm_utils.py index b03a0e7..c3c4353 100644 --- a/docling/utils/glm_utils.py +++ b/docling/utils/glm_utils.py @@ -15,6 +15,7 @@ from docling_core.types.doc import ( TableCell, TableData, ) +from docling_core.types.doc.document import ContentLayer def resolve_item(paths, obj): @@ -311,6 +312,15 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument: current_list = None doc.add_text(label=DocItemLabel.FORMULA, text="", orig=text, prov=prov) + elif label in [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]: + current_list = None + + doc.add_text( + label=DocItemLabel(name_label), + text=text, + prov=prov, + content_layer=ContentLayer.FURNITURE, + ) else: current_list = None diff --git a/poetry.lock b/poetry.lock index 691dd84..c8a35b8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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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": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "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": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "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}, {"prov": [{"bbox": [134.76499938964844, 464.017822265625, 480.5989074707031, 519.2052612304688], "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}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 273.8258056640625, 264.4082946777344, 286.3288879394531], "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": [134.76499938964844, 173.6999969482422, 480.72003173828125, 269.9199523925781], "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": [134.76499938964844, 125.87999725341797, 480.59857177734375, 174.2779541015625], "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]. 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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": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "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": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "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}, {"prov": [{"bbox": [134.76499938964844, 464.017822265625, 480.5989074707031, 519.2052612304688], "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}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 273.8258056640625, 264.4082946777344, 286.3288879394531], "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": [134.76499938964844, 173.6999969482422, 480.72003173828125, 269.9199523925781], "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": [134.76499938964844, 125.87999725341797, 480.59857177734375, 174.2779541015625], "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": 7, "data": [[{"bbox": [160.3699951171875, 441.2538146972656, 168.04522705078125, 452.5425109863281], "spans": [[0, 0]], "text": "#", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [207.9739990234375, 441.2538146972656, 215.64923095703125, 452.5425109863281], "spans": [[0, 1]], "text": "#", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [239.79800415039062, 435.7748107910156, 278.33380126953125, 447.0635070800781], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, 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Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99230194091797, 593.8463745117188, 313.15460205078125, 667.8163452148438], "page": 1, "span": [0, 376], "__ref_s3_data": null}], "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99230194091797, 568.8463745117188, 167.27230834960938, 580.1864013671875], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "Boots Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [71.99229431152344, 491.84637451171875, 318.49224853515625, 565.8163452148438], "page": 1, "span": [0, 319], "__ref_s3_data": null}], "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 404.34637451171875, 316.65728759765625, 478.3163757324219], "page": 1, "span": [0, 332], "__ref_s3_data": null}], "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 291.84637451171875, 318.8122863769531, 390.8163757324219], "page": 1, "span": [0, 477], "__ref_s3_data": null}], "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 254.34637451171875, 313.91229248046875, 278.3163757324219], "page": 1, "span": [0, 122], "__ref_s3_data": null}], "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [72.0, 60.99040222167969, 184.14828491210938, 71.80239868164062], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. Self-locking nuts.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [320.9923095703125, 656.3463745117188, 561.808349609375, 730.3163452148438], "page": 1, "span": [0, 368], "__ref_s3_data": null}], "text": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. 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Stainless steel self-locking nut.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [537.9854125976562, 33.70970153808594, 560.775390625, 46.01969909667969], "page": 1, "span": [0, 4], "__ref_s3_data": null}], "text": "7-45", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [70.59269714355469, 79.6090087890625, 309.863037109375, 242.77777099609375], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. Self-locking nuts.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [320.4467468261719, 81.689208984375, 558.8576049804688, 352.359375], "page": 1, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 7-27. 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In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 254.34637451171875, 313.91229248046875, 278.3163757324219], "page": 1, "span": [0, 122], "__ref_s3_data": null}], "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [72.0, 60.99040222167969, 184.14828491210938, 71.80239868164062], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. 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"list"}], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 18.340221405029297, "t": 584.1799926757812, "r": 36.339778900146484, "b": 231.99996948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 96.3010025024414, "t": 684.9658813476562, "r": 498.9270935058594, "b": 672.0686645507812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "TableFormer: Table Structure Understanding with Transformers.", "text": "TableFormer: Table Structure Understanding with Transformers.", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 142.4770050048828, "t": 645.3146362304688, "r": 452.7502746582031, "b": 620.6796264648438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 208.123, "t": 616.03876, "r": 378.73257, "b": 607.57446, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "{ ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.79849, "t": 712.1882300000001, "r": 486.84909, "b": 703.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "level": 1}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 396.76776, "t": 549.97302, "r": 469.78748, "b": 541.22504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.97653, "t": 558.57703, "r": 324.79254, "b": 552.745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 410.483, "t": 558.57703, "r": 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"parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.7735, "t": 661.21899, "r": 542.73877, "b": 653.92902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8K", "text": "8K"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.79901, "t": 638.07648, "r": 542.76428, "b": 630.7865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6K", "text": "6K"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.5705, "t": 615.242, "r": 542.53577, "b": 607.95203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4K", "text": "4K"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.14551, "t": 592.3537, "r": 542.11078, "b": 585.06372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2K", "text": "2K"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.688072, "t": 669.5603, "r": 84.927567, "b": 666.37109, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 86.54731, "t": 669.5603, "r": 93.026291, "b": 666.37109, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Item", "text": "Item"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 102.50498, "t": 676.74786, "r": 115.3461, "b": 673.55865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Amount", "text": "Amount"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/4"}, "children": 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The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 74.253464, "t": 533.78528, "r": 101.75846, "b": 527.82526, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Input Image", "text": "Input Image"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 122.29972, "t": 533.65479, "r": 157.83972, "b": 527.69476, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Tokenised Tags", "text": "Tokenised Tags"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 78.549347, "t": 420.61420000000004, "r": 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"BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Predicted Tags", "text": "Predicted Tags"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 227.81598999999997, "t": 353.94458, "r": 270.78442, "b": 348.10794, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Bounding Boxes &", "text": "Bounding Boxes &"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 233.70262, "t": 347.93817, "r": 263.51105, "b": 342.1095000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Classification", "text": "Classification"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 184.74655, "t": 498.60498, "r": 212.16055, "b": 493.24097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Transformer", "text": "Transformer"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 178.91229, "t": 492.85498, "r": 216.74378999999996, "b": 487.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Decoder Network", "text": "Decoder Network"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 194.24574, "t": 509.2178, "r": 198.89099, "b": 504.15182000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x4", "text": "x4"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 221.45587, "t": 520.13086, "r": 276.47089, "b": 514.17084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CELL BBOX DECODER", "text": "CELL BBOX DECODER"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 151.65219, "t": 468.55759, "r": 197.29019, "b": 462.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Masked Multi-Head", "text": "Masked Multi-Head"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 163.43277, "t": 462.55759, "r": 184.19028, "b": 456.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Attention", "text": "Attention"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": ""}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": ""}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": ""}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "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.", "text": "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."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 437.37939, "t": 391.44705, "r": 443.69870000000003, "b": 385.12842, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 450.33203, "t": 391.44705, "r": 456.6513100000001, "b": 385.12842, "coord_origin": 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"children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595001220703125, "t": 333.2716369628906, "r": 532.6304931640625, "b": 324.3650817871094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"self_ref": "#/texts/349", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 119.39108, "t": 714.68945, "r": 151.94641, "b": 708.74078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "PubTabNet", "text": "PubTabNet"}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 53.345978, "t": 716.80847, "r": 59.327053, "b": 710.8598, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 289.5791, "t": 714.54169, "r": 319.8266, "b": 708.5930199999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], 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"#/texts/472", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 357.3768, "t": 706.00293, "r": 367.39523, "b": 701.04572, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "65K", "text": "65K"}, {"self_ref": "#/texts/473", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 333.73151, "t": 650.37677, "r": 374.92862, "b": 645.41956, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Complex Simple", "text": "Complex Simple"}, {"self_ref": "#/texts/474", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 345.69101, "t": 705.94409, "r": 355.70944, "b": 700.9868799999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "47K", "text": "47K"}, {"self_ref": "#/texts/475", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 508.54248, "t": 650.62317, "r": 526.00592, "b": 645.66595, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/pictures/11"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 510.44653000000005, "t": 705.9074100000001, "r": 523.70703, "b": 700.9502, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "145K", "text": "145K"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 581.068603515625, "r": 286.3651123046875, "b": 560.20703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "\u00b7 TableFormer output does not include the table cell content.", "text": "\u00b7 TableFormer output does not include the table cell content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/478", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": ""}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 302.59356689453125, "b": 48.9600944519043, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/514", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.8619689941406, "t": 139.0646514892578, "r": 545.1151123046875, "b": 118.20308685302734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact."}, {"self_ref": "#/texts/515", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.11199951171875, "t": 283.6626281738281, "r": 545.1138305664062, "b": 262.80108642578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"self_ref": "#/texts/516", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 315.65362548828125, "t": 563.276611328125, "r": 537.1475219726562, "b": 489.1985778808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/13"}, {"cref": "#/texts/14"}, 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Transformers.", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 142.4770050048828, "t": 645.3146362304688, "r": 452.7502746582031, "b": 620.6796264648438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 208.123, "t": 616.03876, "r": 378.73257, "b": 607.57446, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "{ ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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"#/texts/62", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 459.87621999999993, "t": 354.4064, "r": 465.88101, "b": 344.276, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 277.4996337890625, "r": 545.1151733398438, "b": 232.7270965576172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 220]}], "orig": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.79849, "t": 712.1882300000001, "r": 486.84909, "b": 703.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "level": 1}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 396.76776, "t": 549.97302, "r": 469.78748, "b": 541.22504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.97653, "t": 558.57703, "r": 324.79254, "b": 552.745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/94", "parent": {"cref": 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"BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8K", "text": "8K"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.79901, "t": 638.07648, "r": 542.76428, "b": 630.7865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6K", "text": "6K"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.5705, "t": 615.242, "r": 542.53577, "b": 607.95203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4K", "text": "4K"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.14551, "t": 592.3537, "r": 542.11078, "b": 585.06372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2K", "text": "2K"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. 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"BOTTOMLEFT"}, "charspan": [0, 745]}], "orig": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 74.253464, "t": 533.78528, "r": 101.75846, "b": 527.82526, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Input Image", "text": "Input Image"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 122.29972, "t": 533.65479, "r": 157.83972, "b": 527.69476, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Tokenised Tags", "text": "Tokenised Tags"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 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"parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 178.91229, "t": 492.85498, "r": 216.74378999999996, "b": 487.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Decoder Network", "text": "Decoder Network"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 194.24574, "t": 509.2178, "r": 198.89099, "b": 504.15182000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x4", "text": "x4"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 221.45587, "t": 520.13086, "r": 276.47089, "b": 514.17084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CELL BBOX DECODER", "text": "CELL BBOX DECODER"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 151.65219, "t": 468.55759, "r": 197.29019, "b": 462.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Masked Multi-Head", "text": "Masked Multi-Head"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 163.43277, "t": 462.55759, "r": 184.19028, "b": 456.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Attention", "text": "Attention"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": ""}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": ""}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": ""}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "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.", "text": "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."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 437.37939, "t": 391.44705, "r": 443.69870000000003, "b": 385.12842, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 450.33203, 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"charspan": [0, 2]}], "orig": "29", "text": "29"}, {"self_ref": "#/texts/347", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 384.35437, "t": 410.22278, "r": 430.99261, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Predicted Structure", "text": "Predicted Structure"}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595001220703125, "t": 333.2716369628906, "r": 532.6304931640625, "b": 324.3650817871094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"self_ref": "#/texts/349", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 119.90107727050781, "r": 364.4058532714844, "b": 109.15335845947266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 313.3450012207031, "t": 98.0382080078125, "r": 545.1134033203125, "b": 79.06324768066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. 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"parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 345.69101, "t": 705.94409, "r": 355.70944, "b": 700.9868799999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "47K", "text": "47K"}, {"self_ref": "#/texts/475", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 508.54248, "t": 650.62317, "r": 526.00592, "b": 645.66595, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 510.44653000000005, "t": 705.9074100000001, "r": 523.70703, "b": 700.9502, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "145K", "text": "145K"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 581.068603515625, "r": 286.3651123046875, "b": 560.20703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "\u00b7 TableFormer output does not include the table cell content.", "text": "\u00b7 TableFormer output does not include the table cell content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/478", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": ""}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/groups/13"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/15"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/15"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/groups/15"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 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End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. 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Rueschlikon, Switzerland bpf@zurich.ibm.com"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 255.11602783203125, "t": 658.32763671875, "r": 357.8802490234375, "b": 611.7597045898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 419.2650451660156, "t": 658.32763671875, "r": 522.029296875, "b": 611.7597045898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 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{"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 526.89484, "r": 340.54153, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MAGNIFICATION", "text": "MAGNIFICATION"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 517.23358, "r": 354.59546, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 505.96207, "r": 354.6499, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupillary distance.", "text": "pupils is the interpupillary distance."}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 504.35187, "r": 328.25125, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.93671, "t": 504.35187, "r": 354.29825, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "To adjust the interpupillary distance", "text": "To adjust the interpupillary distance"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 502.74164, "r": 355.02075, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "rotate the prism caps until both eyes", "text": "rotate the prism caps until both eyes"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 501.13144000000005, "r": 350.82028, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 499.52121, "r": 336.2067, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 496.30078, "r": 335.3941, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.07379, "t": 494.69058, "r": 353.18555, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Remove the lens protective cover.", "text": "Remove the lens protective cover."}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 493.08035, "r": 328.35919, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.0972, "t": 493.08035, "r": 353.45065, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Place the specimen on the working", "text": "Place the specimen on the working"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 491.47015, "r": 333.32825, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 489.85991999999993, "r": 328.31296, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.02783, "t": 489.85991999999993, "r": 354.76303, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Focus the specimen with the left eye", "text": "Focus the specimen with the left eye"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 488.24973, "r": 355.96307, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 483.41907, "r": 355.38867, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 478.58844, "r": 344.13388, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 476.97821000000005, "r": 328.37418, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11963, "t": 476.97821000000005, "r": 348.50162, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Disconnect the power cord.", "text": "Disconnect the power cord."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 475.36801, "r": 328.34061, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06931, "t": 475.36801, "r": 353.11588, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 472.14757999999995, "r": 348.02094, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 470.53735, "r": 328.37512, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.12036, "t": 470.53735, "r": 352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 468.92715, "r": 328.36884, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.1102, "t": 468.92715, "r": 356.5412, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Open the window in the base plate and", "text": "Open the window in the base plate and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 528.50507, "r": 366.93256, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 526.89484, "r": 359.89841, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.63751, "t": 526.89484, "r": 387.98407, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Turn the focusing knob away or toward", "text": "Turn the focusing knob away or toward"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 525.28467, "r": 384.58948, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/98", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 504.35187, "r": 367.98694, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 502.74164, "r": 359.80386, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.49353, "t": 502.74164, "r": 386.70093, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "If the image is not clear to both eyes", "text": "If the image is not clear to both eyes"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 501.13144000000005, "r": 388.03534, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 499.52121, "r": 373.13724, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment."}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 494.69058, "r": 359.83682, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.54297, "t": 494.69058, "r": 388.08289, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "To adjust the eyepiece for viewing with", "text": "To adjust the eyepiece for viewing with"}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 493.08035, "r": 382.73251, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 491.47015, "r": 387.72266, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 489.85991999999993, "r": 384.1991, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "and left eyes, follow the following", "text": "and left eyes, follow the following"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right eyepiece"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 462.4863, "r": 382.63382, "b": 460.74725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 459.26587000000006, "r": 375.67661, "b": 457.52682000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 457.65564, "r": 359.90311, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 456.04544, "r": 372.01416, "b": 454.3064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet."}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 454.43521, "r": 359.88327, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.61191, "t": 454.43521, "r": 384.65726, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 452.82501, "r": 385.33649, "b": 451.0859699999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 451.21478, "r": 379.57224, "b": 449.47577, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4274, "t": 449.60458, "r": 359.91788, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.66312, "t": 449.60458, "r": 384.5108, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 447.99434999999994, "r": 359.92792, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.67746, "t": 447.99434999999994, "r": 385.41235, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Open the window in the base plate", "text": "Open the window in the base plate"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 446.38416, "r": 383.2782, "b": 444.64511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "and replace the halogen lamp or", "text": "and replace the halogen lamp or"}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 444.77393, "r": 383.13953, "b": 443.03491, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 443.16373, "r": 368.43472, "b": 441.42468, 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It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 475.20023, "r": 456.73859000000004, "b": 473.02646, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses."}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.62399, "t": 471.57059, "r": 427.77472, "b": 467.65777999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CONSTRUCTION", "text": "CONSTRUCTION"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 465.53930999999994, "r": 456.02639999999997, "b": 463.36551, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope."}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 463.3923300000001, "r": 455.42238999999995, "b": 461.2185400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 461.24539, "r": 457.39844, "b": 459.07159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted illumination. By using this instrument,", "text": "equipped with transmitted illumination. 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Y L G H"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 406.24268, "r": 536.14716, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a", "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 538.31085, "t": 406.24268, "r": 549.49921, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "is placed on", "text": "is placed on"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 403.96399, "r": 547.58185, "b": 401.8429, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 532.05829, "t": 398.7871999999999, "r": 550.42261, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "on the circling line of", "text": "on the circling line of"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 396.50851, "r": 455.74692, "b": 394.38745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "minima.", "text": "minima."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 376.40451, "r": 496.2829, "b": 374.49554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", "text": "Stopways,"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 335.13712, "r": 461.92083999999994, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Taxiways, Park-", "text": "Taxiways, Park-"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 333.08627, "r": 457.08014, "b": 331.17731000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "ing Areas", "text": "ing Areas"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 337.18793, "r": 472.87732, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Displaced", "text": "Displaced"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 335.13712, "r": 472.49792, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Threshold", "text": "Threshold"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 337.18793, "r": 483.61584, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Closed", "text": "Closed"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 335.13712, "r": 486.60754000000003, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Pavement", "text": "Pavement"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 337.18793, "r": 504.20648, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Water Runway", "text": "Water Runway"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 322.67026, "r": 548.59674, "b": 320.54919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 320.39157, "r": 500.08181999999994, "b": 318.27051, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text": "sions, runway slope, arresting gear, and displaced threshold."}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 315.83423, "r": 449.59933000000007, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.09796, "t": 315.83423, "r": 547.82562, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L", "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 547.82623, "t": 315.83423, "r": 548.45862, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 313.55554, "r": 470.52609000000007, "b": 311.43445, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "pads may also be shown.", "text": "pads may also be shown."}, {"self_ref": "#/texts/231", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 308.99817, "r": 493.37906000000004, "b": 306.87708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q", "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 304.4408, "r": 551.80023, "b": 295.48364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 496]}], "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I the landing surface. Circling only approaches will not show a TDZE.", "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I the landing surface. Circling only approaches will not show a TDZE."}, {"self_ref": "#/texts/233", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.80661000000003, "t": 276.05629999999996, "r": 502.08792, "b": 272.98235999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "114", "text": "114"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 444.56319999999994, "t": 369.15131, "r": 446.25998, "b": 320.12872, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 412.62463, "r": 355.13138, "b": 409.86664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "AGL 2013 Financial Calendar", "text": "AGL 2013 Financial Calendar"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 409.69727, "r": 330.96848, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "22", "text": "22"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75003, "t": 409.69727, "r": 341.12875, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 409.69727, "r": 384.81079, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "2012 full year result and fi nal dividend announced", "text": "2012 full year result and fi nal dividend announced"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 407.15448, "r": 330.97336, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "30", "text": "30"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75735, "t": 407.15448, "r": 341.16534, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 407.15448, "r": 372.90613, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Ex-dividend trading commences", "text": "Ex-dividend trading commences"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 404.61172, "r": 330.20337, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.00137, "t": 404.61172, "r": 342.9715, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 404.61172, "r": 374.88693, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Record date for 2012 fi nal dividend", "text": "Record date for 2012 fi nal dividend"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 402.06897, "r": 331.0173, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.82327, "t": 402.06897, "r": 343.91284, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 402.06897, "r": 365.65988, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Final dividend payable", "text": "Final dividend payable"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 399.52618, "r": 330.98804, "b": 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"27"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.7934, "t": 396.9834, "r": 342.1416, "b": 394.72687, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "February 2013", "text": "February 2013"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 342.64841, "t": 396.81702, "r": 342.65811, "b": 395.50142999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47177, "t": 396.98526, "r": 386.25897, "b": 394.7287, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "2013 interim result and interim dividend announced", "text": "2013 interim result and interim dividend announced"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40491, "t": 394.44250000000005, "r": 331.02695, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.83795, "t": 394.44250000000005, "r": 340.75909, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2013", "text": "August 2013"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 341.26437, "t": 394.2746, "r": 341.27408, "b": 392.95905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47144, "t": 394.44287, "r": 385.93265, "b": 392.18631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "2013 full year results and fi nal dividend announced", "text": "2013 full year results and fi nal dividend announced"}, {"self_ref": "#/texts/259", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 391.53845, "r": 329.87708, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/260", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 330.34882, "t": 391.53845, "r": 358.65204, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Indicative dates only, subject to change/Board confi rmation", "text": "Indicative dates only, subject to change/Board confi rmation"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, 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"text": "Ye s te rd ay"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/265", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/266", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/267", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/268", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/269", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, 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[{"page_no": 1, "bbox": {"l": 363.54486, "t": 309.0159, "r": 395.70975, "b": 306.75937, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "achievement of scientifi c knowledge, assisted", "text": "achievement of scientifi c knowledge, assisted"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 306.98236, "r": 394.27283, "b": 304.7258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "by mechanical ingenuity.\u201d Within two years,", "text": "by mechanical ingenuity.\u201d Within two years,"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 304.94879, "r": 396.65939, "b": 302.69226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "165 gas lamps were lighting the City of Sydney.", "text": "165 gas lamps were lighting the City of Sydney."}, 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19]}], "orig": ", you learned about", "text": ", you learned about"}, {"self_ref": "#/texts/285", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 526.06775, "r": 544.50403, "b": 524.02979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "some of the controls in your vehicle. This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. Understanding these three ways", "text": "the messages printed on them. Understanding these three ways"}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 506.09204, "r": 545.26471, "b": 504.05408, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "of classifying signs will help you figure out the meaning of signs", "text": "of classifying signs will help you figure out the meaning of signs"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 504.04901, "r": 513.31335, "b": 502.01105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "that are new to you.", "text": "that are new to you."}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 505.43439, "t": 488.92404, "r": 508.53033000000005, "b": 487.10361, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Stop", "text": "Stop"}, {"self_ref": "#/texts/295", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 527.45502, "t": 488.74646, "r": 541.44678, "b": 486.92603, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Yield the right-of-way", "text": "Yield the right-of-way"}, {"self_ref": "#/texts/296", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.79385, "t": 470.81027, "r": 510.41632, "b": 468.98984, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Shows driving", "text": "Shows driving"}, {"self_ref": "#/texts/297", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.79385, "t": 469.12268000000006, "r": 509.04268999999994, "b": 467.30224999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "regulations", "text": "regulations"}, {"self_ref": 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"children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 471.79062, "r": 489.26166000000006, "b": 470.28897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- yellow lines", "text": "- yellow lines"}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 469.49268, "r": 488.59189, "b": 467.991, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "- white lines", "text": "- white lines"}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 467.1947, "r": 491.17004000000003, "b": 465.69302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 465.56012, "r": 487.58978, "b": 463.52216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "markings", "text": "markings"}, {"self_ref": "#/texts/344", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 462.85449, "r": 491.75177, "b": 461.35284, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- other markings", "text": "- other markings"}, {"self_ref": "#/texts/345", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.15246999999994, "t": 526.92969, "r": 493.75586, "b": 523.93127, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "in this chapter", "text": "in this chapter"}, {"self_ref": "#/texts/346", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 199.53408813476562, "r": 379.82049560546875, "b": 189.22499084472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "level": 1}, {"self_ref": "#/texts/347", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 184.3324432373047, "r": 559.1859741210938, "b": 164.9988250732422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.65997314453125, "t": 151.94566345214844, "r": 404.6536560058594, "b": 144.41390991210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ACM Reference Format:", "text": "ACM Reference Format:", "level": 1}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 141.88003540039062, "r": 559.5494995117188, "b": 84.62297058105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/356", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/357", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/373", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/386", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/387", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/430", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 349.16577, "t": 545.31982, "r": 352.48175, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 385.93698, "t": 545.31982, "r": 392.56894, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 424.366, "t": 545.31982, "r": 430.99796, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "40", "text": 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538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 573.61536, "r": 337.56735, "b": 567.54126, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 599.91339, "r": 337.56735, "b": 593.83929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 626.21136, "r": 337.56735, "b": 620.13727, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", 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"parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 575.99994, "r": 411.54321, "b": 569.92584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/455", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 591.77875, "r": 411.54321, "b": 585.70465, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/456", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 607.55756, "r": 411.54321, "b": 601.48346, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 623.33636, "r": 411.54321, "b": 617.26227, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/458", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 639.11511, "r": 411.54321, "b": 633.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/459", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/461", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH 10% point improvement."}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/487", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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(A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. 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Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 90.96701049804688, "t": 658.32763671875, "r": 193.73123168945312, "b": 611.7597045898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 255.11602783203125, "t": 658.32763671875, "r": 357.8802490234375, "b": 611.7597045898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 419.2650451660156, "t": 658.32763671875, "r": 522.029296875, "b": 611.7597045898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 100.21663665771484, "r": 157.03125, "b": 93.70310974121094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 92.24663543701172, "r": 166.94093322753906, "b": 85.73310852050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "https://doi.org/10.1145/3534678.3539043", "text": "https://doi.org/10.1145/3534678.3539043"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 251.91700744628906, "r": 559.8057861328125, "b": 232.48475646972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "Figure 1: Four examples of complex page layouts across different document categories", "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.86951, "t": 440.21915, "r": 330.41248, "b": 438.04535, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 460.42731000000003, "r": 351.16092, "b": 458.68829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 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[], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.85242, "t": 537.23218, "r": 362.60028, "b": 533.31934, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ")", "text": ")"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 528.50507, "r": 345.84351, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "SELECTING OBJECTIVE", "text": "SELECTING OBJECTIVE"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 526.89484, "r": 340.54153, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MAGNIFICATION", "text": "MAGNIFICATION"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 517.23358, "r": 354.59546, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 505.96207, "r": 354.6499, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupillary distance.", "text": "pupils is the interpupillary distance."}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 504.35187, "r": 328.25125, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.93671, "t": 504.35187, "r": 354.29825, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "To adjust the interpupillary distance", "text": "To adjust the interpupillary distance"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 502.74164, "r": 355.02075, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "rotate the prism caps until both eyes", "text": "rotate the prism caps until both eyes"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 501.13144000000005, "r": 350.82028, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 499.52121, "r": 336.2067, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 496.30078, "r": 335.3941, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.07379, "t": 494.69058, "r": 353.18555, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Remove the lens protective cover.", "text": "Remove the lens protective cover."}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 493.08035, "r": 328.35919, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.0972, "t": 493.08035, "r": 353.45065, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Place the specimen on the working", "text": "Place the specimen on the working"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 491.47015, "r": 333.32825, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 489.85991999999993, "r": 328.31296, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.02783, "t": 489.85991999999993, "r": 354.76303, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Focus the specimen with the left eye", "text": "Focus the specimen with the left eye"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 488.24973, "r": 355.96307, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 483.41907, "r": 355.38867, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/71", "parent": {"cref": 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"#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 475.36801, "r": 328.34061, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06931, "t": 475.36801, "r": 353.11588, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 472.14757999999995, "r": 348.02094, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 470.53735, "r": 328.37512, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.12036, "t": 470.53735, "r": 352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 468.92715, "r": 328.36884, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.1102, "t": 468.92715, "r": 356.5412, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Open the window in the base plate and", "text": "Open the window in the base plate and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 528.50507, "r": 366.93256, "b": 526.76605, "coord_origin": 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"charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/98", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 504.35187, "r": 367.98694, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 502.74164, "r": 359.80386, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.49353, "t": 502.74164, "r": 386.70093, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "If the image is not clear to both eyes", "text": "If the image is not clear to both eyes"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 501.13144000000005, "r": 388.03534, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 499.52121, "r": 373.13724, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment."}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 494.69058, "r": 359.83682, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.54297, "t": 494.69058, "r": 388.08289, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "To adjust the eyepiece for viewing with", "text": "To adjust the eyepiece for viewing with"}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 493.08035, "r": 382.73251, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 491.47015, "r": 387.72266, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 489.85991999999993, "r": 384.1991, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "and left eyes, follow the following", "text": "and left eyes, follow the following"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right eyepiece"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 462.4863, "r": 382.63382, "b": 460.74725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 459.26587000000006, "r": 375.67661, "b": 457.52682000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 457.65564, "r": 359.90311, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 456.04544, "r": 372.01416, "b": 454.3064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet."}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 454.43521, "r": 359.88327, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.61191, "t": 454.43521, "r": 384.65726, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 452.82501, "r": 385.33649, "b": 451.0859699999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 451.21478, "r": 379.57224, "b": 449.47577, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4274, "t": 449.60458, "r": 359.91788, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.66312, "t": 449.60458, "r": 384.5108, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 447.99434999999994, "r": 359.92792, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.67746, "t": 447.99434999999994, "r": 385.41235, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Open the window in the base plate", "text": "Open the window in the base plate"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 446.38416, "r": 383.2782, "b": 444.64511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "and replace the halogen lamp or", "text": "and replace the 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The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W 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O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", "text": "Stopways,"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 335.13712, "r": 461.92083999999994, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Taxiways, Park-", "text": "Taxiways, Park-"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 333.08627, "r": 457.08014, "b": 331.17731000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "ing Areas", "text": "ing Areas"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 337.18793, "r": 472.87732, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Displaced", "text": "Displaced"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 335.13712, "r": 472.49792, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Threshold", "text": "Threshold"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 337.18793, "r": 483.61584, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Closed", "text": "Closed"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 335.13712, "r": 486.60754000000003, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Pavement", "text": "Pavement"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 337.18793, "r": 504.20648, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Water Runway", "text": "Water Runway"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 322.67026, "r": 548.59674, "b": 320.54919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 320.39157, "r": 500.08181999999994, "b": 318.27051, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text": "sions, runway slope, arresting gear, and displaced threshold."}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 315.83423, "r": 449.59933000000007, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.09796, "t": 315.83423, "r": 547.82562, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L", "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 547.82623, "t": 315.83423, "r": 548.45862, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 313.55554, "r": 470.52609000000007, "b": 311.43445, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "pads may also be shown.", "text": "pads may also be shown."}, {"self_ref": "#/texts/231", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 308.99817, "r": 493.37906000000004, "b": 306.87708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q", "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 304.4408, "r": 551.80023, "b": 295.48364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 496]}], "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I the landing surface. Circling only approaches will not show a TDZE.", "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I the landing surface. Circling only approaches will not show a TDZE."}, {"self_ref": "#/texts/233", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.80661000000003, "t": 276.05629999999996, "r": 502.08792, "b": 272.98235999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "114", "text": "114"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 444.56319999999994, "t": 369.15131, "r": 446.25998, "b": 320.12872, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 412.62463, "r": 355.13138, "b": 409.86664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "AGL 2013 Financial Calendar", "text": "AGL 2013 Financial Calendar"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 409.69727, "r": 330.96848, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "22", "text": "22"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75003, "t": 409.69727, "r": 341.12875, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 409.69727, "r": 384.81079, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "2012 full year result and fi nal dividend announced", "text": "2012 full year result and fi nal dividend announced"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 407.15448, "r": 330.97336, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "30", "text": "30"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75735, "t": 407.15448, "r": 341.16534, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 407.15448, "r": 372.90613, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Ex-dividend trading commences", "text": "Ex-dividend trading commences"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 404.61172, "r": 330.20337, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.00137, "t": 404.61172, "r": 342.9715, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 404.61172, "r": 374.88693, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Record date for 2012 fi nal dividend", "text": "Record date for 2012 fi nal dividend"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 402.06897, "r": 331.0173, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.82327, "t": 402.06897, "r": 343.91284, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": 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"bbox": {"l": 329.40536, "t": 391.53845, "r": 329.87708, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/260", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 330.34882, "t": 391.53845, "r": 358.65204, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Indicative dates only, subject to change/Board confi rmation", "text": "Indicative dates only, subject to change/Board confi rmation"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 387.65497, "r": 391.771, "b": 385.39844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney", "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 385.62143, "r": 369.65308, "b": 383.36486999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012."}, {"self_ref": "#/texts/263", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 331.46945000000005, "r": 379.25955, "b": 326.45493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Ye s te rd ay", "text": "Ye s te rd ay"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/265", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/266", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/267", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/268", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/269", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "In 1841, when AGL supplied the gas to light", "text": "In 1841, when AGL supplied the gas to light"}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 313.08301, "r": 393.75891, "b": 310.82648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "the fi rst public street lamp, it was reported", "text": "the fi rst public street lamp, it was reported"}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 311.04947, "r": 390.4975, "b": 308.79291, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "in the Sydney Gazette as a \u201cwonderful", "text": "in the Sydney Gazette as a \u201cwonderful"}, {"self_ref": "#/texts/272", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, 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lighting the City of Sydney."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 372.06876, "r": 384.19696, "b": 360.90588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Looking back on", "text": "Looking back on"}, {"self_ref": "#/texts/276", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 361.89621, "r": 372.16626, "b": 350.73331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "175 years of", "text": "175 years of"}, {"self_ref": "#/texts/277", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 351.72363000000007, "r": 385.3981, "b": 340.56076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "looking forward.", "text": "looking forward."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 419.83841, "r": 353.36179, "b": 418.08331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "AGL Energy Limited ABN 74 115 061 375", "text": "AGL Energy Limited ABN 74 115 061 375"}, {"self_ref": "#/texts/279", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 546.20587, "t": 431.09552, "r": 548.23407, "b": 429.17758, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "29", "text": "29"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 497.77728, "t": 540.56616, "r": 542.8255, "b": 537.05615, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "signs, signals and road markings", "text": "signs, signals and road markings"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 490.30679, "t": 540.52521, "r": 492.09982, "b": 537.0152, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/282", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 528.11078, "r": 500.05637, "b": 526.07281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "In", "text": "In"}, {"self_ref": "#/texts/283", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 500.05637, "t": 528.14282, "r": 524.37036, "b": 526.1369, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "chapter 2, you and your vehicle", "text": "chapter 2, you and your vehicle"}, {"self_ref": "#/texts/284", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 524.37036, "t": 528.11078, "r": 539.89124, "b": 526.07281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": ", you learned about", "text": ", you learned about"}, {"self_ref": "#/texts/285", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 526.06775, "r": 544.50403, "b": 524.02979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "some of the controls in your vehicle. This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. Understanding these three ways", "text": "the messages printed on them. Understanding these three ways"}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 506.09204, "r": 545.26471, "b": 504.05408, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "of classifying signs will help you figure out the meaning of signs", "text": "of classifying signs will help you figure out the meaning of signs"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 504.04901, "r": 513.31335, "b": 502.01105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "that are new to you.", "text": "that are new to you."}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 505.43439, "t": 488.92404, "r": 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ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/356", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/357", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/373", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/386", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/387", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/430", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 349.16577, "t": 545.31982, "r": 352.48175, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 385.93698, "t": 545.31982, "r": 392.56894, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 424.366, "t": 545.31982, "r": 430.99796, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "40", "text": "40"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 462.79504000000003, "t": 545.31982, "r": 469.427, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 501.22406, "t": 545.31982, "r": 507.85602, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "80", "text": "80"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 537.99524, "t": 545.31982, "r": 547.94318, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "100", "text": "100"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 410.28143, "t": 538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 573.61536, "r": 337.56735, "b": 567.54126, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 599.91339, "r": 337.56735, "b": 593.83929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/446", "parent": 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[{"page_no": 6, "bbox": {"l": 322.92276, "t": 643.62311, "r": 328.99686, "b": 605.20782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 0.50:0.95", "text": "mAP 0.50:0.95"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 470.97235, "t": 556.63324, "r": 477.6055, "b": 550.55914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/451", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 477.65662, "t": 557.17609, "r": 479.97778000000005, "b": 552.92419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/452", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 531.55127, "t": 556.58765, "r": 538.18445, "b": 550.51355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/453", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 538.23553, "t": 557.13049, "r": 540.5567, "b": 552.8786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/454", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 575.99994, "r": 411.54321, "b": 569.92584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/455", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 591.77875, "r": 411.54321, "b": 585.70465, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/456", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 607.55756, "r": 411.54321, "b": 601.48346, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 623.33636, "r": 411.54321, "b": 617.26227, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/458", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 639.11511, "r": 411.54321, "b": 633.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/459", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/461", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH 10% point improvement."}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/487", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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null, "children": [], "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": "#/texts/5"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.47799682617188, "t": 700.5064697265625, "r": 447.5447692871094, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.9844055175781, "t": 700.5064697265625, "r": 480.5931396484375, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 675.5369873046875, "r": 480.5966491699219, "b": 639.093017578125, "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."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "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."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "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."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "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."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "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."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "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": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, "b": 441.2538146972656, 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{"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": 194.47799682617188, "t": 700.5064697265625, "r": 447.5447692871094, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.9844055175781, "t": 700.5064697265625, "r": 480.5931396484375, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 675.5369873046875, "r": 480.5966491699219, "b": 639.093017578125, "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."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "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."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "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."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "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."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "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."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "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": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, 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"prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 676.1008911132812, "r": 480.59735107421875, "b": 645.4859008789062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 139.34305, "t": 622.30841, "r": 476.01270000000005, "b": 591.81409, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 222]}], "orig": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]", "text": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 229.52109000000002, "t": 596.41626, "r": 298.6087, "b": 587.61926, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "and Peter Staar", "text": "and Peter Staar"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 279.1051, "t": 574.79602, "r": 336.25153, "b": 566.72632, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.96609, "t": 563.19147, "r": 392.38983, "b": 555.72247, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 396.41107, "t": 511.01648, "r": 402.97336, "b": 502.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 418.58682, "t": 511.10208, "r": 425.14911, "b": 502.57657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 395.74835, "t": 488.76273, 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On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 374.49326, "t": 623.40637, "r": 381.66843, "b": 614.08459, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 398.74011, "t": 623.49994, "r": 405.91528, "b": 614.17816, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 373.76862, "t": 599.07446, "r": 380.94379, "b": 589.75269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], 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cells: \"C\"", "text": "- simple cells: \"C\""}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 539.06744, "r": 337.33313, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.15491, "t": 539.06744, "r": 421.98624, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "- horizontal merges: \"C\", \"L\"", "text": "- horizontal merges: \"C\", \"L\""}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 529.12952, "r": 337.29868, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.086, "t": 529.12952, "r": 415.34375, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "- vertical merges: \"C\", \"U\"", "text": "- vertical merges: \"C\", \"U\""}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 519.19159, "r": 426.59875, "b": 512.97711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "text": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 185.67178, "t": 547.95776, "r": 189.35544, "b": 541.74329, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/217", "parent": {"cref": 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7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 585.16132, "r": 175.72659, "b": 577.39319, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 274.29419, "t": 623.72028, "r": 280.2735, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 359.56152, "t": 623.72028, "r": 365.54083, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 548.78851, "r": 175.27112, "b": 541.02039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "E", "text": "E"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 486.7041931152344, "r": 246.6519775390625, "b": 477.8972473144531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "level": 1}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 466.7522277832031, "r": 363.7961730957031, "b": 457.95526123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 444.8291931152344, "r": 480.5890197753906, "b": 424.0662536621094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. 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"text": "3"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 340.80972, "t": 237.40688, "r": 343.33673, "b": 233.14365999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 330.97992, "t": 237.16965000000005, "r": 333.50693, "b": 232.90643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "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."}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "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."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "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."}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "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."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "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."}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/335", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 180.12473, "t": 275.7667799999999, "r": 190.62042, "b": 273.05008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "", "text": "
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Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.3756103515625, "t": 698.22900390625, "r": 480.5894775390625, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58837890625, "b": 614.2323608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 390]}], "orig": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"self_ref": "#/texts/455", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 298.1791687011719, "r": 197.68641662597656, "b": 287.61077880859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/456", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.37100219726562, "t": 269.1201477050781, "r": 480.5920104980469, "b": 228.12855529785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.3709716796875, "t": 224.4811553955078, "r": 480.5920104980469, "b": 183.53439331054688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 301]}], "orig": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. 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"#/texts/469"}, {"cref": "#/texts/470"}, {"cref": "#/texts/471"}, {"cref": "#/texts/472"}, {"cref": "#/texts/473"}, {"cref": "#/texts/474"}], "content_layer": "body", "name": "list", "label": "list"}, {"self_ref": "#/groups/6", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/477"}, {"cref": "#/texts/478"}, {"cref": "#/texts/479"}, {"cref": "#/texts/480"}, {"cref": "#/texts/481"}, {"cref": "#/texts/482"}], "content_layer": "body", "name": "list", "label": "list"}], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 18.34021759033203, "t": 582.52001953125, "r": 36.339786529541016, "b": 236.99996948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 676.1008911132812, "r": 480.59735107421875, "b": 645.4859008789062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 139.34305, "t": 622.30841, "r": 476.01270000000005, "b": 591.81409, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 222]}], "orig": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]", "text": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 229.52109000000002, "t": 596.41626, "r": 298.6087, "b": 587.61926, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "and Peter Staar", "text": "and Peter Staar"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 279.1051, "t": 574.79602, "r": 336.25153, "b": 566.72632, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.96609, "t": 563.19147, "r": 392.38983, "b": 555.72247, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 396.41107, "t": 511.01648, "r": 402.97336, "b": 502.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 418.58682, "t": 511.10208, "r": 425.14911, "b": 502.57657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": 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", "text": ""}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 230.04745000000003, "t": 382.22638, "r": 245.58765000000002, "b": 375.83224, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "
", "text": ""}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 154.50595, "t": 533.39905, "r": 159.62473, "b": 526.29443, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 164.74348, "t": 533.39905, "r": 185.21857, "b": 526.29443, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 164.3548, "t": 525.50293, "r": 222.05352999999997, "b": 518.39832, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "sequence length:", "text": "sequence length:"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 224.15326, "t": 525.50293, "r": 232.57729, "b": 518.39832, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 339.68621826171875, "r": 480.5923156738281, "b": 271.1133117675781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. 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"label": "text", "prov": [{"page_no": 7, "bbox": {"l": 186.87526, "t": 614.02332, "r": 190.55891, "b": 607.80884, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 196.48746, "t": 622.9848, "r": 200.17111, "b": 616.77032, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 585.16132, "r": 175.72659, "b": 577.39319, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 274.29419, "t": 623.72028, "r": 280.2735, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 359.56152, "t": 623.72028, "r": 365.54083, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 548.78851, "r": 175.27112, "b": 541.02039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "E", "text": "E"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 486.7041931152344, "r": 246.6519775390625, "b": 477.8972473144531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "level": 1}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 466.7522277832031, "r": 363.7961730957031, "b": 457.95526123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 444.8291931152344, "r": 480.5890197753906, "b": 424.0662536621094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 147.30025, "t": 251.26836000000003, "r": 149.70605, "b": 248.89992000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 150.90895, "t": 251.26836000000003, "r": 155.72055, "b": 248.89992000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Item", "text": "Item"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 162.75987, "t": 256.60619999999994, "r": 172.2963, "b": 254.23775999999998, "coord_origin": 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[0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 330.97992, "t": 237.16965000000005, "r": 333.50693, "b": 232.90643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "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."}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "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."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "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."}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "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."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "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."}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/335", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 180.12473, "t": 275.7667799999999, "r": 190.62042, "b": 273.05008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "", "text": "
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6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"self_ref": "#/texts/455", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 298.1791687011719, "r": 197.68641662597656, "b": 287.61077880859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/456", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.37100219726562, "t": 269.1201477050781, "r": 480.5920104980469, "b": 228.12855529785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.3709716796875, "t": 224.4811553955078, "r": 480.5920104980469, "b": 183.53439331054688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 301]}], "orig": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) 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"version": "1.0.0", "name": "amt_handbook_sample", "origin": {"mimetype": "application/pdf", "binary_hash": 10189692113572347872, "filename": "amt_handbook_sample.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "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": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/pictures/0"}, {"cref": "#/texts/13"}, {"cref": "#/texts/14"}, {"cref": "#/texts/15"}, {"cref": "#/texts/16"}, {"cref": "#/texts/17"}, {"cref": "#/texts/18"}, {"cref": "#/texts/19"}, {"cref": "#/pictures/1"}, {"cref": "#/texts/26"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99212646484375, "t": 730.3163452148438, "r": 314.11212158203125, "b": 681.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 244]}], "orig": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 667.8163452148438, "r": 313.15460205078125, "b": 593.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 580.1864013671875, "r": 167.27230834960938, "b": 568.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Boots Self-Locking Nut", "text": "Boots Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 565.8163452148438, "r": 318.49224853515625, "b": 491.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 319]}], "orig": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 478.3163757324219, "r": 316.65728759765625, "b": 404.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 332]}], "orig": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 390.8163757324219, "r": 318.8122863769531, "b": 291.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 477]}], "orig": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 278.3163757324219, "r": 313.91229248046875, "b": 254.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 72.0, "t": 71.80239868164062, "r": 184.14828491210938, "b": 60.99040222167969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Figure 7-26. Self-locking nuts.", "text": "Figure 7-26. Self-locking nuts."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 102.4155, "t": 186.23509, "r": 161.3187, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Boots aircraft nut", "text": "Boots aircraft nut"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 91.685997, "t": 94.690201, "r": 129.77399, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Flexloc nut", "text": "Flexloc nut"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 162.48109, "t": 94.690201, "r": 207.85629, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Fiber locknut", "text": "Fiber locknut"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 237.31379999999996, "t": 94.690201, "r": 289.561, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic stop nut", "text": "Elastic stop nut"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 216.9326, "t": 186.23509, "r": 277.7966, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Elastic anchor nut", "text": "Elastic anchor nut"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.9923095703125, "t": 730.3163452148438, "r": 561.808349609375, "b": 656.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 368]}], "orig": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "text": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 655.3163452148438, "r": 325.99542236328125, "b": 643.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ".", "text": "."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 642.6864013671875, "r": 450.99542236328125, "b": 631.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Stainless Steel Self-Locking Nut", "text": "Stainless Steel Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 628.3163452148438, "r": 568.00439453125, "b": 416.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1015]}], "orig": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 403.1863708496094, "r": 388.50543212890625, "b": 391.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic Stop Nut", "text": "Elastic Stop Nut", "level": 1}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 388.8163757324219, "r": 552.351318359375, "b": 364.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 321.0, "t": 73.82240295410156, "r": 481.6493225097656, "b": 63.01040267944336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Figure 7-27. Stainless steel self-locking nut.", "text": "Figure 7-27. Stainless steel self-locking nut."}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.1354999999999, "t": 101.2654, "r": 531.16748, "b": 91.35340099999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Tightened nut", "text": "Tightened nut"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 474.3699, "t": 242.1082, "r": 535.23389, "b": 232.1962000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Untightened nut", "text": "Untightened nut"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 456.7558900000001, "t": 342.00259, "r": 487.08388999999994, "b": 332.3866, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Nut case", "text": "Nut case"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.62299, "t": 196.17650000000003, "r": 497.47183000000007, "b": 186.56050000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Threaded nut core", "text": "Threaded nut core"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 448.55081, "t": 220.6794, "r": 507.686, "b": 211.0634, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Locking shoulder", "text": "Locking shoulder"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 424.78421, "t": 109.88840000000005, "r": 452.10339000000005, "b": 100.27240000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Keyway", "text": "Keyway"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 537.9854125976562, "t": 46.01969909667969, "r": 560.775390625, "b": 33.70970153808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "7-45", "text": "7-45"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 70.59269714355469, "t": 242.77777099609375, "r": 309.863037109375, "b": 79.6090087890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "captions": [{"cref": "#/texts/7"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/20"}, {"cref": "#/texts/21"}, {"cref": "#/texts/22"}, {"cref": "#/texts/23"}, {"cref": "#/texts/24"}, {"cref": "#/texts/25"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 320.4467468261719, "t": 352.359375, "r": 558.8576049804688, "b": 81.689208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "captions": [{"cref": "#/texts/19"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 594.0, "height": 774.0}, "image": null, "page_no": 1}}} \ No newline at end of file +{"schema_name": "DoclingDocument", "version": "1.1.0", "name": "amt_handbook_sample", "origin": {"mimetype": "application/pdf", "binary_hash": 10189692113572347872, "filename": "amt_handbook_sample.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": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/pictures/0"}, {"cref": "#/texts/13"}, {"cref": "#/texts/14"}, {"cref": "#/texts/15"}, {"cref": "#/texts/16"}, {"cref": "#/texts/17"}, {"cref": "#/texts/18"}, {"cref": "#/texts/19"}, {"cref": "#/pictures/1"}, {"cref": "#/texts/26"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99212646484375, "t": 730.3163452148438, "r": 314.11212158203125, "b": 681.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 244]}], "orig": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 667.8163452148438, "r": 313.15460205078125, "b": 593.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 580.1864013671875, "r": 167.27230834960938, "b": 568.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Boots Self-Locking Nut", "text": "Boots Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 565.8163452148438, "r": 318.49224853515625, "b": 491.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 319]}], "orig": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 478.3163757324219, "r": 316.65728759765625, "b": 404.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 332]}], "orig": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 390.8163757324219, "r": 318.8122863769531, "b": 291.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 477]}], "orig": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 278.3163757324219, "r": 313.91229248046875, "b": 254.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 72.0, "t": 71.80239868164062, "r": 184.14828491210938, "b": 60.99040222167969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Figure 7-26. Self-locking nuts.", "text": "Figure 7-26. Self-locking nuts."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 102.4155, "t": 186.23509, "r": 161.3187, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Boots aircraft nut", "text": "Boots aircraft nut"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 91.685997, "t": 94.690201, "r": 129.77399, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Flexloc nut", "text": "Flexloc nut"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 162.48109, "t": 94.690201, "r": 207.85629, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Fiber locknut", "text": "Fiber locknut"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 237.31379999999996, "t": 94.690201, "r": 289.561, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic stop nut", "text": "Elastic stop nut"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 216.9326, "t": 186.23509, "r": 277.7966, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Elastic anchor nut", "text": "Elastic anchor nut"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.9923095703125, "t": 730.3163452148438, "r": 561.808349609375, "b": 656.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 368]}], "orig": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "text": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 655.3163452148438, "r": 325.99542236328125, "b": 643.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ".", "text": "."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 642.6864013671875, "r": 450.99542236328125, "b": 631.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Stainless Steel Self-Locking Nut", "text": "Stainless Steel Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 628.3163452148438, "r": 568.00439453125, "b": 416.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1015]}], "orig": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 403.1863708496094, "r": 388.50543212890625, "b": 391.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic Stop Nut", "text": "Elastic Stop Nut", "level": 1}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 388.8163757324219, "r": 552.351318359375, "b": 364.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 321.0, "t": 73.82240295410156, "r": 481.6493225097656, "b": 63.01040267944336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Figure 7-27. Stainless steel self-locking nut.", "text": "Figure 7-27. Stainless steel self-locking nut."}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.1354999999999, "t": 101.2654, "r": 531.16748, "b": 91.35340099999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Tightened nut", "text": "Tightened nut"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 474.3699, "t": 242.1082, "r": 535.23389, "b": 232.1962000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Untightened nut", "text": "Untightened nut"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 456.7558900000001, "t": 342.00259, "r": 487.08388999999994, "b": 332.3866, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Nut case", "text": "Nut case"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.62299, "t": 196.17650000000003, "r": 497.47183000000007, "b": 186.56050000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Threaded nut core", "text": "Threaded nut core"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 448.55081, "t": 220.6794, "r": 507.686, "b": 211.0634, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Locking shoulder", "text": "Locking shoulder"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 424.78421, "t": 109.88840000000005, "r": 452.10339000000005, "b": 100.27240000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Keyway", "text": "Keyway"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 537.9854125976562, "t": 46.01969909667969, "r": 560.775390625, "b": 33.70970153808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "7-45", "text": "7-45"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 70.59269714355469, "t": 242.77777099609375, "r": 309.863037109375, "b": 79.6090087890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "captions": [{"cref": "#/texts/7"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/20"}, {"cref": "#/texts/21"}, {"cref": "#/texts/22"}, {"cref": "#/texts/23"}, {"cref": "#/texts/24"}, {"cref": "#/texts/25"}], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 320.4467468261719, "t": 352.359375, "r": 558.8576049804688, "b": 81.689208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "captions": [{"cref": "#/texts/19"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 594.0, "height": 774.0}, "image": null, "page_no": 1}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/elife-56337.xml.json b/tests/data/groundtruth/docling_v2/elife-56337.xml.json index 63b89d7..61a40e1 100644 --- a/tests/data/groundtruth/docling_v2/elife-56337.xml.json +++ b/tests/data/groundtruth/docling_v2/elife-56337.xml.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "elife-56337", "origin": { "mimetype": "application/xml", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -59,6 +60,7 @@ "$ref": "#/texts/63" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -238,6 +240,7 @@ "$ref": "#/texts/120" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -277,6 +280,7 @@ "$ref": "#/texts/64" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage", @@ -288,6 +292,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States", @@ -303,6 +308,7 @@ "$ref": "#/texts/3" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Abstract", @@ -315,6 +321,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The Kr\u00fcppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.", @@ -333,6 +340,7 @@ "$ref": "#/texts/6" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Introduction", @@ -345,6 +353,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Nearly half of the human and mouse genomes consist of transposable elements (TEs). TEs shape the evolution of species, serving as a source for genetic innovation (Chuong et al., 2016; Frank and Feschotte, 2017). However, TEs also potentially harm their hosts by insertional mutagenesis, gene deregulation and activation of innate immunity (Maksakova et al., 2006; Kano et al., 2007; Brodziak et al., 2012; Hancks and Kazazian, 2016). To protect themselves from TE activity, host organisms have developed a wide range of defense mechanisms targeting virtually all steps of the TE life cycle (Dewannieux and Heidmann, 2013). In tetrapods, KRAB zinc finger protein (KRAB-ZFP) genes have amplified and diversified, likely in response to TE colonization (Thomas and Schneider, 2011; Najafabadi et al., 2015; Wolf et al., 2015a; Wolf et al., 2015b; Imbeault et al., 2017). Conventional ZFPs bind DNA using tandem arrays of C2H2 zinc finger domains, each capable of specifically interacting with three nucleotides, whereas some zinc fingers can bind two or four nucleotides and include DNA backbone interactions depending on target DNA structure (Patel et al., 2018). This allows KRAB-ZFPs to flexibly bind to large stretches of DNA with high affinity. The KRAB domain binds the corepressor KAP1, which in turn recruits histone modifying enzymes including the NuRD histone deacetylase complex and the H3K9-specific methylase SETDB1 (Schultz et al., 2002; Sripathy et al., 2006), which induces persistent and heritable gene silencing (Groner et al., 2010). Deletion of KAP1 (Rowe et al., 2010) or SETDB1 (Matsui et al., 2010) in mouse embryonic stem (ES) cells induces TE reactivation and cell death, but only minor phenotypes in differentiated cells, suggesting KRAB-ZFPs are most important during early embryogenesis where they mark TEs for stable epigenetic silencing that persists through development. However, SETDB1-containing complexes are also required to repress TEs in primordial germ cells (Liu et al., 2014) and adult tissues (Ecco et al., 2016), indicating KRAB-ZFPs are active beyond early development.", @@ -356,6 +365,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes.", @@ -380,6 +390,7 @@ "$ref": "#/texts/19" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Results", @@ -408,6 +419,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Mouse KRAB-ZFPs target retrotransposons", @@ -420,6 +432,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1\u2014figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1\u2014figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1\u2014figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017).", @@ -431,6 +444,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to\u00a0~240 and~100 copies in the reference C57BL/6 genome, respectively, with\u00a0~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1\u2014figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1\u2014figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).", @@ -442,6 +456,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5\u2019 end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1\u2014figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1\u2014figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.", @@ -453,6 +468,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1\u2014figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3\u2019 end of the internal region of ETn (Figure 1\u2014figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation.", @@ -464,6 +480,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence of new KRAB-ZFPs and binding patterns. The Chr4-cl KRAB-ZFPs REX2 and ZFP600 bind to the same target within ETn but with varying affinity (Figure 1C). Comparison of the amino acids responsible for DNA contact revealed a high similarity between REX2 and ZFP600, with the main differences at the most C-terminal zinc fingers. Additionally, we found that GM30910, another KRAB-ZFP encoded in the Chr4-cl, also shows a strong similarity to both KRAB-ZFPs yet targets entirely different groups of TEs (Figure 1C and Supplementary file 1). Together with previously shown data (Ecco et al., 2016), this example highlights how addition of a few new zinc fingers to an existing array can entirely shift the mode of DNA binding.", @@ -479,6 +496,7 @@ "$ref": "#/texts/15" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Genetic deletion of KRAB-ZFP gene clusters leads to retrotransposon reactivation", @@ -491,6 +509,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2\u2014figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018).", @@ -509,6 +528,7 @@ "$ref": "#/texts/18" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "KRAB-ZFP cluster deletions license TE-borne enhancers", @@ -521,6 +541,7 @@ "$ref": "#/texts/16" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes.", @@ -532,6 +553,7 @@ "$ref": "#/texts/16" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2\u2014figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3\u2019 end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.", @@ -562,6 +584,7 @@ "$ref": "#/texts/25" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "ETn retrotransposition in Chr4-cl KO and WT mice", @@ -574,6 +597,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nell\u00e5ker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4\u2014figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4\u2014figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability.", @@ -585,6 +609,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4\u2014figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1\u2014figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5\u2019 and 3\u2019 ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate.", @@ -596,6 +621,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4\u2014figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A).", @@ -607,6 +633,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4\u2014figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5\u2019 and 3\u2019 LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5\u2019 end of the internal region. Additionally, the 5\u2019 and 3\u2019 LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element.", @@ -618,6 +645,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4\u2014figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4\u2014figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4\u2014figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4\u2014figure supplement 3D).", @@ -629,6 +657,7 @@ "$ref": "#/texts/19" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Finally, we asked whether there were more novel ETn insertions in mice lacking the Chr4-cl relative to their wild type and heterozygous littermates in our pedigree. Interestingly, only one out of the eight Chr4-cl KO mice in a pure C57BL/6 strain background and none of the eight offspring from a Chr2-cl mating carried a single novel ETn insertion (Figure 4A). When crossing into a 129Sv background for a single generation before intercrossing heterozygous mice (F1), we observed 4 out of 8 Chr4-cl KO mice that contained at least one new ETn insertion, whereas none of 3 heterozygous mice contained any insertions. After crossing to the 129Sv background for a second generation (F2), we determined the number of novel ETn insertions in the offspring of one KO/WT x KO and two KO/WT x KO/WT matings, excluding all samples that were not derived from juvenile tail tissue. Only in the offspring of the KO/WT x KO mating, we observed a statistically significant higher average number of ETn insertions in KO vs. KO/WT animals (7.3 vs. 29.6, p=0.045, Figure 4B). Other than that, only a non-significant trend towards greater average numbers of ETn insertions in KO (11 vs. 27.8, p=0.192, Figure 4B) was apparent in one of the WT/KO x KO/WT matings whereas no difference in ETn insertion numbers between WT and KO mice could be observed in the second mating WT/KO x KO/WT (26 vs. 31, p=0.668, Figure 4B). When comparing all KO with all WT and WT/KO mice from these three matings, a trend towards more ETn insertions in KO remained but was not supported by strong significance (26 vs. 13, p=0.057, Figure 4B). Altogether, we observed a high variability in the number of new ETn insertions in both KO and WT but our data suggest that the Chr4-cl KRAB-ZFPs may have a modest effect on ETn retrotransposition rates in some mouse strains but other genetic and epigenetic effects clearly also play an important role.", @@ -647,6 +676,7 @@ "$ref": "#/texts/28" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Discussion", @@ -659,6 +689,7 @@ "$ref": "#/texts/26" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "C2H2 zinc finger proteins, about half of which contain a KRAB repressor domain, represent the largest DNA-binding protein family in mammals. Nevertheless, most of these factors have not been investigated using loss-of-function studies. The most comprehensive characterization of human KRAB-ZFPs revealed a strong preference to bind TEs (Imbeault et al., 2017;\u00a0Najafabadi et al., 2015) yet their function remains unknown. In humans, very few TEs are capable of retrotransposition yet many of them, often tens of million years old, are bound by KRAB-ZFPs. While this suggests that human KRAB-ZFPs mainly serve to control TE-borne enhancers and may have potentially transcription-independent functions, we were interested in the biological significance of KRAB-ZFPs in restricting potentially active TEs. The mouse is an ideal model for such studies since the mouse genome contains several active TE families, including IAP, ETn and L1 elements. We found that many of the young KRAB-ZFPs present in the genomic clusters of KRAB-ZFPs on chromosomes 2 and 4, which are highly expressed in a restricted pattern in ES cells, bound redundantly to these three active TE families. In several cases, KRAB-ZFPs bound to functionally constrained sequence elements we and others have demonstrated to be necessary for retrotransposition, including PBS and viral packaging signals. Targeting such sequences may help the host defense system keep pace with rapidly evolving mouse transposons. This provides strong evidence that many young KRAB-ZFPs are indeed expanding in response to TE activity. But do these young KRAB-ZFP genes limit the mobilization of TEs? Despite the large number of polymorphic ETn elements in mouse strains (Nell\u00e5ker et al., 2012) and several reports of phenotype-causing novel ETn germ line insertions, no new ETn insertions were reported in recent screens of C57BL/6 mouse genomes (Richardson et al., 2017; Gagnier et al., 2019), indicating that the overall rate of ETn germ line mobilization in inbred mice is rather low. We have demonstrated that Chr4-cl KRAB-ZFPs control ETn/ETnERV expression in ES cells, but this does not lead to widespread ETn mobility in viable C57BL/6 mice. In contrast, we found numerous novel, including several germ line, ETn insertions in both WT and Chr4-cl KO mice in a C57BL/6 129Sv mixed genetic background, with generally more insertions in KO mice and in mice with more 129Sv DNA. This is consistent with a report detecting ETn insertions in FVB.129 mice (Schauer et al., 2018). Notably, there was a large variation in the number of new insertions in these mice, possibly caused by hyperactive polymorphic ETn insertions that varied from individual to individual, epigenetic variation at ETn insertions between individuals and/or the general stochastic nature of ETn mobilization. Furthermore, recent reports have suggested that KRAB-ZFP gene content is distinct in different strains of laboratory mice (Lilue et al., 2018; Treger et al., 2019), and reduced KRAB-ZFP gene content could contribute to increased activity in individual mice. Although we have yet to find obvious phenotypes in the mice carrying new insertions, novel ETn germ line insertions have been shown to cause phenotypes from short tails (Lugani et al., 2013; Semba et al., 2013; Vlangos et al., 2013) to limb malformation (Kano et al., 2007) and severe morphogenetic defects including polypodia (Lehoczky et al., 2013) depending upon their insertion site.", @@ -670,6 +701,7 @@ "$ref": "#/texts/26" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Despite a lack of widespread ETn activation in Chr4-cl KO mice, it still remains to be determined whether other TEs, like L1, IAP or other LTR retrotransposons are activated in any of the KRAB-ZFP cluster KO mice, which will require the development of additional capture-seq based assays. Notably, two of the heterozygous matings from Chr2-cl KO mice failed to produce viable knockout offspring, which could indicate a TE-reactivation phenotype. It may also be necessary to generate compound homozygous mutants of distinct KRAB-ZFP clusters to eliminate redundancy before TEs become unleashed. The KRAB-ZFP cluster knockouts produced here will be useful reagents to test such hypotheses. In sum, our data supports that a major driver of KRAB-ZFP gene expansion in mice is recent retrotransposon insertions, and that redundancy within the KRAB-ZFP gene family and with other TE restriction pathways provides protection against widespread TE mobility, explaining the non-essential function of the majority of KRAB-ZFP genes.", @@ -709,6 +741,7 @@ "$ref": "#/texts/47" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Materials and methods", @@ -725,6 +758,7 @@ "$ref": "#/texts/31" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Cell lines and transgenic mice", @@ -737,6 +771,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129\u2010 Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 \u00d7 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 \u00d7 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.", @@ -752,6 +787,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Generation of KRAB-ZFP expressing cell lines", @@ -764,6 +800,7 @@ "$ref": "#/texts/32" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 \u00b5g/ml) and resistant clones were pooled and further expanded for ChIP-seq.", @@ -779,6 +816,7 @@ "$ref": "#/texts/35" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion", @@ -791,6 +829,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 \u00b5g of each pX330-gRNA plasmid, 1 \u00b5g pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 \u00b5g/ml) for 24 hr. Individual KO and WT clones were picked 7\u20138 days after nucleofection and expanded for PCR genotyping (Supplementary file 3).", @@ -809,6 +848,7 @@ "$ref": "#/texts/38" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "ChIP-seq analysis", @@ -821,6 +861,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5\u201310 \u00d7 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.", @@ -832,6 +873,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment\u00a0>20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment\u00a0>10) or low (p<1e-5, peak enrichment\u00a0>10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).", @@ -847,6 +889,7 @@ "$ref": "#/texts/40" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Luciferase reporter assays", @@ -859,6 +902,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above.", @@ -874,6 +918,7 @@ "$ref": "#/texts/42" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "RNA-seq analysis", @@ -886,6 +931,7 @@ "$ref": "#/texts/41" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina\u2019s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust().", @@ -901,6 +947,7 @@ "$ref": "#/texts/44" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Reduced representation bisulfite sequencing (RRBS-seq)", @@ -913,6 +960,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ES cell media containing FCS or for one week in 2i media containing vitamin C as described previously (Blaschke et al., 2013). Genomic DNA was purified from WT and Chr4-cl KO ES cells using the Quick-gDNA purification kit (Zymo Research) and bisulfite-converted with the NEXTflex Bisulfite-Seq Kit (Bio Scientific) using Msp1 digestion to fragment DNA. Libraries were sequenced as 50 bp paired-end reads on an Illumina HiSeq. The reads were processed using Trim Galore (--illumina --paired \u2013rrbs) to trim poor quality bases and adaptors. Additionally, the first 5 nt of R2 and the last 3 nt of R1 and R2 were trimmed. Reads were then mapped to the reference genome (mm9) using Bismark (Krueger and Andrews, 2011) to extract methylation calling results. The CpG methylation pattern for each covered CpG dyads (two complementary CG dinucleotides) was calculated using a custom script (Source code 1: get_CpG_ML.pl). For comparison of CpG methylation between WT and Chr4-cl KO ES cells (in serum or 2i + Vitamin C conditions) only CpG sites with at least 10-fold coverage in each sample were considered for analysis.", @@ -928,6 +976,7 @@ "$ref": "#/texts/46" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Retrotransposition assay", @@ -940,6 +989,7 @@ "$ref": "#/texts/45" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 \u00d7 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (\u0394Gm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (\u0394Rex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma).", @@ -955,6 +1005,7 @@ "$ref": "#/texts/48" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Capture-seq screen", @@ -967,6 +1018,7 @@ "$ref": "#/texts/47" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5\u2019 ends of the 5\u2019 LTRs and the 120 bp 3\u2019 ends of the 3\u2019 LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4\u2014source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nell\u00e5ker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.", @@ -985,6 +1037,7 @@ "$ref": "#/tables/1" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Tables", @@ -997,6 +1050,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 1.: * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.", @@ -1008,6 +1062,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Key resources table: ", @@ -1053,6 +1108,7 @@ "$ref": "#/pictures/10" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Figures", @@ -1065,6 +1121,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 1.: Genome-wide binding patterns of mouse KRAB-ZFPs.\n(A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher\u2019s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment\u00a0>20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.\nFigure 1\u2014source data 1.KRAB-ZFP expression in 40 mouse tissues and cell lines (ENCODE).Mean values of replicates are shown as log2 transcripts per million.\nFigure 1\u2014source data 2.Probability heatmap of KRAB-ZFP binding to TEs.Values corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher\u2019s exact test).", @@ -1076,6 +1133,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 1\u2014figure supplement 1.: ES cell-specific expression of KRAB-ZFP gene clusters.\n(A) Heatmap showing expression patterns of mouse KRAB-ZFPs in 40 mouse tissues and cell lines (ENCODE). Heatmap colors indicate gene expression levels in log2 transcripts per million (TPM). The asterisk indicates a group of 30 KRAB-ZFPs that are exclusively expressed in ES cells. (B) Physical location of the genes encoding for the 30 KRAB-ZFPs that are exclusively expressed in ES cells. (C) Phylogenetic (Maximum likelihood) tree of the KRAB domains of mouse KRAB-ZFPs. KRAB-ZFPs encoded on the gene clusters on chromosome 2 and 4 are highlighted. The scale bar at the bottom indicates amino acid substitutions per site.", @@ -1087,6 +1145,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 1\u2014figure supplement 2.: KRAB-ZFP binding motifs and their repression activity.\n(A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR\u00a0<\u00a00.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.", @@ -1098,6 +1157,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 1\u2014figure supplement 3.: KRAB-ZFP binding to ETn retrotransposons.\n(A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).", @@ -1109,6 +1169,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 2.: Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells.\n(A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.\nFigure 2\u2014source data 1.Differential H3K9me3 and KAP1 distribution in WT and KRAB-ZFP cluster KO ES cells at TE families and KRAB-ZFP bound TE insertions.Differential read counts and statistical testing were determined by DESeq2.", @@ -1120,6 +1181,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 2\u2014figure supplement 1.: Epigenetic changes at TEs and TE-borne enhancers in KRAB-ZFP cluster KO ES cells.\n(A) Differential analysis of summative (all individual insertions combined) H3K9me3 enrichment at TE groups in Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells.\u00a0TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in orange (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (B) Top: Schematic view of the Cd59a/Cd59b locus with a 5\u2019 truncated ETn insertion. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). Bottom: Transcriptional activity of a 5 kb fragment with or without fragments of the ETn insertion was tested by luciferase reporter assay in Chr4-cl WT and KO ES cells.", @@ -1131,6 +1193,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 3.: TE-dependent gene activation in KRAB-ZFP cluster KO ES cells.\n(A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5\u2019 truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student\u2019s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.", @@ -1142,6 +1205,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 4.: ETn retrotransposition in Chr4-cl KO mice.\n(A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.\nFigure 4\u2014source data 1.Coordinates of identified novel ETn insertions and supporting capture-seq read counts.Genomic regions indicate cluster of supporting reads.\nFigure 4\u2014source data 2.Sequences of capture-seq probes used to enrich genomic DNA for ETn and MuLV (RLTR4) insertions.", @@ -1153,6 +1217,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 4\u2014figure supplement 1.: Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues.\n(A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds.\u00a0(B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.", @@ -1164,6 +1229,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 4\u2014figure supplement 2.: Identification of polymorphic ETn and MuLV retrotransposon insertions in Chr4-cl KO and WT mice.\nHeatmaps show normalized capture-seq read counts in RPM (Read Per Million) for identified polymorphic ETn (A) and MuLV (B) loci in different mouse strains. Only loci with strong support for germ line ETn or MuLV insertions (at least 100 or 3000 ETn or MuLV RPM, respectively) in at least two animals are shown. Non-polymorphic insertion loci with high read counts in all screened mice were excluded for better visibility. The sample information (sample name and cell type/tissue) is annotated at the bottom, with the strain information indicated by color at the top. The color gradient indicates log10(RPM+1).", @@ -1175,6 +1241,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Figure 4\u2014figure supplement 3.: Confirmation of novel ETn insertions identified by capture-seq.\n(A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).", @@ -1190,6 +1257,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "References", @@ -1202,6 +1270,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "TL Bailey; M Boden; FA Buske; M Frith; CE Grant; L Clementi; J Ren; WW Li; WS Noble. MEME SUITE: tools for motif discovery and searching. 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PNAS (2014)", @@ -1553,6 +1648,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "J Lilue; AG Doran; IT Fiddes; M Abrudan; J Armstrong; R Bennett; W Chow; J Collins; S Collins; A Czechanski; P Danecek; M Diekhans; DD Dolle; M Dunn; R Durbin; D Earl; A Ferguson-Smith; P Flicek; J Flint; A Frankish; B Fu; M Gerstein; J Gilbert; L Goodstadt; J Harrow; K Howe; X Ibarra-Soria; M Kolmogorov; CJ Lelliott; DW Logan; J Loveland; CE Mathews; R Mott; P Muir; S Nachtweide; FCP Navarro; DT Odom; N Park; S Pelan; SK Pham; M Quail; L Reinholdt; L Romoth; L Shirley; C Sisu; M Sjoberg-Herrera; M Stanke; C Steward; M Thomas; G Threadgold; D Thybert; J Torrance; K Wong; J Wood; B Yalcin; F Yang; DJ Adams; B Paten; TM Keane. Sixteen diverse laboratory mouse reference genomes define strain-specific haplotypes and novel functional loci. Nature Genetics (2018)", @@ -1566,6 +1662,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "S Liu; J Brind'Amour; MM Karimi; K Shirane; A Bogutz; L Lefebvre; H Sasaki; Y Shinkai; MC Lorincz. Setdb1 is required for germline development and silencing of H3K9me3-marked endogenous retroviruses in primordial germ cells. Genes & Development (2014)", @@ -1579,6 +1676,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "MI Love; W Huber; S Anders. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology (2014)", @@ -1592,6 +1690,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "F Lugani; R Arora; N Papeta; A Patel; Z Zheng; R Sterken; RA Singer; G Caridi; C Mendelsohn; L Sussel; VE Papaioannou; AG Gharavi. A retrotransposon insertion in the 5' regulatory domain of Ptf1a results in ectopic gene expression and multiple congenital defects in Danforth's short tail mouse. PLOS Genetics (2013)", @@ -1605,6 +1704,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "TS Macfarlan; WD Gifford; S Driscoll; K Lettieri; HM Rowe; D Bonanomi; A Firth; O Singer; D Trono; SL Pfaff. Embryonic stem cell potency fluctuates with endogenous retrovirus activity. Nature (2012)", @@ -1618,6 +1718,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "IA Maksakova; MT Romanish; L Gagnier; CA Dunn; LN van de Lagemaat; DL Mager. Retroviral elements and their hosts: insertional mutagenesis in the mouse germ line. PLOS Genetics (2006)", @@ -1631,6 +1732,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "T Matsui; D Leung; H Miyashita; IA Maksakova; H Miyachi; H Kimura; M Tachibana; MC Lorincz; Y Shinkai. Proviral silencing in embryonic stem cells requires the histone methyltransferase ESET. Nature (2010)", @@ -1644,6 +1746,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "HS Najafabadi; S Mnaimneh; FW Schmitges; M Garton; KN Lam; A Yang; M Albu; MT Weirauch; E Radovani; PM Kim; J Greenblatt; BJ Frey; TR Hughes. C2H2 zinc finger proteins greatly expand the human regulatory lexicon. Nature Biotechnology (2015)", @@ -1657,6 +1760,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "C Nell\u00e5ker; TM Keane; B Yalcin; K Wong; A Agam; TG Belgard; J Flint; DJ Adams; WN Frankel; CP Ponting. The genomic landscape shaped by selection on transposable elements across 18 mouse strains. Genome Biology (2012)", @@ -1670,6 +1774,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "H O'Geen; S Frietze; PJ Farnham. Using ChIP-seq technology to identify targets of zinc finger transcription factors. Methods in Molecular Biology (2010)", @@ -1683,6 +1788,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "A Patel; P Yang; M Tinkham; M Pradhan; M-A Sun; Y Wang; D Hoang; G Wolf; JR Horton; X Zhang; T Macfarlan; X Cheng. DNA conformation induces adaptable binding by tandem zinc finger proteins. Cell (2018)", @@ -1696,6 +1802,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "D Ribet; M Dewannieux; T Heidmann. An active murine transposon family pair: retrotransposition of \"master\" MusD copies and ETn trans-mobilization. Genome Research (2004)", @@ -1709,6 +1816,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "SR Richardson; P Gerdes; DJ Gerhardt; FJ Sanchez-Luque; GO Bodea; M Mu\u00f1oz-Lopez; JS Jesuadian; MHC Kempen; PE Carreira; JA Jeddeloh; JL Garcia-Perez; HH Kazazian; AD Ewing; GJ Faulkner. Heritable L1 retrotransposition in the mouse primordial germline and early embryo. Genome Research (2017)", @@ -1722,6 +1830,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "HM Rowe; J Jakobsson; D Mesnard; J Rougemont; S Reynard; T Aktas; PV Maillard; H Layard-Liesching; S Verp; J Marquis; F Spitz; DB Constam; D Trono. KAP1 controls endogenous retroviruses in embryonic stem cells. Nature (2010)", @@ -1735,6 +1844,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "HM Rowe; A Kapopoulou; A Corsinotti; L Fasching; TS Macfarlan; Y Tarabay; S Viville; J Jakobsson; SL Pfaff; D Trono. TRIM28 repression of retrotransposon-based enhancers is necessary to preserve transcriptional dynamics in embryonic stem cells. Genome Research (2013)", @@ -1748,6 +1858,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "SN Schauer; PE Carreira; R Shukla; DJ Gerhardt; P Gerdes; FJ Sanchez-Luque; P Nicoli; M Kindlova; S Ghisletti; AD Santos; D Rapoud; D Samuel; J Faivre; AD Ewing; SR Richardson; GJ Faulkner. L1 retrotransposition is a common feature of mammalian hepatocarcinogenesis. Genome Research (2018)", @@ -1761,6 +1872,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "DC Schultz; K Ayyanathan; D Negorev; GG Maul; FJ Rauscher. SETDB1: a novel KAP-1-associated histone H3, lysine 9-specific methyltransferase that contributes to HP1-mediated silencing of euchromatic genes by KRAB zinc-finger proteins. Genes & Development (2002)", @@ -1774,6 +1886,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "K Semba; K Araki; K Matsumoto; H Suda; T Ando; A Sei; H Mizuta; K Takagi; M Nakahara; M Muta; G Yamada; N Nakagata; A Iida; S Ikegawa; Y Nakamura; M Araki; K Abe; K Yamamura. Ectopic expression of Ptf1a induces spinal defects, urogenital defects, and anorectal malformations in Danforth's short tail mice. PLOS Genetics (2013)", @@ -1787,6 +1900,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "SP Sripathy; J Stevens; DC Schultz. The KAP1 corepressor functions to coordinate the assembly of de novo HP1-demarcated microenvironments of heterochromatin required for KRAB zinc finger protein-mediated transcriptional repression. Molecular and Cellular Biology (2006)", @@ -1800,6 +1914,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "JH Thomas; S Schneider. Coevolution of retroelements and tandem zinc finger genes. Genome Research (2011)", @@ -1813,6 +1928,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "PJ Thompson; TS Macfarlan; MC Lorincz. Long terminal repeats: from parasitic elements to building blocks of the transcriptional regulatory repertoire. Molecular Cell (2016)", @@ -1826,6 +1942,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "RS Treger; SD Pope; Y Kong; M Tokuyama; M Taura; A Iwasaki. The lupus susceptibility locus Sgp3 encodes the suppressor of endogenous retrovirus expression SNERV. Immunity (2019)", @@ -1839,6 +1956,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "CN Vlangos; AN Siuniak; D Robinson; AM Chinnaiyan; RH Lyons; JD Cavalcoli; CE Keegan. Next-generation sequencing identifies the Danforth's short tail mouse mutation as a retrotransposon insertion affecting Ptf1a expression. PLOS Genetics (2013)", @@ -1852,6 +1970,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "J Wang; G Xie; M Singh; AT Ghanbarian; T Rask\u00f3; A Szvetnik; H Cai; D Besser; A Prigione; NV Fuchs; GG Schumann; W Chen; MC Lorincz; Z Ivics; LD Hurst; Z Izsv\u00e1k. Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells. Nature (2014)", @@ -1865,6 +1984,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "D Wolf; K Hug; SP Goff. TRIM28 mediates primer binding site-targeted silencing of Lys1,2 tRNA-utilizing retroviruses in embryonic cells. PNAS (2008)", @@ -1878,6 +1998,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "G Wolf; D Greenberg; TS Macfarlan. Spotting the enemy within: targeted silencing of foreign DNA in mammalian genomes by the Kr\u00fcppel-associated box zinc finger protein family. Mobile DNA (2015a)", @@ -1891,6 +2012,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "G Wolf; P Yang; AC F\u00fcchtbauer; EM F\u00fcchtbauer; AM Silva; C Park; W Wu; AL Nielsen; FS Pedersen; TS Macfarlan. The KRAB zinc finger protein ZFP809 is required to initiate epigenetic silencing of endogenous retroviruses. Genes & Development (2015b)", @@ -1904,6 +2026,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "M Yamauchi; B Freitag; C Khan; B Berwin; E Barklis. Stem cell factor binding to retrovirus primer binding site silencers. Journal of Virology (1995)", @@ -1917,6 +2040,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Y Zhang; T Liu; CA Meyer; J Eeckhoute; DS Johnson; BE Bernstein; C Nusbaum; RM Myers; M Brown; W Li; XS Liu. Model-based analysis of ChIP-Seq (MACS). Genome Biology (2008)", @@ -1932,6 +2056,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1949,6 +2074,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1966,6 +2092,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1983,6 +2110,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2000,6 +2128,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2017,6 +2146,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2034,6 +2164,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2051,6 +2182,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2068,6 +2200,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2085,6 +2218,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2102,6 +2236,7 @@ "$ref": "#/texts/52" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2121,6 +2256,7 @@ "$ref": "#/texts/49" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -3243,6 +3379,7 @@ "$ref": "#/texts/49" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ diff --git a/tests/data/groundtruth/docling_v2/example_01.html.json b/tests/data/groundtruth/docling_v2/example_01.html.json index 044287c..cff4f95 100644 --- a/tests/data/groundtruth/docling_v2/example_01.html.json +++ b/tests/data/groundtruth/docling_v2/example_01.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "example_01", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -37,6 +39,7 @@ "$ref": "#/texts/5" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -53,6 +56,7 @@ "$ref": "#/texts/7" } ], + "content_layer": "body", "name": "ordered list", "label": "ordered_list" } @@ -71,6 +75,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Introduction", @@ -82,6 +87,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "This is the first paragraph of the introduction.", @@ -106,6 +112,7 @@ "$ref": "#/groups/1" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Background", @@ -118,6 +125,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some background information here.", @@ -129,6 +137,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in unordered list", @@ -142,6 +151,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in unordered list", @@ -155,6 +165,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in ordered list", @@ -168,6 +179,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in ordered list", @@ -183,6 +195,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/example_02.html.json b/tests/data/groundtruth/docling_v2/example_02.html.json index 0dbff9f..321032f 100644 --- a/tests/data/groundtruth/docling_v2/example_02.html.json +++ b/tests/data/groundtruth/docling_v2/example_02.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "example_02", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -37,6 +39,7 @@ "$ref": "#/texts/5" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -53,6 +56,7 @@ "$ref": "#/texts/7" } ], + "content_layer": "body", "name": "ordered list", "label": "ordered_list" } @@ -71,6 +75,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Introduction", @@ -82,6 +87,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "This is the first paragraph of the introduction.", @@ -103,6 +109,7 @@ "$ref": "#/groups/1" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Background", @@ -115,6 +122,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some background information here.", @@ -126,6 +134,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in unordered list", @@ -139,6 +148,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in unordered list", @@ -152,6 +162,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in ordered list", @@ -165,6 +176,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in ordered list", diff --git a/tests/data/groundtruth/docling_v2/example_03.html.json b/tests/data/groundtruth/docling_v2/example_03.html.json index 206048d..1df23c6 100644 --- a/tests/data/groundtruth/docling_v2/example_03.html.json +++ b/tests/data/groundtruth/docling_v2/example_03.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "example_03", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -37,6 +39,7 @@ "$ref": "#/texts/8" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -53,6 +56,7 @@ "$ref": "#/texts/7" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -69,6 +73,7 @@ "$ref": "#/texts/12" } ], + "content_layer": "body", "name": "ordered list", "label": "ordered_list" }, @@ -85,6 +90,7 @@ "$ref": "#/texts/11" } ], + "content_layer": "body", "name": "ordered list", "label": "ordered_list" } @@ -106,6 +112,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Example Document", @@ -121,6 +128,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Introduction", @@ -133,6 +141,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "This is the first paragraph of the introduction.", @@ -154,6 +163,7 @@ "$ref": "#/groups/2" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Background", @@ -166,6 +176,7 @@ "$ref": "#/texts/3" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some background information here.", @@ -181,6 +192,7 @@ "$ref": "#/groups/1" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in unordered list", @@ -194,6 +206,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nested item 1", @@ -207,6 +220,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nested item 2", @@ -220,6 +234,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in unordered list", @@ -237,6 +252,7 @@ "$ref": "#/groups/3" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "First item in ordered list", @@ -250,6 +266,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nested ordered item 1", @@ -263,6 +280,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nested ordered item 2", @@ -276,6 +294,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Second item in ordered list", @@ -293,6 +312,7 @@ "$ref": "#/tables/0" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Data Table", @@ -308,6 +328,7 @@ "$ref": "#/texts/13" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/example_04.html.json b/tests/data/groundtruth/docling_v2/example_04.html.json index c7d6af0..ab32c1c 100644 --- a/tests/data/groundtruth/docling_v2/example_04.html.json +++ b/tests/data/groundtruth/docling_v2/example_04.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "example_04", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -35,6 +37,7 @@ "$ref": "#/tables/0" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Data Table with Rowspan and Colspan", @@ -49,6 +52,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/example_05.html.json b/tests/data/groundtruth/docling_v2/example_05.html.json index ae31139..8a67ed9 100644 --- a/tests/data/groundtruth/docling_v2/example_05.html.json +++ b/tests/data/groundtruth/docling_v2/example_05.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "example_05", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -35,6 +37,7 @@ "$ref": "#/tables/0" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Omitted html and body tags", @@ -49,6 +52,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/ipa20180000016.json b/tests/data/groundtruth/docling_v2/ipa20180000016.json index d5d0d3e..5fdbd09 100644 --- a/tests/data/groundtruth/docling_v2/ipa20180000016.json +++ b/tests/data/groundtruth/docling_v2/ipa20180000016.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "ipa20180000016.xml", "origin": { "mimetype": "application/xml", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -68,6 +70,7 @@ "$ref": "#/texts/171" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "LIGHT EMITTING DEVICE AND PLANT CULTIVATION METHOD", @@ -83,6 +86,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "ABSTRACT", @@ -95,6 +99,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Provided is a light emitting device that includes a light emitting element having a light emission peak wavelength ranging from 380 nm to 490 nm, and a fluorescent material excited by light from the light emitting element and emitting light having at a light emission peak wavelength ranging from 580 nm or more to less than 680 nm. The light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B ranging from 2.0 to 4.0 and a ratio R/FR of the photon flux density R to a photon flux density FR ranging from 0.7 to 13.0, the photon flux density R being in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B being in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR being in a wavelength range of 700 nm or more and 780 nm or less.", @@ -110,6 +115,7 @@ "$ref": "#/texts/4" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "CROSS-REFERENCE TO RELATED APPLICATION", @@ -122,6 +128,7 @@ "$ref": "#/texts/3" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The application claims benefit of Japanese Patent Application No. 2016-128835 filed on Jun. 29, 2016, the entire disclosure of which is hereby incorporated by reference in its entirety.", @@ -133,6 +140,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "BACKGROUND", @@ -149,6 +157,7 @@ "$ref": "#/texts/7" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Technical Field", @@ -161,6 +170,7 @@ "$ref": "#/texts/6" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The present disclosure relates to a light emitting device and a plant cultivation method.", @@ -188,6 +198,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Description of Related Art", @@ -200,6 +211,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "With environmental changes due to climate change and other artificial disruptions, plant factories are expected to increase production efficiency of vegetables and be capable of adjusting production in order to make it possible to stably supply vegetables. Plant factories that are capable of artificial management can stably supply clean and safe vegetables to markets, and therefore are expected to be the next-generation industries.", @@ -211,6 +223,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Plant factories that are completely isolated from external environment make it possible to artificially control and collect various data such as growth method, growth rate data, yield data, depending on classification of plants. Based on those data, plant factories are able to plan production according to the balance between supply and demand in markets, and supply plants such as vegetables without depending on surrounding conditions such as climatic environment. Particularly, an increase in food production is indispensable with world population growth. If plants can be systematically produced without the influence by surrounding conditions such as climatic environment, vegetables produced in plant factories can be stably supplied within a country, and additionally can be exported abroad as viable products.", @@ -222,6 +235,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "In general, vegetables that are grown outdoors get sunlight, grow while conducting photosynthesis, and are gathered. On the other hand, vegetables that are grown in plant factories are required to be harvested in a short period of time, or are required to grow in larger than normal sizes even in an ordinary growth period.", @@ -233,6 +247,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "In plant factories, the light source used in place of sunlight affect a growth period, growth of plants. LED lighting is being used in place of conventional fluorescent lamps, from a standpoint of power consumption reduction.", @@ -244,6 +259,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "For example, Japanese Unexamined Patent Publication No. 2009-125007 discloses a plant growth method. In this method, the plants is irradiated with light emitted from a first LED light emitting element and/or a second LED light emitting element at predetermined timings using a lighting apparatus including the first LED light emitting element emitting light having a wavelength region of 625 to 690 nm and the second LED light emitting element emitting light having a wavelength region of 420 to 490 nm in order to emit lights having sufficient intensities and different wavelengths from each other.", @@ -274,6 +290,7 @@ "$ref": "#/texts/20" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "SUMMARY", @@ -286,6 +303,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "However, even though plants are merely irradiated with lights having different wavelengths as in the plant growth method disclosed in Japanese Unexamined Patent Publication No. 2009-125007, the effect of promoting plant growth is not sufficient. Further improvement is required in promotion of plant growth.", @@ -297,6 +315,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Accordingly, an object of the present disclosure is to provide a light emitting device capable of promoting growth of plants and a plant cultivation method.", @@ -308,6 +327,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Means for solving the above problems are as follows, and the present disclosure includes the following embodiments.", @@ -319,6 +339,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A first embodiment of the present disclosure is a light emitting device including a light emitting element having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less, and a fluorescent material that is excited by light from the light emitting element and emits light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm. The light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B within a range of 2.0 or more and 4.0 or less, and a ratio R/FR of a photon flux density R to a photon flux density FR within a range of 0.7 or more and 13.0 or less, where the photon flux density R is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 700 nm or more and 780 nm or less.", @@ -330,6 +351,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A second embodiment of the present disclosure is a plant cultivation method including irradiating plants with light from the light emitting device.", @@ -341,6 +363,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "According to embodiments of the present disclosure, a light emitting device capable of promoting growth of plants and a plant cultivation method can be provided.", @@ -365,6 +388,7 @@ "$ref": "#/texts/25" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "BRIEF DESCRIPTION OF THE DRAWINGS", @@ -377,6 +401,7 @@ "$ref": "#/texts/21" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "FIG. 1 is a schematic cross sectional view of a light emitting device according to an embodiment of the present disclosure.", @@ -388,6 +413,7 @@ "$ref": "#/texts/21" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "FIG. 2 is a diagram showing spectra of wavelengths and relative photon flux densities of exemplary light emitting devices according to embodiments of the present disclosure and a comparative light emitting devices.", @@ -399,6 +425,7 @@ "$ref": "#/texts/21" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "FIG. 3 is a graph showing fresh weight (edible part) at the harvest time of each plant grown by irradiating the plant with light from exemplary light emitting devices according to embodiments of the present disclosure and a comparative light emitting device.", @@ -410,6 +437,7 @@ "$ref": "#/texts/21" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "FIG. 4 is a graph showing nitrate nitrogen content in each plant grown by irradiating the plant with light from exemplary light emitting devices according to embodiments of the present disclosure and a comparative light emitting device.", @@ -458,6 +486,7 @@ "$ref": "#/texts/126" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "DETAILED DESCRIPTION", @@ -470,6 +499,7 @@ "$ref": "#/texts/26" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A light emitting device and a plant cultivation method according to the present invention will be described below based on an embodiment. However, the embodiment described below only exemplifies the technical concept of the present invention, and the present invention is not limited to the light emitting device and plant cultivation method described below. In the present specification, the relationship between the color name and the chromaticity coordinate, the relationship between the wavelength range of light and the color name of monochromatic light follows JIS Z8110.", @@ -524,6 +554,7 @@ "$ref": "#/texts/42" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Light Emitting Device", @@ -536,6 +567,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "An embodiment of the present disclosure is a light emitting device including a light emitting element having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less (hereinafter sometimes referred to as a \u201cregion of from near ultraviolet to blue color\u201d), and a first fluorescent material emitting light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm by being excited by light from the light emitting element. The light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B within a range of 2.0 or more and 4.0 or less, and a ratio R/FR of the photon flux density R to a photon flux density FR within a range of 0.7 or more and 13.0 or less, where the photon flux density R is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 700 nm or more and 780 nm or less.", @@ -547,6 +579,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "An example of the light emitting device according to one embodiment of the present disclosure is described below based on the drawings. FIG. 1 is a schematic cross sectional view showing a light emitting device 100 according to an embodiment of the present disclosure.", @@ -558,6 +591,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The light emitting device 100 includes a molded article 40, a light emitting element 10 and a fluorescent member 50, as shown in FIG. 1. The molded article 40 includes a first lead 20 and a second lead 30 that are integrally molded with a resin portion 42 containing a thermoplastic resin or a thermosetting resin. The molded article 40 forms a depression having a bottom and sides, and the light emitting element 10 is placed on the bottom of the depression. The light emitting element 10 has a pair of an anode and a cathode, and the anode and the cathode are electrically connected to the first lead 20 and the second lead 30 respectively through the respective wires 60. The light emitting element 10 is covered with the fluorescent member 50. The fluorescent member 50 includes, for example, a fluorescent material 70 performing wavelength conversion of light from the light emitting element 10, and a resin. The fluorescent material 70 includes a first fluorescent material 71 and a second fluorescent material 72. A part of the first lead 20 and the second lead 30 that are connected to a pair of the anode and the cathode of the light emitting element 10 is exposed toward outside a package constituting the light emitting element 100. The light emitting device 100 can emit light by receiving electric power supply from the outside through the first lead 20 and the second lead 30.", @@ -569,6 +603,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The fluorescent member 50 not only performs wavelength conversion of light emitted from the light emitting element 10, but functions as a member for protecting the light emitting element 10 from the external environment. In FIG. 1, the fluorescent material 70 is localized in the fluorescent member 50 in the state that the first fluorescent material 71 and the second fluorescent material 72 are mixed with each other, and is arranged adjacent to the light emitting element 10. This constitution can efficiently perform the wavelength conversion of light from the light emitting element 10 in the fluorescent material 70, and as a result, can provide a light emitting device having excellent light emission efficiency. The arrangement of the fluorescent member 50 containing the fluorescent material 70, and the light emitting element 10 is not limited to the embodiment that the fluorescent material 70 is arranged adjacent to the light emitting element 10 as shown in FIG. 1, and considering the influence of heat generated from the light emitting element 10, the fluorescent material 70 can be arranged separated from the light emitting element 10 in the fluorescent member 50. Furthermore, light having suppressed color unevenness can be emitted from the light emitting device 100 by arranging the fluorescent material 70 almost evenly in the fluorescent member 50. In FIG. 1, the fluorescent material 70 is arranged in the state that the first fluorescent material 71 and the second fluorescent material 72 are mixed with each other. However, for example, the first fluorescent material 71 may be arranged in a layer state and the second fluorescent material 72 may be arranged thereon in another layer state. Alternatively, the second fluorescent material 72 may be arranged in a layer state and the first fluorescent material 71 may be arranged thereon in another layer state.", @@ -580,6 +615,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The light emitting device 100 includes the first fluorescent material 71 having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm by being excited by light from the light emitting element 10, and preferably further includes the second fluorescent material 72 having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less by being excited by light from the light emitting element 10.", @@ -591,6 +627,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The first fluorescent material 71 and the second fluorescent material 72 are contained in, for example, the fluorescent member 50 covering the light emitting element 10. The light emitting device 100 in which the light emitting element 10 has been covered with the fluorescent member 50 containing the first fluorescent material 71 and the second fluorescent material 72 emits light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm by a part of light emission of the light emitting element 10 that is absorbed in the first fluorescent material 71. Furthermore, the light emitting device 100 emits light having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less by a part of light emission of the light emitting element 10 that is absorbed in the second fluorescent material 72.", @@ -602,6 +639,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Plants grow when a pigment (chlorophyll a and chlorophyll b) present in chlorophyll thereof absorbs light and additionally takes carbon dioxide gas and water therein, and these are converted to carbohydrates (saccharides) by photosynthesis. Chlorophyll a and chlorophyll b used in growth promotion of plants particularly have absorption peaks in a red region of 625 nm or more and 675 nm or less and a blue region of 425 nm or more and 475 nm or less. The action of photosynthesis by chlorophylls of plants mainly occurs in a wavelength range of 400 nm or more and 700 nm or less, but chlorophyll a and chlorophyll b further have local absorption peaks in a region of 700 nm or more and 800 nm or less.", @@ -613,6 +651,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "For example, when plants are irradiated with light having longer wavelength than and absorption peak (in the vicinity of 680 nm) in a red region of chlorophyll a, a phenomenon called red drop, in which activity of photosynthesis rapidly decreases, occurs. However, it is known that when plants are irradiated with light containing near infrared region together with light of red region, photosynthesis is accelerated by a synergistic effect of those two kinds of lights. This phenomenon is called the Emerson effect.", @@ -624,6 +663,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Intensity of light with which plants are irradiated is represented by photon flux density. The photon flux density (\u03bcmol\u00b7m\u207b\u00b2\u00b7s\u207b\u00b9) is the number of photons reaching a unit area per unit time. The amount of photosynthesis depends on the number of photons, and therefore does not depend on other optical characteristics if the photon flux density is the same. However, wavelength dependency activating photosynthesis differs depending on photosynthetic pigment. Intensity of light necessary for photosynthesis of plants is sometimes represented by Photosynthetic Photon Flux Density (PPFD).", @@ -635,6 +675,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The light emitting device 100 emits light having a ratio R/B of a photon flux density R to a photon flux density B within a range of 2.0 or more and 4.0 or less, and a ratio R/FR of the photon flux density R to a photon flux density FR within a range of 0.7 or more and 13.0 or less, where the photon flux density R is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR is the number of light quanta (\u03bcmol\u00b7m\u207b\u00b2\u00b7g\u207b\u00b9) incident per unit time and unit area in a wavelength range of 700 nm or more and 780 nm or less.", @@ -646,6 +687,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "It is estimated that in plants, which are irradiated with light containing the photon flux density FR from the light emitting device 100, photosynthesis is activated by Emerson effect, and as a result, growth of plants can be promoted. Furthermore, when plants are irradiated with light containing the photon flux density FR, growth of the plants can be promoted by a reversible reaction between red light irradiation, to which chlorophyll as chromoprotein contained in plants has participated, and far infrared light irradiation.", @@ -657,6 +699,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Examples of nutrients necessary for growth of plants include nitrogen, phosphoric acid, and potassium. Of those nutrients, nitrogen is absorbed in plants as nitrate nitrogen (nitrate ion: NO\u2083\u207b). The nitrate nitrogen changes into nitrite ion (NO\u2082\u207b) by a reduction reaction, and when the nitrite ion is further reacted with fatty acid amine, nitrosoamine is formed. It is known that nitrite ion acts to hemoglobin in blood, and it is known that a nitroso compound sometimes affects health of a human body. Mechanism of converting nitrate nitrogen into nitrite ion in vivo is complicated, and the relationship between the amount of intake of nitrate nitrogen and the influence to health of a human body is not clarified. However, it is desired that the content of nitrate nitrogen having a possibility of affecting health of a human body is smaller.", @@ -668,6 +711,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "For the above reasons, nitrogen is one of nutrients necessary for growth of plants, but it is preferred that the content of nitrate nitrogen in food plants be reduced to a range that does not disturb the growth of plants.", @@ -679,6 +723,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "It is preferred that the light emitting device 100 further include the second fluorescent material 72 having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less by being excited by light from the light emitting element 10, wherein the R/FR ratio is within a range of 0.7 or more and 5.0 or less. The R/FR ratio is more preferably within a range of 0.7 or more and 2.0 or less.", @@ -700,6 +745,7 @@ "$ref": "#/texts/46" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Light Emitting Element", @@ -712,6 +758,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The light emitting element 10 is used as an excitation light source, and is a light emitting element emitting light having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less. As a result, a stable light emitting device having high efficiency, high linearity of output to input and strong mechanical impacts can be obtained.", @@ -723,6 +770,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The range of the light emission peak wavelength of the light emitting element 10 is preferably in a range of 390 nm or more and 480 nm or less, more preferably in a range of 420 nm or more and 470 nm or less, and still more preferably in a range of 440 nm or more and 460 nm or less, and particularly preferably in a range of 445 nm or more and 455 nm or less. A light emitting element including a nitride semiconductor (In\u2093AlyGa\u2081\u208b\u2093\u208byN, 0\u2266X, 0\u2266Y and X+Y\u22661) is preferably used as the light emitting element 10.", @@ -734,6 +782,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The half value width of emission spectrum of the light emitting element 10 can be, for example, 30 nm or less.", @@ -755,6 +804,7 @@ "$ref": "#/texts/50" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Fluorescent Member", @@ -767,6 +817,7 @@ "$ref": "#/texts/47" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The fluorescent member 50 used in the light emitting device 100 preferably includes the first fluorescent material 71 and a sealing material, and more preferably further includes the second fluorescent material 72. A thermoplastic resin and a thermosetting resin can be used as the sealing material. The fluorescent member 50 may contain other components such as a filler, a light stabilizer and a colorant, in addition to the fluorescent material and the sealing material. Examples of the filler include silica, barium titanate, titanium oxide and aluminum oxide.", @@ -778,6 +829,7 @@ "$ref": "#/texts/47" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The content of other components other than the fluorescent material 70 and the sealing material in the fluorescent member 50 is preferably in a range of 0.01 parts by mass or more and 20 parts by mass or less, per 100 parts by mass of the sealing material.", @@ -789,6 +841,7 @@ "$ref": "#/texts/47" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The total content of the fluorescent material 70 in the fluorescent member 50 can be, for example, 5 parts by mass or more and 300 parts by mass or less, per 100 parts by mass of the sealing material. The total content is preferably 10 parts by mass or more and 250 parts by mass or less, more preferably 15 parts by mass or more and 230 parts by mass or less, and still more preferably 15 parts by mass or more and 200 parts by mass or less. When the total content of the fluorescent material 70 in the fluorescent member 50 is within the above range, the light emitted from the light emitting element 10 can be efficiently subjected to wavelength conversion in the fluorescent material 70.", @@ -855,6 +908,7 @@ "$ref": "#/texts/69" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "First Fluorescent Material", @@ -867,6 +921,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The first fluorescent material 71 is a fluorescent material that is excited by light from the light emitting element 10 and emits light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm. Examples of the first fluorescent material 71 include an Mn\u2074\u207a-activated fluorogermanate fluorescent material, an Eu\u00b2\u207a-activated nitride fluorescent material, an Eu\u00b2\u207a-activated alkaline earth sulfide fluorescent material and an Mn\u2074\u207a-activated halide fluorescent material. The first fluorescent material 71 may use one selected from those fluorescent materials and may use a combination of two or more thereof. The first fluorescent material preferably contains an Eu\u00b2\u207a-activated nitride fluorescent material and an Mn\u2074\u207a-activated fluorogermanate fluorescent material.", @@ -878,6 +933,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The Eu\u00b2\u207a-activated nitride fluorescent material is preferably a fluorescent material that has a composition including at least one element selected from Sr and Ca, and Al and contains silicon nitride that is activated by Eu\u00b2\u207a, or a fluorescent material that has a composition including at least one element selected from the group consisting of alkaline earth metal elements and at least one element selected from the group consisting of alkali metal elements and contains aluminum nitride that is activated by Eu\u00b2\u207a.", @@ -889,6 +945,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The halide fluorescent material that is activated by Mn\u2074\u207a is preferably a fluorescent material that has a composition including at least one element or ion selected from the group consisting of alkali metal elements and an ammonium ion (NH\u2074\u207a) and at least one element selected from the group consisting of Group 4 elements and Group 14 elements and contains a fluoride that is activated by Mn\u2074\u207a.", @@ -900,6 +957,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Examples of the first fluorescent material 71 specifically include fluorescent materials having any one composition of the following formulae (I) to (VI).", @@ -911,6 +969,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "(i\u2212j)MgO.(j/2)Sc\u2082O\u2083.kMgF\u2082.mCaF\u2082.(1\u2212n)GeO\u2082.(n/2)Mt\u2082O\u2083:zMn\u2074\u207a (I)", @@ -922,6 +981,7 @@ "$ref": "#/texts/51" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "wherein Mt is at least one selected from the group consisting of Al, Ga, and In, and j, k, m, n, and z are numbers satisfying 2\u2266i\u22664, 0\u2266j<0.5, 00.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].", @@ -944,6 +995,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with \u226565% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.", @@ -955,6 +1007,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48\u201350], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51\u201353].", @@ -966,6 +1019,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the \u201cmother\u201d districts are usually flowed down to these new \u201cdaughter\u201d districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original \u201cmother\u201d district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district.", @@ -977,6 +1031,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey.", @@ -988,6 +1043,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation.", @@ -1006,6 +1062,7 @@ "$ref": "#/tables/1" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Tables", @@ -1018,6 +1075,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 1: Categorization of potential factors influencing pre-TAS results.", @@ -1029,6 +1087,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 2: Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.", @@ -1056,6 +1115,7 @@ "$ref": "#/pictures/4" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Figures", @@ -1068,6 +1128,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 1: Number of pre-TAS by country.", @@ -1079,6 +1140,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 2: District-level baseline prevalence by country.", @@ -1090,6 +1152,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 3: Percent pre-TAS failure by each characteristic (unadjusted).", @@ -1101,6 +1164,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 4: Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.", @@ -1112,6 +1176,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 5: Analysis of failures by model combinations.", @@ -1127,6 +1192,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "References", @@ -1139,6 +1205,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "World Health Organization. Lymphatic filariasis: progress report 2000\u20132009 and strategic plan 2010\u20132020. Geneva; 2010. ", @@ -1152,6 +1219,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "World Health Organization. Validation of elimination of lymphatic filariasis as a public health problem. Geneva; 2017. ", @@ -1165,6 +1233,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Global programme to eliminate lymphatic filariasis: progress report, 2018. Wkly Epidemiol Rec (2019)", @@ -1178,6 +1247,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "World Health Organization. Global programme to eliminate lymphatic filariasis: monitoring and epidemiological assessment of mass drug administration. Geneva; 2011. ", @@ -1191,6 +1261,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "World Health Organization. Strengthening the assessment of lymphatic filariasis transmission and documenting the achievement of elimination\u2014Meeting of the Neglected Tropical Diseases Strategic and Technical Advisory Group\u2019s Monitoring and Evaluation Subgroup on Disease-specific Indicators. 2016; 42. ", @@ -1204,6 +1275,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kyelem D; Biswas G; Bockarie MJ; Bradley MH; El-Setouhy M; Fischer PU. Determinants of success in national programs to eliminate lymphatic filariasis: a perspective identifying essential elements and research needs. 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Geospat Health (2013)", @@ -1516,6 +1611,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Manhenje I; Teresa Gal\u00e1n-Puchades M; Fuentes M V. Socio-environmental variables and transmission risk of lymphatic filariasis in central and northern Mozambique. Geospat Health (2013)", @@ -1529,6 +1625,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ngwira BM; Tambala P; Perez a M; Bowie C; Molyneux DH. The geographical distribution of lymphatic filariasis infection in Malawi. Filaria J (2007)", @@ -1542,6 +1639,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Simonsen PE; Mwakitalu ME. Urban lymphatic filariasis. Parasitol Res (2013)", @@ -1555,6 +1653,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Proville J; Zavala-Araiza D; Wagner G. Night-time lights: a global, long term look at links to socio-economic trends. PLoS One (2017)", @@ -1568,6 +1667,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Endeshaw T; Taye A; Tadesse Z; Katabarwa MN; Shafi O; Seid T. Presence of Wuchereria bancrofti microfilaremia despite seven years of annual ivermectin monotherapy mass drug administration for onchocerciasis control: a study in north-west Ethiopia. Pathog Glob Health (2015)", @@ -1581,6 +1681,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Richards FO; Eigege A; Pam D; Kal A; Lenhart A; Oneyka JOA. Mass ivermectin treatment for onchocerciasis: lack of evidence for collateral impact on transmission of Wuchereria bancrofti in areas of co-endemicity. Filaria J (2005)", @@ -1594,6 +1695,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kyelem D; Sanou S; Boatin B a; Medlock J; Couibaly S; Molyneux DH. Impact of long-term ivermectin (Mectizan) on Wuchereria bancrofti and Mansonella perstans infections in Burkina Faso: strategic and policy implications. Ann Trop Med Parasitol (2003)", @@ -1607,6 +1709,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Weil GJ; Lammie PJ; Richards FO; Eberhard ML. Changes in circulating parasite antigen levels after treatment of bancroftian filariasis with diethylcarbamazine and ivermectin. J Infect Dis (1991)", @@ -1620,6 +1723,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kumar A; Sachan P. Measuring impact on filarial infection status in a community study: role of coverage of mass drug administration. Trop Biomed (2014)", @@ -1633,6 +1737,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Njenga SM; Mwandawiro CS; Wamae CN; Mukoko DA; Omar AA; Shimada M. Sustained reduction in prevalence of lymphatic filariasis infection in spite of missed rounds of mass drug administration in an area under mosquito nets for malaria control. Parasites and Vectors (2011)", @@ -1646,6 +1751,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Boyd A; Won KY; McClintock SK; Donovan C V; Laney SJ; Williams SA. A community-based study of factors associated with continuing transmission of lymphatic filariasis in Leogane, Haiti. PLoS Negl Trop Dis (2010)", @@ -1659,6 +1765,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Irvine MA; Reimer LJ; Njenga SM; Gunawardena S; Kelly-Hope L; Bockarie M. Modelling strategies to break transmission of lymphatic filariasis\u2014aggregation, adherence and vector competence greatly alter elimination. Parasites and Vectors (2015)", @@ -1672,6 +1779,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Irvine MA; Stolk WA; Smith ME; Subramanian S; Singh BK; Weil GJ. Effectiveness of a triple-drug regimen for global elimination of lymphatic filariasis: a modelling study. Lancet Infect Dis (2017)", @@ -1685,6 +1793,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Pion SD; Montavon C; Chesnais CB; Kamgno J; Wanji S; Klion AD. Positivity of antigen tests used for diagnosis of lymphatic filariasis in individuals without Wuchereria bancrofti infection but with high loa loa microfilaremia. Am J Trop Med Hyg (2016)", @@ -1698,6 +1807,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wanji S; Esum ME; Njouendou AJ; Mbeng AA; Chounna Ndongmo PW; Abong RA. Mapping of lymphatic filariasis in loiasis areas: a new strategy shows no evidence for Wuchereria bancrofti endemicity in Cameroon. PLoS Negl Trop Dis (2018)", @@ -1711,6 +1821,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Chesnais CB; Awaca-Uvon NP; Bolay FK; Boussinesq M; Fischer PU; Gankpala L. A multi-center field study of two point-of-care tests for circulating Wuchereria bancrofti antigenemia in Africa. PLoS Negl Trop Dis (2017)", @@ -1724,6 +1835,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Silumbwe A; Zulu JM; Halwindi H; Jacobs C; Zgambo J; Dambe R. A systematic review of factors that shape implementation of mass drug administration for lymphatic filariasis in sub-Saharan Africa. BMC Public Health (2017)", @@ -1737,6 +1849,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Adams AM; Vuckovic M; Birch E; Brant TA; Bialek S; Yoon D. Eliminating neglected tropical diseases in urban areas: a review of challenges, strategies and research directions for successful mass drug administration. Trop Med Infect Dis (2018)", @@ -1750,6 +1863,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Rao RU; Samarasekera SD; Nagodavithana KC; Dassanayaka TDM; Punchihewa MW; Ranasinghe USB. Reassessment of areas with persistent lymphatic filariasis nine years after cessation of mass drug administration in Sri Lanka. PLoS Negl Trop Dis (2017)", @@ -1763,6 +1877,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Xu Z; Graves PM; Lau CL; Clements A; Geard N; Glass K. GEOFIL: a spatially-explicit agent-based modelling framework for predicting the long-term transmission dynamics of lymphatic filariasis in American Samoa. Epidemics (2018)", @@ -1776,6 +1891,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Id CM; Tettevi EJ; Mechan F; Idun B; Biritwum N; Osei-atweneboana MY. Elimination within reach: a cross-sectional study highlighting the factors that contribute to persistent lymphatic filariasis in eight communities in rural Ghana. PLoS Negl Trop Dis (2019)", @@ -1789,6 +1905,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Eigege A; Kal A; Miri E; Sallau A; Umaru J; Mafuyai H. Long-lasting insecticidal nets are synergistic with mass drug administration for interruption of lymphatic filariasis transmission in Nigeria. PLoS Negl Trop Dis (2013)", @@ -1802,6 +1919,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Van den Berg H; Kelly-Hope LA; Lindsay SW. Malaria and lymphatic filariasis: The case for integrated vector management. Lancet Infect Dis (2013)", @@ -1815,6 +1933,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Webber R.. Eradication of Wuchereria bancrofti infection through vector control. Trans R Soc Trop Med Hyg (1979)", @@ -1830,6 +1949,7 @@ "$ref": "#/texts/56" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1847,6 +1967,7 @@ "$ref": "#/texts/56" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1864,6 +1985,7 @@ "$ref": "#/texts/56" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1881,6 +2003,7 @@ "$ref": "#/texts/56" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1898,6 +2021,7 @@ "$ref": "#/texts/56" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -1917,6 +2041,7 @@ "$ref": "#/texts/53" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -5433,6 +5558,7 @@ "$ref": "#/texts/53" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ diff --git a/tests/data/groundtruth/docling_v2/pone.0234687.xml.json b/tests/data/groundtruth/docling_v2/pone.0234687.xml.json index 0854314..92b50b8 100644 --- a/tests/data/groundtruth/docling_v2/pone.0234687.xml.json +++ b/tests/data/groundtruth/docling_v2/pone.0234687.xml.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "pone.0234687", "origin": { "mimetype": "application/xml", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -47,6 +48,7 @@ "$ref": "#/texts/76" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -286,6 +288,7 @@ "$ref": "#/texts/153" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -325,6 +328,7 @@ "$ref": "#/texts/77" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions", @@ -336,6 +340,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ribeiro-Filho Henrique M. N.; 1: Department of Animal Science, University of California, Davis, California, United States of America, 2: Programa de P\u00f3s-gradua\u00e7\u00e3o em Ci\u00eancia Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Civiero Maur\u00edcio; 2: Programa de P\u00f3s-gradua\u00e7\u00e3o em Ci\u00eancia Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Kebreab Ermias; 1: Department of Animal Science, University of California, Davis, California, United States of America", @@ -351,6 +356,7 @@ "$ref": "#/texts/3" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Abstract", @@ -363,6 +369,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein \u00d7 Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.", @@ -390,6 +397,7 @@ "$ref": "#/texts/9" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Introduction", @@ -402,6 +410,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Greenhouse gas (GHG) emissions from livestock activities represent 10\u201312% of global emissions [1], ranging from 5.5\u20137.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].", @@ -413,6 +422,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8\u201311] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.", @@ -424,6 +434,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17\u201319]).", @@ -435,6 +446,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.", @@ -446,6 +458,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.", @@ -491,6 +504,7 @@ "$ref": "#/texts/41" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Materials and methods", @@ -503,6 +517,7 @@ "$ref": "#/texts/10" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the \u2018Comiss\u00e3o de \u00c9tica no Uso de Animais\u2019 (CEUA/UDESC) on September 15, 2016\u2014Approval number 4373090816 - https://www.udesc.br/cav/ceua.", @@ -518,6 +533,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "System boundary", @@ -530,6 +546,7 @@ "$ref": "#/texts/12" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.", @@ -548,6 +565,7 @@ "$ref": "#/texts/16" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Functional unit", @@ -560,6 +578,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:", @@ -571,6 +590,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "ECM = Milk production \u00d7 (0.0929 \u00d7 fat% + 0.0588\u00d7 true protein% + 0.192) / (0.0929 \u00d7 (4%) + 0.0588 \u00d7 (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein \u00d7 Jersey cows, with key characteristics described in Table 1.", @@ -589,6 +609,7 @@ "$ref": "#/texts/19" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Data sources and livestock system description", @@ -601,6 +622,7 @@ "$ref": "#/texts/17" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum \u2018Campeiro\u2019) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum \u2018Maximus\u2019) did not need changes to ECM production compared to the ad libitum TMR intake group.", @@ -612,6 +634,7 @@ "$ref": "#/texts/17" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.", @@ -627,6 +650,7 @@ "$ref": "#/texts/21" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Impact assessment", @@ -639,6 +663,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.", @@ -657,6 +682,7 @@ "$ref": "#/texts/25" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Feed production", @@ -673,6 +699,7 @@ "$ref": "#/texts/24" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Diets composition", @@ -685,6 +712,7 @@ "$ref": "#/texts/23" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.", @@ -703,6 +731,7 @@ "$ref": "#/texts/27" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "GHG emissions from crop and pasture production", @@ -715,6 +744,7 @@ "$ref": "#/texts/25" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33\u201336], assuming that animals are able to consume 70% of pastures during grazing.", @@ -726,6 +756,7 @@ "$ref": "#/texts/25" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].", @@ -741,6 +772,7 @@ "$ref": "#/texts/29" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Animal husbandry", @@ -753,6 +785,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 \u00d7 NDF (% DM intake).", @@ -774,6 +807,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Manure from confined cows and urine and dung from grazing animals", @@ -786,6 +820,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eug\u00e8ne et al. [44] as: VS = NDOMI + (UE \u00d7 GE) \u00d7 (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) \u00d7 OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).", @@ -797,6 +832,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6\u00baC) or the average temperatures during the growth period of temperate (14.4\u00baC) or tropical (21\u00baC) annual pastures, which were 31%, 26% and 46%, respectively.", @@ -808,6 +844,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1\u22120.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions \u00d7 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.", @@ -838,6 +875,7 @@ "$ref": "#/texts/58" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Farm management", @@ -850,6 +888,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in \u2018Emissions from crop and pasture production\u2019 section.", @@ -861,6 +900,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI \u00d7 days in confinement \u00d7 proportion of daily time that animals stayed on confinement.", @@ -872,6 +912,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) \u00d7 (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling \u00d7 emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) \u00d7 (kg machinery mass (kg manure)-1 \u00d7 10\u22123) [42] \u00d7 kg CO2e (kg machinery mass)-1 [42].", @@ -883,6 +924,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].", @@ -898,6 +940,7 @@ "$ref": "#/texts/40" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Co-product allocation", @@ -910,6 +953,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1\u20136.04 \u00d7 BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.", @@ -925,6 +969,7 @@ "$ref": "#/texts/42" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Sensitivity analysis", @@ -937,6 +982,7 @@ "$ref": "#/texts/41" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.", @@ -967,6 +1013,7 @@ "$ref": "#/texts/61" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Results and discussion", @@ -979,6 +1026,7 @@ "$ref": "#/texts/43" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.", @@ -1003,6 +1051,7 @@ "$ref": "#/texts/49" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Greenhouse gas emissions", @@ -1015,6 +1064,7 @@ "$ref": "#/texts/45" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.", @@ -1026,6 +1076,7 @@ "$ref": "#/texts/45" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture\u20140.91 kg CO2e (kg ECM)-1\u2014was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.", @@ -1037,6 +1088,7 @@ "$ref": "#/texts/45" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.", @@ -1048,6 +1100,7 @@ "$ref": "#/texts/45" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].", @@ -1066,6 +1119,7 @@ "$ref": "#/texts/52" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Methane emissions", @@ -1078,6 +1132,7 @@ "$ref": "#/texts/50" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.", @@ -1089,6 +1144,7 @@ "$ref": "#/texts/50" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].", @@ -1110,6 +1166,7 @@ "$ref": "#/texts/56" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Emissions from excreta and feed production", @@ -1122,6 +1179,7 @@ "$ref": "#/texts/53" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62\u201364]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].", @@ -1133,6 +1191,7 @@ "$ref": "#/texts/53" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.", @@ -1144,6 +1203,7 @@ "$ref": "#/texts/53" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].", @@ -1155,6 +1215,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the \u2018emissions from crop and pasture production\u2019, total emissions from farm management were not greater than 5% of total C footprint.", @@ -1166,6 +1227,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].", @@ -1181,6 +1243,7 @@ "$ref": "#/texts/60" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Assumptions and limitations", @@ -1193,6 +1256,7 @@ "$ref": "#/texts/59" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.", @@ -1211,6 +1275,7 @@ "$ref": "#/texts/63" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Further considerations", @@ -1223,6 +1288,7 @@ "$ref": "#/texts/61" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal\u2019s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].", @@ -1234,6 +1300,7 @@ "$ref": "#/texts/61" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].", @@ -1249,6 +1316,7 @@ "$ref": "#/texts/65" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Conclusions", @@ -1261,6 +1329,7 @@ "$ref": "#/texts/64" }, "children": [], + "content_layer": "body", "label": "text", "prov": [], "orig": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.", @@ -1288,6 +1357,7 @@ "$ref": "#/tables/4" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Tables", @@ -1300,6 +1370,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 1: Descriptive characteristics of the herd.", @@ -1311,6 +1382,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 2: Dairy cows\u2019 diets in different scenariosa.", @@ -1322,6 +1394,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 3: GHG emission factors for Off- and On-farm feed production.", @@ -1333,6 +1406,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 4: GHG emissions from On-farm feed production.", @@ -1344,6 +1418,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Table 5: Factors for major resource inputs in farm management.", @@ -1368,6 +1443,7 @@ "$ref": "#/pictures/3" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Figures", @@ -1380,6 +1456,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 1: Overview of the milk production system boundary considered in the study.", @@ -1391,6 +1468,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 2: Overall greenhouse gas emissions in dairy cattle systems under various scenarios.\nTMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", @@ -1402,6 +1480,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 3: Sensitivity of the C footprint.\nSensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", @@ -1413,6 +1492,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Fig 4: Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.\nTMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.", @@ -1428,6 +1508,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "References", @@ -1440,6 +1521,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Climate Change and Land. Chapter 5: Food Security (2019)", @@ -1453,6 +1535,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Herrero M; Henderson B; Havl\u00edk P; Thornton PK; Conant RT; Smith P. Greenhouse gas mitigation potentials in the livestock sector. Nat Clim Chang (2016)", @@ -1466,6 +1549,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Rivera-Ferre MG; L\u00f3pez-i-Gelats F; Howden M; Smith P; Morton JF; Herrero M. Re-framing the climate change debate in the livestock sector: mitigation and adaptation options. Wiley Interdiscip Rev Clim Chang (2016)", @@ -1479,6 +1563,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "van Zanten HHE; Mollenhorst H; Klootwijk CW; van Middelaar CE; de Boer IJM. Global food supply: land use efficiency of livestock systems. Int J Life Cycle Assess (2016)", @@ -1492,6 +1577,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hristov AN; Oh J; Firkins L; Dijkstra J; Kebreab E; Waghorn G. SPECIAL TOPICS\u2014Mitigation of methane and nitrous oxide emissions from animal operations: I. 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Solutions for a cultivated planet. Nature (2011)", @@ -2389,6 +2543,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Lal R.. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science (80-) (2004)", @@ -2402,6 +2557,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Boddey RM; Jantalia CP; Concei\u00e7ao PC; Zanatta JA; Bayer C; Mielniczuk J. Carbon accumulation at depth in Ferralsols under zero-till subtropical agriculture. Glob Chang Biol (2010)", @@ -2415,6 +2571,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "McConkey B; Angers D; Bentham M; Boehm M; Brierley T; Cerkowniak D. Canadian agricultural greenhouse gas monitoring accounting and reporting system: methodology and greenhouse gas estimates for agricultural land in the LULUCF sector for NIR 2014. (2014)", @@ -2430,6 +2587,7 @@ "$ref": "#/texts/72" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2447,6 +2605,7 @@ "$ref": "#/texts/72" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2464,6 +2623,7 @@ "$ref": "#/texts/72" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2481,6 +2641,7 @@ "$ref": "#/texts/72" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -2500,6 +2661,7 @@ "$ref": "#/texts/66" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -3486,6 +3648,7 @@ "$ref": "#/texts/66" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -8664,6 +8827,7 @@ "$ref": "#/texts/66" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -9786,6 +9950,7 @@ "$ref": "#/texts/66" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ @@ -13226,6 +13391,7 @@ "$ref": "#/texts/66" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [ diff --git a/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json b/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json index 44288d7..3d5b32e 100644 --- a/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json +++ b/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "powerpoint_sample", "origin": { "mimetype": "application/vnd.ms-powerpoint", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -26,6 +27,7 @@ "$ref": "#/groups/2" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -46,6 +48,7 @@ "$ref": "#/tables/0" } ], + "content_layer": "body", "name": "slide-0", "label": "chapter" }, @@ -74,6 +77,7 @@ "$ref": "#/texts/7" } ], + "content_layer": "body", "name": "slide-1", "label": "chapter" }, @@ -105,6 +109,7 @@ "$ref": "#/groups/7" } ], + "content_layer": "body", "name": "slide-2", "label": "chapter" }, @@ -124,6 +129,7 @@ "$ref": "#/texts/10" } ], + "content_layer": "body", "name": "list", "label": "ordered_list" }, @@ -146,6 +152,7 @@ "$ref": "#/texts/14" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -162,6 +169,7 @@ "$ref": "#/texts/17" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -181,6 +189,7 @@ "$ref": "#/texts/21" } ], + "content_layer": "body", "name": "list", "label": "ordered_list" }, @@ -200,6 +209,7 @@ "$ref": "#/texts/24" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -211,6 +221,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "title", "prov": [ { @@ -237,6 +248,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -263,6 +275,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "title", "prov": [ { @@ -289,6 +302,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -315,6 +329,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -341,6 +356,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -367,6 +383,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -393,6 +410,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -419,6 +437,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -447,6 +466,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -475,6 +495,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -503,6 +524,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -531,6 +553,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -559,6 +582,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -587,6 +611,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -615,6 +640,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -641,6 +667,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -669,6 +696,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -697,6 +725,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -723,6 +752,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -751,6 +781,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -779,6 +810,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -807,6 +839,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -835,6 +868,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -863,6 +897,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [ { @@ -894,6 +929,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [ { diff --git a/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json b/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json index eaa343f..b6c7422 100644 --- a/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json +++ b/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "powerpoint_with_image", "origin": { "mimetype": "application/vnd.ms-powerpoint", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -40,6 +42,7 @@ "$ref": "#/pictures/0" } ], + "content_layer": "body", "name": "slide-0", "label": "chapter" } @@ -51,6 +54,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "title", "prov": [ { @@ -77,6 +81,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [ { @@ -105,6 +110,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [ { diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.json index 5d884b0..d7edc39 100644 --- a/tests/data/groundtruth/docling_v2/redp5110_sampled.json +++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "redp5110_sampled", "origin": {"mimetype": "application/pdf", "binary_hash": 12110913468886801317, "filename": "redp5110_sampled.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": 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64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH Security fundamentals", "text": "GLYPH Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH Current state of IBM i security", "text": "GLYPH Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH DB2 for i security controls", "text": "GLYPH DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH Work Function Usage ( WRKFCNUSG )", "text": "GLYPH Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH Change Function Usage ( CHGFCNUSG )", "text": "GLYPH Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH Display Function Usage ( DSPFCNUSG )", "text": "GLYPH Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.86685, "t": 652.32031, "r": 257.58578, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 257.59152, "t": 652.32031, "r": 336.99741, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "permission name", "text": "permission name"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 337.01233, "t": 652.32031, "r": 342.73126, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 346.56491, "t": 670.53748, "r": 530.74371, "b": 662.66492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "Names the row permission for row access control", "text": "Names the row permission for row access control"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.30872, "t": 610.93744, "r": 226.86777, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.86548000000002, "t": 610.93744, "r": 232.58441, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": 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"orig": "DISABLE", "text": "DISABLE"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.58514, "t": 404.0228, "r": 190.6628, "b": 394.20416000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.73431, "t": 415.34283, "r": 455.83047000000005, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Specifies that the row permission is to", "text": "Specifies that the row permission is to"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8848, "t": 415.34283, "r": 527.62122, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "be initially disabled", "text": "be initially disabled"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH A user connects to the server using the user profile ALICE.", "text": "GLYPH A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "USER = ALICE CURRENT USER = ALICE", "text": "USER = ALICE CURRENT USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "code_language": "unknown"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "code_language": "unknown"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "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 ;", "text": "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 ;", "code_language": "unknown"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "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:", "text": "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:"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/226", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "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.", "text": "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.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "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.", "text": "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.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "code_language": "unknown"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/15"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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"row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5494995117188, "t": 564.3729248046875, "r": 547.1978759765625, "b": 555.159912109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 551.89306640625, "r": 284.0286560058594, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449462890625, "t": 551.89306640625, "r": 547.1211547851562, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 536.0958862304688, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6658935546875, "t": 432.8550720214844, "r": 547.2047119140625, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 420.37518310546875, "r": 535.9526977539062, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.5558471679688, "t": 420.37518310546875, "r": 547.1590576171875, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, 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{"bbox": {"l": 151.19720458984375, "t": 395.35565185546875, "r": 536.0748901367188, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6302490234375, "t": 395.35565185546875, "r": 547.1856079101562, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 382.8757629394531, "r": 411.2704772949219, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.8177490234375, "t": 382.8757629394531, "r": 547.1786499023438, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 370.3958740234375, "r": 536.035888671875, "b": 361.1828918457031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", 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"coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 345.3763427734375, "r": 530.5352172851562, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755004882812, "t": 345.3763427734375, "r": 547.156005859375, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 332.8964538574219, "r": 547.256591796875, "b": 323.6834716796875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 310.3968200683594, "r": 530.5396118164062, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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"end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1971893310547, "t": 273.3774719238281, "r": 530.4347534179688, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.9962158203125, "t": 273.3774719238281, "r": 547.1190795898438, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "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": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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30]}], "orig": "r ISV education and enablement", "text": "r ISV education and enablement", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH Security fundamentals", "text": "GLYPH Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH Current state of IBM i security", "text": "GLYPH Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH DB2 for i security controls", "text": "GLYPH DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH Work Function Usage ( WRKFCNUSG )", "text": "GLYPH Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH Change Function Usage ( CHGFCNUSG )", "text": "GLYPH Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH Display Function Usage ( DSPFCNUSG )", "text": "GLYPH Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.86685, "t": 652.32031, "r": 257.58578, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 257.59152, "t": 652.32031, "r": 336.99741, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "permission name", "text": "permission name"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 337.01233, "t": 652.32031, "r": 342.73126, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 346.56491, "t": 670.53748, "r": 530.74371, "b": 662.66492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "Names the row permission for row access control", "text": "Names the row permission for row access control"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.30872, "t": 610.93744, "r": 226.86777, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.86548000000002, "t": 610.93744, "r": 232.58441, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.3204, "t": 625.70587, "r": 450.77191000000005, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Identifies the table on which the row", "text": "Identifies the table on which the row"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 450.86123999999995, "t": 625.70587, "r": 529.93134, "b": 617.83331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "permission is created", "text": "permission is created"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 569.5545, "r": 163.10973, "b": 559.72437, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "AS", "text": "AS"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 165.68669, "t": 569.5545, "r": 171.40562, "b": 559.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 171.41136, "t": 569.5545, "r": 251.20424000000003, "b": 559.72437, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "correlation name", "text": "correlation name"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.21115, "t": 569.5545, "r": 256.93008, "b": 559.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 235.79649, "t": 587.77161, "r": 406.62051, "b": 579.89905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Specifies an optional correlation name that ca", "text": "Specifies an optional correlation name that ca"}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 480.53094, "t": 587.77161, "r": 532.89496, "b": 579.89905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "be used within search-condition", "text": "be used within search-condition"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 528.17163, "r": 199.72467, "b": 518.34149, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOR ROWS", "text": "FOR ROWS"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 321.56271, "t": 545.90588, "r": 455.3432, "b": 538.03333, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Indicates that a row permission is cr", "text": "Indicates that a row permission is cr"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.20786000000004, "t": 545.90588, "r": 476.48404, "b": 538.03333, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "eated", "text": "eated"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 321.5972, "t": 525.69733, "r": 444.0292400000001, "b": 517.82477, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Specifies a condition that can be", "text": "Specifies a condition that can be"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 444.07986, "t": 525.69733, "r": 459.08678999999995, "b": 517.82477, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "true,", "text": "true,"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 464.2088, "t": 525.69733, "r": 530.94897, "b": 517.82477, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": 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{"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 472.73705999999993, "t": 415.34283, "r": 490.1676, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "iti ll", "text": "iti ll"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 496.33661, "t": 415.34283, "r": 503.2608, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "di", "text": "di"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 511.26138, "t": 415.34283, "r": 527.59674, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "bl d", "text": "bl d"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 404.0228, "r": 187.6265, "b": 394.19265999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "DISABLE", "text": "DISABLE"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.58514, "t": 404.0228, "r": 190.6628, "b": 394.20416000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.73431, "t": 415.34283, "r": 455.83047000000005, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Specifies that the row permission is to", "text": "Specifies that the row permission is to"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8848, "t": 415.34283, "r": 527.62122, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "be initially disabled", "text": "be initially disabled"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH A user connects to the server using the user profile ALICE.", "text": "GLYPH A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "USER = ALICE CURRENT USER = ALICE", "text": "USER = ALICE CURRENT USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "code_language": "unknown"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "code_language": "unknown"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "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 ;", "text": "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 ;", "code_language": "unknown"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "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:", "text": "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:"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/226", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/13"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "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.", "text": "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.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/groups/15"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "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.", "text": "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.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "code_language": "unknown"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/15"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/16"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment."}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 213.1680908203125, "r": 570.947998046875, "b": 190.48809814453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "For more information: ibm.com /redbooks", "text": "For more information: ibm.com /redbooks"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 513.4560546875, "t": 765.9149169921875, "r": 586.1583251953125, "b": 737.1808471679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901123046875, "t": 624.3718872070312, "r": 279.3973083496094, "b": 615.1588745117188, "coord_origin": "BOTTOMLEFT"}, "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": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.6194152832031, "t": 624.3718872070312, "r": 547.1907958984375, "b": 615.1588745117188, "coord_origin": "BOTTOMLEFT"}, "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": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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"row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 536.0958862304688, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6658935546875, "t": 432.8550720214844, "r": 547.2047119140625, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 420.37518310546875, "r": 535.9526977539062, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.5558471679688, "t": 420.37518310546875, "r": 547.1590576171875, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, 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{"bbox": {"l": 151.19720458984375, "t": 395.35565185546875, "r": 536.0748901367188, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6302490234375, "t": 395.35565185546875, "r": 547.1856079101562, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 382.8757629394531, "r": 411.2704772949219, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.8177490234375, "t": 382.8757629394531, "r": 547.1786499023438, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 370.3958740234375, "r": 536.035888671875, "b": 361.1828918457031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", 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Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "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": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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612.0, "height": 792.0}, "image": null, "page_no": 14}, "15": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 15}, "16": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 16}, "17": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 17}, "18": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 18}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/tablecell.docx.json b/tests/data/groundtruth/docling_v2/tablecell.docx.json index d811cc8..42ff026 100644 --- a/tests/data/groundtruth/docling_v2/tablecell.docx.json +++ b/tests/data/groundtruth/docling_v2/tablecell.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "tablecell", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -38,6 +39,7 @@ "$ref": "#/texts/6" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -55,6 +57,7 @@ "$ref": "#/texts/1" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -66,6 +69,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hello world1", @@ -79,6 +83,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hello2", @@ -92,6 +97,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -103,6 +109,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some text before", @@ -114,6 +121,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -125,6 +133,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -136,6 +145,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some text after", @@ -150,6 +160,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.json b/tests/data/groundtruth/docling_v2/test-01.xlsx.json index b664229..153fe11 100644 --- a/tests/data/groundtruth/docling_v2/test-01.xlsx.json +++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "test-01", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -26,6 +27,7 @@ "$ref": "#/groups/2" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -40,6 +42,7 @@ "$ref": "#/tables/0" } ], + "content_layer": "body", "name": "sheet: Sheet1", "label": "section" }, @@ -59,6 +62,7 @@ "$ref": "#/tables/3" } ], + "content_layer": "body", "name": "sheet: Sheet2", "label": "section" }, @@ -78,6 +82,7 @@ "$ref": "#/pictures/0" } ], + "content_layer": "body", "name": "sheet: Sheet3", "label": "section" } @@ -90,6 +95,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -114,6 +120,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -652,6 +659,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -1554,6 +1562,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -1944,6 +1953,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -2334,6 +2344,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -2800,6 +2811,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json b/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json index 6418a21..1319d4a 100644 --- a/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json +++ b/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "test_emf_docx", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -38,6 +39,7 @@ "$ref": "#/pictures/2" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -49,6 +51,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Test with three images in unusual formats", @@ -60,6 +63,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Raster in emf:", @@ -71,6 +75,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Vector in emf:", @@ -82,6 +87,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Raster in webp:", @@ -95,6 +101,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -108,6 +115,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -121,6 +129,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/unit_test_01.html.json b/tests/data/groundtruth/docling_v2/unit_test_01.html.json index fa12617..35f76ec 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_01.html.json +++ b/tests/data/groundtruth/docling_v2/unit_test_01.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "unit_test_01", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -34,6 +36,7 @@ "$ref": "#/texts/4" } ], + "content_layer": "body", "name": "header-3", "label": "section" } @@ -52,6 +55,7 @@ "$ref": "#/texts/3" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Title", @@ -67,6 +71,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-1", @@ -79,6 +84,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-1.1", @@ -101,6 +107,7 @@ "$ref": "#/texts/6" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-2", @@ -113,6 +120,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-2.0.1", @@ -125,6 +133,7 @@ "$ref": "#/texts/3" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-2.2", @@ -137,6 +146,7 @@ "$ref": "#/texts/3" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "section-2.3", diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json b/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json index c76d241..4c25a39 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "unit_test_headers", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -34,6 +36,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "name": "header-2", "label": "section" } @@ -55,6 +58,7 @@ "$ref": "#/texts/27" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Test Document", @@ -66,6 +70,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -99,6 +104,7 @@ "$ref": "#/texts/14" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1", @@ -111,6 +117,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -122,6 +129,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1", @@ -133,6 +141,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -144,6 +153,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2", @@ -155,6 +165,7 @@ "$ref": "#/texts/2" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -182,6 +193,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.1", @@ -194,6 +206,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -205,6 +218,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.1", @@ -216,6 +230,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -227,6 +242,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.2", @@ -238,6 +254,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -268,6 +285,7 @@ "$ref": "#/texts/20" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.2", @@ -280,6 +298,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -291,6 +310,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.1", @@ -302,6 +322,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -313,6 +334,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.2", @@ -324,6 +346,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -354,6 +377,7 @@ "$ref": "#/texts/26" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.2.3", @@ -366,6 +390,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -377,6 +402,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2.3.1", @@ -388,6 +414,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -399,6 +426,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2.3.1", @@ -410,6 +438,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -421,6 +450,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -454,6 +484,7 @@ "$ref": "#/texts/39" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2", @@ -466,6 +497,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -477,6 +509,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1", @@ -488,6 +521,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -499,6 +533,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.2", @@ -510,6 +545,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -537,6 +573,7 @@ "$ref": "#/texts/38" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2.1.1", @@ -549,6 +586,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -560,6 +598,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1.1", @@ -571,6 +610,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -582,6 +622,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1.1", @@ -593,6 +634,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -623,6 +665,7 @@ "$ref": "#/texts/45" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2.1", @@ -635,6 +678,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -646,6 +690,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1", @@ -657,6 +702,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -668,6 +714,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.2", @@ -679,6 +726,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -690,6 +738,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json index 38a25d3..8c0d713 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "unit_test_headers_numbered", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -56,6 +57,7 @@ "$ref": "#/groups/2" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -73,6 +75,7 @@ "$ref": "#/texts/27" } ], + "content_layer": "body", "name": "header-0", "label": "section" }, @@ -89,6 +92,7 @@ "$ref": "#/texts/14" } ], + "content_layer": "body", "name": "header-1", "label": "section" }, @@ -102,6 +106,7 @@ "$ref": "#/groups/3" } ], + "content_layer": "body", "name": "header-0", "label": "section" }, @@ -118,6 +123,7 @@ "$ref": "#/texts/39" } ], + "content_layer": "body", "name": "header-1", "label": "section" }, @@ -131,6 +137,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "name": "header-2", "label": "section" } @@ -149,6 +156,7 @@ "$ref": "#/texts/2" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Test Document", @@ -160,6 +168,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -171,6 +180,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1", @@ -183,6 +193,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -194,6 +205,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1", @@ -205,6 +217,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -216,6 +229,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2", @@ -227,6 +241,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -254,6 +269,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.1", @@ -266,6 +282,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -277,6 +294,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.1", @@ -288,6 +306,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -299,6 +318,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.2", @@ -310,6 +330,7 @@ "$ref": "#/texts/8" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -340,6 +361,7 @@ "$ref": "#/texts/20" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.2", @@ -352,6 +374,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -363,6 +386,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.1", @@ -374,6 +398,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -385,6 +410,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.1.2", @@ -396,6 +422,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -426,6 +453,7 @@ "$ref": "#/texts/26" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 1.2.3", @@ -438,6 +466,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -449,6 +478,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2.3.1", @@ -460,6 +490,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -471,6 +502,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 1.2.3.1", @@ -482,6 +514,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -493,6 +526,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -504,6 +538,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2", @@ -516,6 +551,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -527,6 +563,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1", @@ -538,6 +575,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -549,6 +587,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.2", @@ -560,6 +599,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -587,6 +627,7 @@ "$ref": "#/texts/38" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2.1.1", @@ -599,6 +640,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -610,6 +652,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1.1", @@ -621,6 +664,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -632,6 +676,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1.1", @@ -643,6 +688,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -673,6 +719,7 @@ "$ref": "#/texts/45" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Section 2.1", @@ -685,6 +732,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -696,6 +744,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1", @@ -707,6 +756,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -718,6 +768,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.2", @@ -729,6 +780,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -740,6 +792,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", diff --git a/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json b/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json index 1410586..6d72d27 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "unit_test_lists", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -34,6 +36,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "header-0", "label": "section" }, @@ -62,6 +65,7 @@ "$ref": "#/texts/36" } ], + "content_layer": "body", "name": "header-2", "label": "section" }, @@ -81,6 +85,7 @@ "$ref": "#/texts/10" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -100,6 +105,7 @@ "$ref": "#/texts/15" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -122,6 +128,7 @@ "$ref": "#/texts/23" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -141,6 +148,7 @@ "$ref": "#/texts/22" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -160,6 +168,7 @@ "$ref": "#/texts/28" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -173,6 +182,7 @@ "$ref": "#/texts/27" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -192,6 +202,7 @@ "$ref": "#/texts/34" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -208,6 +219,7 @@ "$ref": "#/groups/10" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -221,6 +233,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -243,6 +256,7 @@ "$ref": "#/texts/42" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -262,6 +276,7 @@ "$ref": "#/groups/13" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -275,6 +290,7 @@ "$ref": "#/texts/41" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -308,6 +324,7 @@ "$ref": "#/groups/1" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test Document", @@ -320,6 +337,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -331,6 +349,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -342,6 +361,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.1", @@ -353,6 +373,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -364,6 +385,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Paragraph 2.1.2", @@ -375,6 +397,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -393,6 +416,7 @@ "$ref": "#/texts/11" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 1:", @@ -405,6 +429,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1", @@ -418,6 +443,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 2", @@ -431,6 +457,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 3", @@ -444,6 +471,7 @@ "$ref": "#/texts/7" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -462,6 +490,7 @@ "$ref": "#/texts/16" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 2:", @@ -474,6 +503,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item a", @@ -487,6 +517,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item b", @@ -500,6 +531,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item c", @@ -513,6 +545,7 @@ "$ref": "#/texts/12" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -531,6 +564,7 @@ "$ref": "#/texts/24" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 3:", @@ -543,6 +577,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1", @@ -556,6 +591,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 2", @@ -569,6 +605,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.1", @@ -582,6 +619,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.2", @@ -595,6 +633,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.3", @@ -608,6 +647,7 @@ "$ref": "#/groups/4" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 3", @@ -621,6 +661,7 @@ "$ref": "#/texts/17" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -639,6 +680,7 @@ "$ref": "#/texts/29" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 4:", @@ -651,6 +693,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1", @@ -664,6 +707,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.1", @@ -677,6 +721,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 2", @@ -690,6 +735,7 @@ "$ref": "#/texts/25" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -708,6 +754,7 @@ "$ref": "#/texts/35" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 5:", @@ -720,6 +767,7 @@ "$ref": "#/groups/8" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1", @@ -733,6 +781,7 @@ "$ref": "#/groups/9" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.1", @@ -746,6 +795,7 @@ "$ref": "#/groups/10" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.1.1", @@ -759,6 +809,7 @@ "$ref": "#/groups/8" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 3", @@ -772,6 +823,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -796,6 +848,7 @@ "$ref": "#/texts/45" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test 6:", @@ -808,6 +861,7 @@ "$ref": "#/groups/11" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1", @@ -821,6 +875,7 @@ "$ref": "#/groups/11" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 2", @@ -834,6 +889,7 @@ "$ref": "#/groups/12" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.1", @@ -847,6 +903,7 @@ "$ref": "#/groups/12" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.2", @@ -860,6 +917,7 @@ "$ref": "#/groups/13" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 1.2.1", @@ -873,6 +931,7 @@ "$ref": "#/groups/11" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "List item 3", @@ -886,6 +945,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -897,6 +957,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -908,6 +969,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", diff --git a/tests/data/groundtruth/docling_v2/wiki_duck.html.json b/tests/data/groundtruth/docling_v2/wiki_duck.html.json index 1116876..196c903 100644 --- a/tests/data/groundtruth/docling_v2/wiki_duck.html.json +++ b/tests/data/groundtruth/docling_v2/wiki_duck.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "wiki_duck", "origin": { "mimetype": "text/html", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -95,6 +96,7 @@ "$ref": "#/texts/250" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -124,6 +126,7 @@ "$ref": "#/texts/5" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -149,6 +152,7 @@ "$ref": "#/texts/10" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -158,6 +162,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -171,6 +176,7 @@ "$ref": "#/texts/11" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -180,6 +186,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -196,6 +203,7 @@ "$ref": "#/texts/13" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -212,6 +220,7 @@ "$ref": "#/texts/15" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -228,6 +237,7 @@ "$ref": "#/texts/17" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -241,6 +251,7 @@ "$ref": "#/texts/18" } ], + "content_layer": "body", "name": "header-1", "label": "section" }, @@ -281,6 +292,7 @@ "$ref": "#/texts/38" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -290,6 +302,7 @@ "$ref": "#/texts/20" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -299,6 +312,7 @@ "$ref": "#/texts/21" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -308,6 +322,7 @@ "$ref": "#/texts/22" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -317,6 +332,7 @@ "$ref": "#/texts/23" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -339,6 +355,7 @@ "$ref": "#/texts/28" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -348,6 +365,7 @@ "$ref": "#/texts/25" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -357,6 +375,7 @@ "$ref": "#/texts/26" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -366,6 +385,7 @@ "$ref": "#/texts/27" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -375,6 +395,7 @@ "$ref": "#/texts/28" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -397,6 +418,7 @@ "$ref": "#/texts/33" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -406,6 +428,7 @@ "$ref": "#/texts/30" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -415,6 +438,7 @@ "$ref": "#/texts/31" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -424,6 +448,7 @@ "$ref": "#/texts/32" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -433,6 +458,7 @@ "$ref": "#/texts/33" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -442,6 +468,7 @@ "$ref": "#/texts/34" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -458,6 +485,7 @@ "$ref": "#/texts/37" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -467,6 +495,7 @@ "$ref": "#/texts/36" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -476,6 +505,7 @@ "$ref": "#/texts/37" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -485,6 +515,7 @@ "$ref": "#/texts/38" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -903,6 +934,7 @@ "$ref": "#/texts/175" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -919,6 +951,7 @@ "$ref": "#/texts/177" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -928,6 +961,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" }, @@ -947,6 +981,7 @@ "$ref": "#/texts/180" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -966,6 +1001,7 @@ "$ref": "#/texts/183" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1006,6 +1042,7 @@ "$ref": "#/texts/193" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1022,6 +1059,7 @@ "$ref": "#/texts/195" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1038,6 +1076,7 @@ "$ref": "#/texts/197" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1051,6 +1090,7 @@ "$ref": "#/texts/256" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1079,6 +1119,7 @@ "$ref": "#/texts/262" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1254,6 +1295,7 @@ "$ref": "#/texts/319" } ], + "content_layer": "body", "name": "ordered list", "label": "ordered_list" }, @@ -1324,6 +1366,7 @@ "$ref": "#/texts/340" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1352,6 +1395,7 @@ "$ref": "#/texts/347" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1374,6 +1418,7 @@ "$ref": "#/texts/351" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1393,6 +1438,7 @@ "$ref": "#/texts/354" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1481,6 +1527,7 @@ "$ref": "#/texts/380" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1497,6 +1544,7 @@ "$ref": "#/texts/382" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1534,6 +1582,7 @@ "$ref": "#/texts/391" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1550,6 +1599,7 @@ "$ref": "#/texts/393" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -1559,6 +1609,7 @@ "$ref": "#/texts/341" }, "children": [], + "content_layer": "body", "name": "list", "label": "list" } @@ -1570,6 +1621,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Main page", @@ -1583,6 +1635,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Contents", @@ -1596,6 +1649,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Current events", @@ -1609,6 +1663,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Random article", @@ -1622,6 +1677,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "About Wikipedia", @@ -1635,6 +1691,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Contact us", @@ -1648,6 +1705,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Help", @@ -1661,6 +1719,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Learn to edit", @@ -1674,6 +1733,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Community portal", @@ -1687,6 +1747,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Recent changes", @@ -1700,6 +1761,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Upload file", @@ -1713,6 +1775,7 @@ "$ref": "#/groups/3" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Donate", @@ -1726,6 +1789,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Create account", @@ -1739,6 +1803,7 @@ "$ref": "#/groups/5" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Log in", @@ -1752,6 +1817,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Create account", @@ -1765,6 +1831,7 @@ "$ref": "#/groups/6" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Log in", @@ -1778,6 +1845,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Contributions", @@ -1791,6 +1859,7 @@ "$ref": "#/groups/7" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Talk", @@ -1808,6 +1877,7 @@ "$ref": "#/groups/9" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Contents", @@ -1820,6 +1890,7 @@ "$ref": "#/groups/9" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "(Top)", @@ -1837,6 +1908,7 @@ "$ref": "#/groups/10" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "1 Etymology", @@ -1854,6 +1926,7 @@ "$ref": "#/groups/11" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "2 Taxonomy", @@ -1871,6 +1944,7 @@ "$ref": "#/groups/12" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "3 Morphology", @@ -1888,6 +1962,7 @@ "$ref": "#/groups/13" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "4 Distribution and habitat", @@ -1905,6 +1980,7 @@ "$ref": "#/groups/14" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "5 Behaviour Toggle Behaviour subsection", @@ -1922,6 +1998,7 @@ "$ref": "#/groups/15" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "5.1 Feeding", @@ -1939,6 +2016,7 @@ "$ref": "#/groups/16" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "5.2 Breeding", @@ -1956,6 +2034,7 @@ "$ref": "#/groups/17" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "5.3 Communication", @@ -1973,6 +2052,7 @@ "$ref": "#/groups/18" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "5.4 Predators", @@ -1990,6 +2070,7 @@ "$ref": "#/groups/19" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "6 Relationship with humans Toggle Relationship with humans subsection", @@ -2007,6 +2088,7 @@ "$ref": "#/groups/20" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "6.1 Hunting", @@ -2024,6 +2106,7 @@ "$ref": "#/groups/21" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "6.2 Domestication", @@ -2041,6 +2124,7 @@ "$ref": "#/groups/22" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "6.3 Heraldry", @@ -2058,6 +2142,7 @@ "$ref": "#/groups/23" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "6.4 Cultural references", @@ -2075,6 +2160,7 @@ "$ref": "#/groups/24" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "7 See also", @@ -2092,6 +2178,7 @@ "$ref": "#/groups/25" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "8 Notes Toggle Notes subsection", @@ -2109,6 +2196,7 @@ "$ref": "#/groups/26" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "8.1 Citations", @@ -2126,6 +2214,7 @@ "$ref": "#/groups/27" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "8.2 Sources", @@ -2143,6 +2232,7 @@ "$ref": "#/groups/28" } ], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "9 External links", @@ -2220,6 +2310,7 @@ "$ref": "#/texts/341" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Duck", @@ -2231,6 +2322,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ac\u00e8h", @@ -2244,6 +2336,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Afrikaans", @@ -2257,6 +2350,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Alemannisch", @@ -2270,6 +2364,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u12a0\u121b\u122d\u129b", @@ -2283,6 +2378,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u00c6nglisc", @@ -2296,6 +2392,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0627\u0644\u0639\u0631\u0628\u064a\u0629", @@ -2309,6 +2406,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Aragon\u00e9s", @@ -2322,6 +2420,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0710\u072a\u0721\u071d\u0710", @@ -2335,6 +2434,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Arm\u00e3neashti", @@ -2348,6 +2448,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Asturianu", @@ -2361,6 +2462,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Atikamekw", @@ -2374,6 +2476,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0410\u0432\u0430\u0440", @@ -2387,6 +2490,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Aymar aru", @@ -2400,6 +2504,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u062a\u06c6\u0631\u06a9\u062c\u0647", @@ -2413,6 +2518,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Basa Bali", @@ -2426,6 +2532,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u09ac\u09be\u0982\u09b2\u09be", @@ -2439,6 +2546,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u95a9\u5357\u8a9e / B\u00e2n-l\u00e2m-g\u00fa", @@ -2452,6 +2560,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f", @@ -2465,6 +2574,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f (\u0442\u0430\u0440\u0430\u0448\u043a\u0435\u0432\u0456\u0446\u0430)", @@ -2478,6 +2588,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Bikol Central", @@ -2491,6 +2602,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0411\u044a\u043b\u0433\u0430\u0440\u0441\u043a\u0438", @@ -2504,6 +2616,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Brezhoneg", @@ -2517,6 +2630,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0411\u0443\u0440\u044f\u0430\u0434", @@ -2530,6 +2644,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Catal\u00e0", @@ -2543,6 +2658,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0427\u04d1\u0432\u0430\u0448\u043b\u0430", @@ -2556,6 +2672,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u010ce\u0161tina", @@ -2569,6 +2686,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "ChiShona", @@ -2582,6 +2700,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Cymraeg", @@ -2595,6 +2714,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Dagbanli", @@ -2608,6 +2728,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Dansk", @@ -2621,6 +2742,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Deitsch", @@ -2634,6 +2756,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Deutsch", @@ -2647,6 +2770,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0921\u094b\u091f\u0947\u0932\u0940", @@ -2660,6 +2784,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac", @@ -2673,6 +2798,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Emili\u00e0n e rumagn\u00f2l", @@ -2686,6 +2812,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Espa\u00f1ol", @@ -2699,6 +2826,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Esperanto", @@ -2712,6 +2840,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Euskara", @@ -2725,6 +2854,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0641\u0627\u0631\u0633\u06cc", @@ -2738,6 +2868,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Fran\u00e7ais", @@ -2751,6 +2882,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Gaeilge", @@ -2764,6 +2896,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Galego", @@ -2777,6 +2910,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0413\u04c0\u0430\u043b\u0433\u04c0\u0430\u0439", @@ -2790,6 +2924,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u8d1b\u8a9e", @@ -2803,6 +2938,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u06af\u06cc\u0644\u06a9\u06cc", @@ -2816,6 +2952,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\ud800\udf32\ud800\udf3f\ud800\udf44\ud800\udf39\ud800\udf43\ud800\udf3a", @@ -2829,6 +2966,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0917\u094b\u0902\u092f\u091a\u0940 \u0915\u094b\u0902\u0915\u0923\u0940 / G\u00f5ychi Konknni", @@ -2842,6 +2980,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u5ba2\u5bb6\u8a9e / Hak-k\u00e2-ng\u00ee", @@ -2855,6 +2994,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\ud55c\uad6d\uc5b4", @@ -2868,6 +3008,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hausa", @@ -2881,6 +3022,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0540\u0561\u0575\u0565\u0580\u0565\u0576", @@ -2894,6 +3036,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0939\u093f\u0928\u094d\u0926\u0940", @@ -2907,6 +3050,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hrvatski", @@ -2920,6 +3064,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ido", @@ -2933,6 +3078,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Bahasa Indonesia", @@ -2946,6 +3092,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "I\u00f1upiatun", @@ -2959,6 +3106,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u00cdslenska", @@ -2972,6 +3120,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Italiano", @@ -2985,6 +3134,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u05e2\u05d1\u05e8\u05d9\u05ea", @@ -2998,6 +3148,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Jawa", @@ -3011,6 +3162,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0c95\u0ca8\u0ccd\u0ca8\u0ca1", @@ -3024,6 +3176,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kapampangan", @@ -3037,6 +3190,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u10e5\u10d0\u10e0\u10d7\u10e3\u10da\u10d8", @@ -3050,6 +3204,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0915\u0949\u0936\u0941\u0930 / \u06a9\u0672\u0634\u064f\u0631", @@ -3063,6 +3218,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u049a\u0430\u0437\u0430\u049b\u0448\u0430", @@ -3076,6 +3232,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ikirundi", @@ -3089,6 +3246,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kongo", @@ -3102,6 +3260,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Krey\u00f2l ayisyen", @@ -3115,6 +3274,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u041a\u044b\u0440\u044b\u043a \u043c\u0430\u0440\u044b", @@ -3128,6 +3288,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0ea5\u0eb2\u0ea7", @@ -3141,6 +3302,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Latina", @@ -3154,6 +3316,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Latvie\u0161u", @@ -3167,6 +3330,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Lietuvi\u0173", @@ -3180,6 +3344,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Li Niha", @@ -3193,6 +3358,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ligure", @@ -3206,6 +3372,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Limburgs", @@ -3219,6 +3386,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ling\u00e1la", @@ -3232,6 +3400,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Malagasy", @@ -3245,6 +3414,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0d2e\u0d32\u0d2f\u0d3e\u0d33\u0d02", @@ -3258,6 +3428,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u092e\u0930\u093e\u0920\u0940", @@ -3271,6 +3442,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0645\u0627\u0632\u0650\u0631\u0648\u0646\u06cc", @@ -3284,6 +3456,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Bahasa Melayu", @@ -3297,6 +3470,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\uabc3\uabe4\uabc7\uabe9 \uabc2\uabe3\uabdf", @@ -3310,6 +3484,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u95a9\u6771\u8a9e / M\u00ecng-d\u0115\u0324ng-ng\u1e73\u0304", @@ -3323,6 +3498,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u041c\u043e\u043a\u0448\u0435\u043d\u044c", @@ -3336,6 +3512,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u041c\u043e\u043d\u0433\u043e\u043b", @@ -3349,6 +3526,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u1019\u103c\u1014\u103a\u1019\u102c\u1018\u102c\u101e\u102c", @@ -3362,6 +3540,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nederlands", @@ -3375,6 +3554,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Nedersaksies", @@ -3388,6 +3568,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0928\u0947\u092a\u093e\u0932\u0940", @@ -3401,6 +3582,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0928\u0947\u092a\u093e\u0932 \u092d\u093e\u0937\u093e", @@ -3414,6 +3596,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u65e5\u672c\u8a9e", @@ -3427,6 +3610,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u041d\u043e\u0445\u0447\u0438\u0439\u043d", @@ -3440,6 +3624,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Norsk nynorsk", @@ -3453,6 +3638,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Occitan", @@ -3466,6 +3652,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Oromoo", @@ -3479,6 +3666,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0a2a\u0a70\u0a1c\u0a3e\u0a2c\u0a40", @@ -3492,6 +3680,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Picard", @@ -3505,6 +3694,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Plattd\u00fc\u00fctsch", @@ -3518,6 +3708,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Polski", @@ -3531,6 +3722,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Portugu\u00eas", @@ -3544,6 +3736,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Q\u0131r\u0131mtatarca", @@ -3557,6 +3750,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Rom\u00e2n\u0103", @@ -3570,6 +3764,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0420\u0443\u0441\u0441\u043a\u0438\u0439", @@ -3583,6 +3778,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0421\u0430\u0445\u0430 \u0442\u044b\u043b\u0430", @@ -3596,6 +3792,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u1c65\u1c5f\u1c71\u1c5b\u1c5f\u1c72\u1c64", @@ -3609,6 +3806,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Sardu", @@ -3622,6 +3820,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Scots", @@ -3635,6 +3834,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Seeltersk", @@ -3648,6 +3848,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Shqip", @@ -3661,6 +3862,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Sicilianu", @@ -3674,6 +3876,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0dc3\u0dd2\u0d82\u0dc4\u0dbd", @@ -3687,6 +3890,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Simple English", @@ -3700,6 +3904,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0633\u0646\u068c\u064a", @@ -3713,6 +3918,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u06a9\u0648\u0631\u062f\u06cc", @@ -3726,6 +3932,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0421\u0440\u043f\u0441\u043a\u0438 / srpski", @@ -3739,6 +3946,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Srpskohrvatski / \u0441\u0440\u043f\u0441\u043a\u043e\u0445\u0440\u0432\u0430\u0442\u0441\u043a\u0438", @@ -3752,6 +3960,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Sunda", @@ -3765,6 +3974,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Svenska", @@ -3778,6 +3988,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Tagalog", @@ -3791,6 +4002,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0ba4\u0bae\u0bbf\u0bb4\u0bcd", @@ -3804,6 +4016,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Taqbaylit", @@ -3817,6 +4030,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0422\u0430\u0442\u0430\u0440\u0447\u0430 / tatar\u00e7a", @@ -3830,6 +4044,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0e44\u0e17\u0e22", @@ -3843,6 +4058,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "T\u00fcrk\u00e7e", @@ -3856,6 +4072,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0430", @@ -3869,6 +4086,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u0626\u06c7\u064a\u063a\u06c7\u0631\u0686\u06d5 / Uyghurche", @@ -3882,6 +4100,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Vahcuengh", @@ -3895,6 +4114,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ti\u1ebfng Vi\u1ec7t", @@ -3908,6 +4128,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Walon", @@ -3921,6 +4142,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u6587\u8a00", @@ -3934,6 +4156,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Winaray", @@ -3947,6 +4170,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u5434\u8bed", @@ -3960,6 +4184,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u7cb5\u8a9e", @@ -3973,6 +4198,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u017demait\u0117\u0161ka", @@ -3986,6 +4212,7 @@ "$ref": "#/groups/29" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "\u4e2d\u6587", @@ -3999,6 +4226,7 @@ "$ref": "#/groups/30" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Article", @@ -4012,6 +4240,7 @@ "$ref": "#/groups/30" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Talk", @@ -4025,6 +4254,7 @@ "$ref": "#/groups/32" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Read", @@ -4038,6 +4268,7 @@ "$ref": "#/groups/32" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "View source", @@ -4051,6 +4282,7 @@ "$ref": "#/groups/32" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "View history", @@ -4064,6 +4296,7 @@ "$ref": "#/groups/33" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Read", @@ -4077,6 +4310,7 @@ "$ref": "#/groups/33" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "View source", @@ -4090,6 +4324,7 @@ "$ref": "#/groups/33" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "View history", @@ -4103,6 +4338,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "What links here", @@ -4116,6 +4352,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Related changes", @@ -4129,6 +4366,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Upload file", @@ -4142,6 +4380,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Special pages", @@ -4155,6 +4394,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Permanent link", @@ -4168,6 +4408,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Page information", @@ -4181,6 +4422,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Cite this page", @@ -4194,6 +4436,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Get shortened URL", @@ -4207,6 +4450,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Download QR code", @@ -4220,6 +4464,7 @@ "$ref": "#/groups/34" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wikidata item", @@ -4233,6 +4478,7 @@ "$ref": "#/groups/35" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Download as PDF", @@ -4246,6 +4492,7 @@ "$ref": "#/groups/35" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Printable version", @@ -4259,6 +4506,7 @@ "$ref": "#/groups/36" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wikimedia Commons", @@ -4272,6 +4520,7 @@ "$ref": "#/groups/36" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wikiquote", @@ -4285,6 +4534,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Duck is the common name for numerous species of waterfowl in the family Anatidae. Ducks are generally smaller and shorter-necked than swans and geese, which are members of the same family. Divided among several subfamilies, they are a form taxon; they do not represent a monophyletic group (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly aquatic birds, and may be found in both fresh water and sea water.", @@ -4296,6 +4546,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks are sometimes confused with several types of unrelated water birds with similar forms, such as loons or divers, grebes, gallinules and coots.", @@ -4329,6 +4580,7 @@ "$ref": "#/pictures/6" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Etymology", @@ -4341,6 +4593,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The word duck comes from Old English d\u016bce 'diver', a derivative of the verb *d\u016bcan 'to duck, bend down low as if to get under something, or dive', because of the way many species in the dabbling duck group feed by upending; compare with Dutch duiken and German tauchen 'to dive'.", @@ -4352,6 +4605,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Pacific black duck displaying the characteristic upending \"duck\"", @@ -4363,6 +4617,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "This word replaced Old English ened /\u00e6nid 'duck', possibly to avoid confusion with other words, such as ende 'end' with similar forms. Other Germanic languages still have similar words for duck, for example, Dutch eend, German Ente and Norwegian and. The word ened /\u00e6nid was inherited from Proto-Indo-European; cf. Latin anas \"duck\", Lithuanian \u00e1ntis 'duck', Ancient Greek \u03bd\u1fc6\u03c3\u03c3\u03b1 /\u03bd\u1fc6\u03c4\u03c4\u03b1 (n\u0113ssa /n\u0113tta) 'duck', and Sanskrit \u0101t\u00ed 'water bird', among others.", @@ -4374,6 +4629,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A duckling is a young duck in downy plumage[1] or baby duck,[2] but in the food trade a young domestic duck which has just reached adult size and bulk and its meat is still fully tender, is sometimes labelled as a duckling.", @@ -4385,6 +4641,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A male is called a drake and the female is called a duck, or in ornithology a hen.[3][4]", @@ -4396,6 +4653,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Male mallard.", @@ -4407,6 +4665,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Wood ducks.", @@ -4431,6 +4690,7 @@ "$ref": "#/texts/212" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Taxonomy", @@ -4443,6 +4703,7 @@ "$ref": "#/texts/208" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "All ducks belong to the biological order Anseriformes, a group that contains the ducks, geese and swans, as well as the screamers, and the magpie goose.[5] All except the screamers belong to the biological family Anatidae.[5] Within the family, ducks are split into a variety of subfamilies and 'tribes'. The number and composition of these subfamilies and tribes is the cause of considerable disagreement among taxonomists.[5] Some base their decisions on morphological characteristics, others on shared behaviours or genetic studies.[6][7] The number of suggested subfamilies containing ducks ranges from two to five.[8][9] The significant level of hybridisation that occurs among wild ducks complicates efforts to tease apart the relationships between various species.[9]", @@ -4454,6 +4715,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Mallard landing in approach", @@ -4465,6 +4727,7 @@ "$ref": "#/texts/208" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "In most modern classifications, the so-called 'true ducks' belong to the subfamily Anatinae, which is further split into a varying number of tribes.[10] The largest of these, the Anatini, contains the 'dabbling' or 'river' ducks \u2013 named for their method of feeding primarily at the surface of fresh water.[11] The 'diving ducks', also named for their primary feeding method, make up the tribe Aythyini.[12] The 'sea ducks' of the tribe Mergini are diving ducks which specialise on fish and shellfish and spend a majority of their lives in saltwater.[13] The tribe Oxyurini contains the 'stifftails', diving ducks notable for their small size and stiff, upright tails.[14]", @@ -4476,6 +4739,7 @@ "$ref": "#/texts/208" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A number of other species called ducks are not considered to be 'true ducks', and are typically placed in other subfamilies or tribes. The whistling ducks are assigned either to a tribe (Dendrocygnini) in the subfamily Anatinae or the subfamily Anserinae,[15] or to their own subfamily (Dendrocygninae) or family (Dendrocyganidae).[9][16] The freckled duck of Australia is either the sole member of the tribe Stictonettini in the subfamily Anserinae,[15] or in its own family, the Stictonettinae.[9] The shelducks make up the tribe Tadornini in the family Anserinae in some classifications,[15] and their own subfamily, Tadorninae, in others,[17] while the steamer ducks are either placed in the family Anserinae in the tribe Tachyerini[15] or lumped with the shelducks in the tribe Tadorini.[9] The perching ducks make up in the tribe Cairinini in the subfamily Anserinae in some classifications, while that tribe is eliminated in other classifications and its members assigned to the tribe Anatini.[9] The torrent duck is generally included in the subfamily Anserinae in the monotypic tribe Merganettini,[15] but is sometimes included in the tribe Tadornini.[18] The pink-eared duck is sometimes included as a true duck either in the tribe Anatini[15] or the tribe Malacorhynchini,[19] and other times is included with the shelducks in the tribe Tadornini.[15]", @@ -4497,6 +4761,7 @@ "$ref": "#/texts/216" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Morphology", @@ -4509,6 +4774,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Male Mandarin duck", @@ -4520,6 +4786,7 @@ "$ref": "#/texts/213" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The overall body plan of ducks is elongated and broad, and they are also relatively long-necked, albeit not as long-necked as the geese and swans. The body shape of diving ducks varies somewhat from this in being more rounded. The bill is usually broad and contains serrated pectens, which are particularly well defined in the filter-feeding species. In the case of some fishing species the bill is long and strongly serrated. The scaled legs are strong and well developed, and generally set far back on the body, more so in the highly aquatic species. The wings are very strong and are generally short and pointed, and the flight of ducks requires fast continuous strokes, requiring in turn strong wing muscles. Three species of steamer duck are almost flightless, however. Many species of duck are temporarily flightless while moulting; they seek out protected habitat with good food supplies during this period. This moult typically precedes migration.", @@ -4531,6 +4798,7 @@ "$ref": "#/texts/213" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The drakes of northern species often have extravagant plumage, but that is moulted in summer to give a more female-like appearance, the \"eclipse\" plumage. Southern resident species typically show less sexual dimorphism, although there are exceptions such as the paradise shelduck of New Zealand, which is both strikingly sexually dimorphic and in which the female's plumage is brighter than that of the male. The plumage of juvenile birds generally resembles that of the female. Female ducks have evolved to have a corkscrew shaped vagina to prevent rape.", @@ -4555,6 +4823,7 @@ "$ref": "#/texts/221" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Distribution and habitat", @@ -4567,6 +4836,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Flying steamer ducks in Ushuaia, Argentina", @@ -4578,6 +4848,7 @@ "$ref": "#/texts/217" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks have a cosmopolitan distribution, and are found on every continent except Antarctica.[5] Several species manage to live on subantarctic islands, including South Georgia and the Auckland Islands.[20] Ducks have reached a number of isolated oceanic islands, including the Hawaiian Islands, Micronesia and the Gal\u00e1pagos Islands, where they are often vagrants and less often residents.[21][22] A handful are endemic to such far-flung islands.[21]", @@ -4589,6 +4860,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Female mallard in Cornwall, England", @@ -4600,6 +4872,7 @@ "$ref": "#/texts/217" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Some duck species, mainly those breeding in the temperate and Arctic Northern Hemisphere, are migratory; those in the tropics are generally not. Some ducks, particularly in Australia where rainfall is erratic, are nomadic, seeking out the temporary lakes and pools that form after localised heavy rain.[23]", @@ -4624,6 +4897,7 @@ "$ref": "#/texts/238" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Behaviour", @@ -4661,6 +4935,7 @@ "$ref": "#/texts/231" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Feeding", @@ -4673,6 +4948,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Pecten along the bill", @@ -4684,6 +4960,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Mallard duckling preening", @@ -4695,6 +4972,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks eat food sources such as grasses, aquatic plants, fish, insects, small amphibians, worms, and small molluscs.", @@ -4706,6 +4984,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Dabbling ducks feed on the surface of water or on land, or as deep as they can reach by up-ending without completely submerging.[24] Along the edge of the bill, there is a comb-like structure called a pecten. This strains the water squirting from the side of the bill and traps any food. The pecten is also used to preen feathers and to hold slippery food items.", @@ -4717,6 +4996,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Diving ducks and sea ducks forage deep underwater. To be able to submerge more easily, the diving ducks are heavier than dabbling ducks, and therefore have more difficulty taking off to fly.", @@ -4728,6 +5008,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A few specialized species such as the mergansers are adapted to catch and swallow large fish.", @@ -4739,6 +5020,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The others have the characteristic wide flat bill adapted to dredging-type jobs such as pulling up waterweed, pulling worms and small molluscs out of mud, searching for insect larvae, and bulk jobs such as dredging out, holding, turning head first, and swallowing a squirming frog. To avoid injury when digging into sediment it has no cere, but the nostrils come out through hard horn.", @@ -4750,6 +5032,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The Guardian published an article advising that ducks should not be fed with bread because it damages the health of the ducks and pollutes waterways.[25]", @@ -4768,6 +5051,7 @@ "$ref": "#/texts/234" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Breeding", @@ -4780,6 +5064,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "A Muscovy duckling", @@ -4791,6 +5076,7 @@ "$ref": "#/texts/232" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks generally only have one partner at a time, although the partnership usually only lasts one year.[26] Larger species and the more sedentary species (like fast-river specialists) tend to have pair-bonds that last numerous years.[27] Most duck species breed once a year, choosing to do so in favourable conditions (spring/summer or wet seasons). Ducks also tend to make a nest before breeding, and, after hatching, lead their ducklings to water. Mother ducks are very caring and protective of their young, but may abandon some of their ducklings if they are physically stuck in an area they cannot get out of (such as nesting in an enclosed courtyard) or are not prospering due to genetic defects or sickness brought about by hypothermia, starvation, or disease. Ducklings can also be orphaned by inconsistent late hatching where a few eggs hatch after the mother has abandoned the nest and led her ducklings to water.[28]", @@ -4809,6 +5095,7 @@ "$ref": "#/texts/237" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Communication", @@ -4821,6 +5108,7 @@ "$ref": "#/texts/235" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Female mallard ducks (as well as several other species in the genus Anas, such as the American and Pacific black ducks, spot-billed duck, northern pintail and common teal) make the classic \"quack\" sound while males make a similar but raspier sound that is sometimes written as \"breeeeze\",[29][self-published source?] but, despite widespread misconceptions, most species of duck do not \"quack\".[30] In general, ducks make a range of calls, including whistles, cooing, yodels and grunts. For example, the scaup \u2013 which are diving ducks \u2013 make a noise like \"scaup\" (hence their name). Calls may be loud displaying calls or quieter contact calls.", @@ -4832,6 +5120,7 @@ "$ref": "#/texts/235" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A common urban legend claims that duck quacks do not echo; however, this has been proven to be false. This myth was first debunked by the Acoustics Research Centre at the University of Salford in 2003 as part of the British Association's Festival of Science.[31] It was also debunked in one of the earlier episodes of the popular Discovery Channel television show MythBusters.[32]", @@ -4853,6 +5142,7 @@ "$ref": "#/texts/241" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Predators", @@ -4865,6 +5155,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Ringed teal", @@ -4876,6 +5167,7 @@ "$ref": "#/texts/238" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks have many predators. Ducklings are particularly vulnerable, since their inability to fly makes them easy prey not only for predatory birds but also for large fish like pike, crocodilians, predatory testudines such as the alligator snapping turtle, and other aquatic hunters, including fish-eating birds such as herons. Ducks' nests are raided by land-based predators, and brooding females may be caught unaware on the nest by mammals, such as foxes, or large birds, such as hawks or owls.", @@ -4887,6 +5179,7 @@ "$ref": "#/texts/238" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Adult ducks are fast fliers, but may be caught on the water by large aquatic predators including big fish such as the North American muskie and the European pike. In flight, ducks are safe from all but a few predators such as humans and the peregrine falcon, which uses its speed and strength to catch ducks.", @@ -4911,6 +5204,7 @@ "$ref": "#/texts/252" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Relationship with humans", @@ -4930,6 +5224,7 @@ "$ref": "#/texts/245" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Hunting", @@ -4942,6 +5237,7 @@ "$ref": "#/texts/243" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Humans have hunted ducks since prehistoric times. Excavations of middens in California dating to 7800 \u2013 6400 BP have turned up bones of ducks, including at least one now-extinct flightless species.[33] Ducks were captured in \"significant numbers\" by Holocene inhabitants of the lower Ohio River valley, suggesting they took advantage of the seasonal bounty provided by migrating waterfowl.[34] Neolithic hunters in locations as far apart as the Caribbean,[35] Scandinavia,[36] Egypt,[37] Switzerland,[38] and China relied on ducks as a source of protein for some or all of the year.[39] Archeological evidence shows that M\u0101ori people in New Zealand hunted the flightless Finsch's duck, possibly to extinction, though rat predation may also have contributed to its fate.[40] A similar end awaited the Chatham duck, a species with reduced flying capabilities which went extinct shortly after its island was colonised by Polynesian settlers.[41] It is probable that duck eggs were gathered by Neolithic hunter-gathers as well, though hard evidence of this is uncommon.[35][42]", @@ -4953,6 +5249,7 @@ "$ref": "#/texts/243" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "In many areas, wild ducks (including ducks farmed and released into the wild) are hunted for food or sport,[43] by shooting, or by being trapped using duck decoys. Because an idle floating duck or a duck squatting on land cannot react to fly or move quickly, \"a sitting duck\" has come to mean \"an easy target\". These ducks may be contaminated by pollutants such as PCBs.[44]", @@ -4971,6 +5268,7 @@ "$ref": "#/texts/248" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Domestication", @@ -4983,6 +5281,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Indian Runner ducks, a common breed of domestic ducks", @@ -4994,6 +5293,7 @@ "$ref": "#/texts/246" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks have many economic uses, being farmed for their meat, eggs, and feathers (particularly their down). Approximately 3 billion ducks are slaughtered each year for meat worldwide.[45] They are also kept and bred by aviculturists and often displayed in zoos. Almost all the varieties of domestic ducks are descended from the mallard (Anas platyrhynchos), apart from the Muscovy duck (Cairina moschata).[46][47] The Call duck is another example of a domestic duck breed. Its name comes from its original use established by hunters, as a decoy to attract wild mallards from the sky, into traps set for them on the ground. The call duck is the world's smallest domestic duck breed, as it weighs less than 1\u00a0kg (2.2\u00a0lb).[48]", @@ -5012,6 +5312,7 @@ "$ref": "#/texts/251" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Heraldry", @@ -5024,6 +5325,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "caption", "prov": [], "orig": "Three black-colored ducks in the coat of arms of Maaninka[49]", @@ -5035,6 +5337,7 @@ "$ref": "#/texts/249" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Ducks appear on several coats of arms, including the coat of arms of Lub\u0101na (Latvia)[50] and the coat of arms of F\u00f6gl\u00f6 (\u00c5land).[51]", @@ -5053,6 +5356,7 @@ "$ref": "#/texts/254" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Cultural references", @@ -5065,6 +5369,7 @@ "$ref": "#/texts/252" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "In 2002, psychologist Richard Wiseman and colleagues at the University of Hertfordshire, UK, finished a year-long LaughLab experiment, concluding that of all animals, ducks attract the most humor and silliness; he said, \"If you're going to tell a joke involving an animal, make it a duck.\"[52] The word \"duck\" may have become an inherently funny word in many languages, possibly because ducks are seen as silly in their looks or behavior. Of the many ducks in fiction, many are cartoon characters, such as Walt Disney's Donald Duck, and Warner Bros.' Daffy Duck. Howard the Duck started as a comic book character in 1973[53][54] and was made into a movie in 1986.", @@ -5076,6 +5381,7 @@ "$ref": "#/texts/252" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck as the mascot for the fictional youth hockey team who are protagonists of the movie, based on the duck being described as a fierce fighter. This led to the duck becoming the nickname and mascot for the eventual National Hockey League professional team of the Anaheim Ducks, who were founded with the name the Mighty Ducks of Anaheim.[citation needed] The duck is also the nickname of the University of Oregon sports teams as well as the Long Island Ducks minor league baseball team.[55]", @@ -5094,6 +5400,7 @@ "$ref": "#/groups/38" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "See also", @@ -5106,6 +5413,7 @@ "$ref": "#/groups/37" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Birds portal", @@ -5119,6 +5427,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Domestic duck", @@ -5132,6 +5441,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Duck as food", @@ -5145,6 +5455,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Duck test", @@ -5158,6 +5469,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Duck breeds", @@ -5171,6 +5483,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Fictional ducks", @@ -5184,6 +5497,7 @@ "$ref": "#/groups/38" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Rubber duck", @@ -5204,6 +5518,7 @@ "$ref": "#/texts/320" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Notes", @@ -5220,6 +5535,7 @@ "$ref": "#/groups/39" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Citations", @@ -5232,6 +5548,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Duckling\". The American Heritage Dictionary of the English Language, Fourth Edition. Houghton Mifflin Company. 2006. Retrieved 2015-05-22.", @@ -5245,6 +5562,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Duckling\". Kernerman English Multilingual Dictionary (Beta Version). K. Dictionaries Ltd. 2000\u20132006. Retrieved 2015-05-22.", @@ -5258,6 +5576,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Dohner, Janet Vorwald (2001). The Encyclopedia of Historic and Endangered Livestock and Poultry Breeds. Yale University Press. ISBN\u00a0978-0300138139.", @@ -5271,6 +5590,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Visca, Curt; Visca, Kelley (2003). How to Draw Cartoon Birds. The Rosen Publishing Group. ISBN\u00a09780823961566.", @@ -5284,6 +5604,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ a b c d Carboneras 1992, p.\u00a0536.", @@ -5297,6 +5618,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Livezey 1986, pp.\u00a0737\u2013738.", @@ -5310,6 +5632,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Madsen, McHugh & de Kloet 1988, p.\u00a0452.", @@ -5323,6 +5646,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Donne-Gouss\u00e9, Laudet & H\u00e4nni 2002, pp.\u00a0353\u2013354.", @@ -5336,6 +5660,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ a b c d e f Carboneras 1992, p.\u00a0540.", @@ -5349,6 +5674,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Elphick, Dunning & Sibley 2001, p.\u00a0191.", @@ -5362,6 +5688,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Kear 2005, p.\u00a0448.", @@ -5375,6 +5702,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Kear 2005, p.\u00a0622\u2013623.", @@ -5388,6 +5716,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Kear 2005, p.\u00a0686.", @@ -5401,6 +5730,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Elphick, Dunning & Sibley 2001, p.\u00a0193.", @@ -5414,6 +5744,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ a b c d e f g Carboneras 1992, p.\u00a0537.", @@ -5427,6 +5758,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ American Ornithologists' Union 1998, p.\u00a0xix.", @@ -5440,6 +5772,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ American Ornithologists' Union 1998.", @@ -5453,6 +5786,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Carboneras 1992, p.\u00a0538.", @@ -5466,6 +5800,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Christidis & Boles 2008, p.\u00a062.", @@ -5479,6 +5814,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Shirihai 2008, pp.\u00a0239, 245.", @@ -5492,6 +5828,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ a b Pratt, Bruner & Berrett 1987, pp.\u00a098\u2013107.", @@ -5505,6 +5842,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Fitter, Fitter & Hosking 2000, pp.\u00a052\u20133.", @@ -5518,6 +5856,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Pacific Black Duck\". www.wiresnr.org. Retrieved 2018-04-27.", @@ -5531,6 +5870,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Ogden, Evans. \"Dabbling Ducks\". CWE. Retrieved 2006-11-02.", @@ -5544,6 +5884,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Karl Mathiesen (16 March 2015). \"Don't feed the ducks bread, say conservationists\". The Guardian. Retrieved 13 November 2016.", @@ -5557,6 +5898,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Rohwer, Frank C.; Anderson, Michael G. (1988). \"Female-Biased Philopatry, Monogamy, and the Timing of Pair Formation in Migratory Waterfowl\". Current Ornithology. pp.\u00a0187\u2013221. doi:10.1007/978-1-4615-6787-5_4. ISBN\u00a0978-1-4615-6789-9.", @@ -5570,6 +5912,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Smith, Cyndi M.; Cooke, Fred; Robertson, Gregory J.; Goudie, R. Ian; Boyd, W. Sean (2000). \"Long-Term Pair Bonds in Harlequin Ducks\". The Condor. 102 (1): 201\u2013205. doi:10.1093/condor/102.1.201. hdl:10315/13797.", @@ -5583,6 +5926,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"If You Find An Orphaned Duckling - Wildlife Rehabber\". wildliferehabber.com. Archived from the original on 2018-09-23. Retrieved 2018-12-22.", @@ -5596,6 +5940,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Carver, Heather (2011). The Duck Bible. Lulu.com. ISBN\u00a09780557901562.[self-published source]", @@ -5609,6 +5954,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Titlow, Budd (2013-09-03). Bird Brains: Inside the Strange Minds of Our Fine Feathered Friends. Rowman & Littlefield. ISBN\u00a09780762797707.", @@ -5622,6 +5968,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Amos, Jonathan (2003-09-08). \"Sound science is quackers\". BBC News. Retrieved 2006-11-02.", @@ -5635,6 +5982,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Mythbusters Episode 8\". 12 December 2003.", @@ -5648,6 +5996,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Erlandson 1994, p.\u00a0171.", @@ -5661,6 +6010,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Jeffries 2008, pp.\u00a0168, 243.", @@ -5674,6 +6024,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ a b Sued-Badillo 2003, p.\u00a065.", @@ -5687,6 +6038,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Thorpe 1996, p.\u00a068.", @@ -5700,6 +6052,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Maisels 1999, p.\u00a042.", @@ -5713,6 +6066,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Rau 1876, p.\u00a0133.", @@ -5726,6 +6080,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Higman 2012, p.\u00a023.", @@ -5739,6 +6094,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Hume 2012, p.\u00a053.", @@ -5752,6 +6108,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Hume 2012, p.\u00a052.", @@ -5765,6 +6122,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Fieldhouse 2002, p.\u00a0167.", @@ -5778,6 +6136,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Livingston, A. D. (1998-01-01). Guide to Edible Plants and Animals. Wordsworth Editions, Limited. ISBN\u00a09781853263774.", @@ -5791,6 +6150,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Study plan for waterfowl injury assessment: Determining PCB concentrations in Hudson river resident waterfowl\" (PDF). New York State Department of Environmental Conservation. US Department of Commerce. December 2008. p.\u00a03. Archived (PDF) from the original on 2022-10-09. Retrieved 2 July 2019.", @@ -5804,6 +6164,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"FAOSTAT\". www.fao.org. Retrieved 2019-10-25.", @@ -5817,6 +6178,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Anas platyrhynchos, Domestic Duck; DigiMorph Staff - The University of Texas at Austin\". Digimorph.org. Retrieved 2012-12-23.", @@ -5830,6 +6192,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Sy Montgomery. \"Mallard; Encyclop\u00e6dia Britannica\". Britannica.com. Retrieved 2012-12-23.", @@ -5843,6 +6206,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Glenday, Craig (2014). Guinness World Records. Guinness World Records Limited. pp.\u00a0135. ISBN\u00a0978-1-908843-15-9.", @@ -5856,6 +6220,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Suomen kunnallisvaakunat (in Finnish). Suomen Kunnallisliitto. 1982. p.\u00a0147. ISBN\u00a0951-773-085-3.", @@ -5869,6 +6234,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Lub\u0101nas simbolika\" (in Latvian). Retrieved September 9, 2021.", @@ -5882,6 +6248,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"F\u00f6gl\u00f6\" (in Swedish). Retrieved September 9, 2021.", @@ -5895,6 +6262,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Young, Emma. \"World's funniest joke revealed\". New Scientist. Retrieved 7 January 2019.", @@ -5908,6 +6276,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"Howard the Duck (character)\". Grand Comics Database.", @@ -5921,6 +6290,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ Sanderson, Peter; Gilbert, Laura (2008). \"1970s\". Marvel Chronicle A Year by Year History. London, United Kingdom: Dorling Kindersley. p.\u00a0161. ISBN\u00a0978-0756641238. December saw the debut of the cigar-smoking Howard the Duck. In this story by writer Steve Gerber and artist Val Mayerik, various beings from different realities had begun turning up in the Man-Thing's Florida swamp, including this bad-tempered talking duck.", @@ -5934,6 +6304,7 @@ "$ref": "#/groups/39" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "^ \"The Duck\". University of Oregon Athletics. Retrieved 2022-01-20.", @@ -5951,6 +6322,7 @@ "$ref": "#/groups/40" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Sources", @@ -5963,6 +6335,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "American Ornithologists' Union (1998). Checklist of North American Birds (PDF). Washington, DC: American Ornithologists' Union. ISBN\u00a0978-1-891276-00-2. Archived (PDF) from the original on 2022-10-09.", @@ -5976,6 +6349,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Carboneras, Carlos (1992). del Hoyo, Josep; Elliott, Andrew; Sargatal, Jordi (eds.). Handbook of the Birds of the World. Vol.\u00a01: Ostrich to Ducks. Barcelona: Lynx Edicions. ISBN\u00a0978-84-87334-10-8.", @@ -5989,6 +6363,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Christidis, Les; Boles, Walter E., eds. (2008). Systematics and Taxonomy of Australian Birds. Collingwood, VIC: Csiro Publishing. ISBN\u00a0978-0-643-06511-6.", @@ -6002,6 +6377,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Donne-Gouss\u00e9, Carole; Laudet, Vincent; H\u00e4nni, Catherine (July 2002). \"A molecular phylogeny of Anseriformes based on mitochondrial DNA analysis\". Molecular Phylogenetics and Evolution. 23 (3): 339\u2013356. Bibcode:2002MolPE..23..339D. doi:10.1016/S1055-7903(02)00019-2. PMID\u00a012099792.", @@ -6015,6 +6391,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Elphick, Chris; Dunning, John B. Jr.; Sibley, David, eds. (2001). The Sibley Guide to Bird Life and Behaviour. London: Christopher Helm. ISBN\u00a0978-0-7136-6250-4.", @@ -6028,6 +6405,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Erlandson, Jon M. (1994). Early Hunter-Gatherers of the California Coast. New York, NY: Springer Science & Business Media. ISBN\u00a0978-1-4419-3231-0.", @@ -6041,6 +6419,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Fieldhouse, Paul (2002). Food, Feasts, and Faith: An Encyclopedia of Food Culture in World Religions. Vol.\u00a0I: A\u2013K. Santa Barbara: ABC-CLIO. ISBN\u00a0978-1-61069-412-4.", @@ -6054,6 +6433,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Fitter, Julian; Fitter, Daniel; Hosking, David (2000). Wildlife of the Gal\u00e1pagos. Princeton, NJ: Princeton University Press. ISBN\u00a0978-0-691-10295-5.", @@ -6067,6 +6447,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Higman, B. W. (2012). How Food Made History. Chichester, UK: John Wiley & Sons. ISBN\u00a0978-1-4051-8947-7.", @@ -6080,6 +6461,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Hume, Julian H. (2012). Extinct Birds. London: Christopher Helm. ISBN\u00a0978-1-4729-3744-5.", @@ -6093,6 +6475,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Jeffries, Richard (2008). Holocene Hunter-Gatherers of the Lower Ohio River Valley. Tuscaloosa: University of Alabama Press. ISBN\u00a0978-0-8173-1658-7.", @@ -6106,6 +6489,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Kear, Janet, ed. (2005). Ducks, Geese and Swans: Species Accounts (Cairina to Mergus). Bird Families of the World. Oxford: Oxford University Press. ISBN\u00a0978-0-19-861009-0.", @@ -6119,6 +6503,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Livezey, Bradley C. (October 1986). \"A phylogenetic analysis of recent Anseriform genera using morphological characters\" (PDF). The Auk. 103 (4): 737\u2013754. doi:10.1093/auk/103.4.737. Archived (PDF) from the original on 2022-10-09.", @@ -6132,6 +6517,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Madsen, Cort S.; McHugh, Kevin P.; de Kloet, Siwo R. (July 1988). \"A partial classification of waterfowl (Anatidae) based on single-copy DNA\" (PDF). The Auk. 105 (3): 452\u2013459. doi:10.1093/auk/105.3.452. Archived (PDF) from the original on 2022-10-09.", @@ -6145,6 +6531,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Maisels, Charles Keith (1999). Early Civilizations of the Old World. London: Routledge. ISBN\u00a0978-0-415-10975-8.", @@ -6158,6 +6545,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Pratt, H. Douglas; Bruner, Phillip L.; Berrett, Delwyn G. (1987). A Field Guide to the Birds of Hawaii and the Tropical Pacific. Princeton, NJ: Princeton University Press. ISBN\u00a00-691-02399-9.", @@ -6171,6 +6559,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Rau, Charles (1876). Early Man in Europe. New York: Harper & Brothers. LCCN\u00a005040168.", @@ -6184,6 +6573,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Shirihai, Hadoram (2008). A Complete Guide to Antarctic Wildlife. Princeton, NJ, US: Princeton University Press. ISBN\u00a0978-0-691-13666-0.", @@ -6197,6 +6587,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Sued-Badillo, Jalil (2003). Autochthonous Societies. General History of the Caribbean. Paris: UNESCO. ISBN\u00a0978-92-3-103832-7.", @@ -6210,6 +6601,7 @@ "$ref": "#/groups/40" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Thorpe, I. J. (1996). The Origins of Agriculture in Europe. New York: Routledge. ISBN\u00a0978-0-415-08009-5.", @@ -6254,6 +6646,7 @@ "$ref": "#/groups/48" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "External links", @@ -6266,6 +6659,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Definitions from Wiktionary", @@ -6279,6 +6673,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Media from Commons", @@ -6292,6 +6687,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Quotations from Wikiquote", @@ -6305,6 +6701,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Recipes from Wikibooks", @@ -6318,6 +6715,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Taxa from Wikispecies", @@ -6331,6 +6729,7 @@ "$ref": "#/groups/41" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Data from Wikidata", @@ -6344,6 +6743,7 @@ "$ref": "#/groups/42" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "list of books (useful looking abstracts)", @@ -6357,6 +6757,7 @@ "$ref": "#/groups/42" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ducks on postage stamps Archived 2013-05-13 at the Wayback Machine", @@ -6370,6 +6771,7 @@ "$ref": "#/groups/42" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "", @@ -6383,6 +6785,7 @@ "$ref": "#/groups/42" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ducks at a Distance, by Rob Hines at Project Gutenberg - A modern illustrated guide to identification of US waterfowl", @@ -6396,6 +6799,7 @@ "$ref": "#/groups/43" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Ducks", @@ -6409,6 +6813,7 @@ "$ref": "#/groups/43" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Game birds", @@ -6422,6 +6827,7 @@ "$ref": "#/groups/43" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Bird common names", @@ -6435,6 +6841,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "All accuracy disputes", @@ -6448,6 +6855,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Accuracy disputes from February 2020", @@ -6461,6 +6869,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "CS1 Finnish-language sources (fi)", @@ -6474,6 +6883,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "CS1 Latvian-language sources (lv)", @@ -6487,6 +6897,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "CS1 Swedish-language sources (sv)", @@ -6500,6 +6911,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles with short description", @@ -6513,6 +6925,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Short description is different from Wikidata", @@ -6526,6 +6939,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wikipedia indefinitely move-protected pages", @@ -6539,6 +6953,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wikipedia indefinitely semi-protected pages", @@ -6552,6 +6967,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles with 'species' microformats", @@ -6565,6 +6981,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing Old English (ca. 450-1100)-language text", @@ -6578,6 +6995,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing Dutch-language text", @@ -6591,6 +7009,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing German-language text", @@ -6604,6 +7023,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing Norwegian-language text", @@ -6617,6 +7037,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing Lithuanian-language text", @@ -6630,6 +7051,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing Ancient Greek (to 1453)-language text", @@ -6643,6 +7065,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "All articles with self-published sources", @@ -6656,6 +7079,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles with self-published sources from February 2020", @@ -6669,6 +7093,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "All articles with unsourced statements", @@ -6682,6 +7107,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles with unsourced statements from January 2022", @@ -6695,6 +7121,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "CS1: long volume value", @@ -6708,6 +7135,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Pages using Sister project links with wikidata mismatch", @@ -6721,6 +7149,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Pages using Sister project links with hidden wikidata", @@ -6734,6 +7163,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Webarchive template wayback links", @@ -6747,6 +7177,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles with Project Gutenberg links", @@ -6760,6 +7191,7 @@ "$ref": "#/groups/44" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Articles containing video clips", @@ -6773,6 +7205,7 @@ "$ref": "#/groups/45" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "This page was last edited on 21 September 2024, at 12:11\u00a0(UTC).", @@ -6786,6 +7219,7 @@ "$ref": "#/groups/45" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Text is available under the Creative Commons Attribution-ShareAlike License 4.0;\nadditional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia\u00ae is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.", @@ -6799,6 +7233,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Privacy policy", @@ -6812,6 +7247,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "About Wikipedia", @@ -6825,6 +7261,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Disclaimers", @@ -6838,6 +7275,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Contact Wikipedia", @@ -6851,6 +7289,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Code of Conduct", @@ -6864,6 +7303,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Developers", @@ -6877,6 +7317,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Statistics", @@ -6890,6 +7331,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Cookie statement", @@ -6903,6 +7345,7 @@ "$ref": "#/groups/46" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Mobile view", @@ -6916,6 +7359,7 @@ "$ref": "#/groups/47" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "", @@ -6929,6 +7373,7 @@ "$ref": "#/groups/47" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "", @@ -6944,6 +7389,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -6957,6 +7403,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -6970,6 +7417,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -6983,6 +7431,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -6996,6 +7445,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7013,6 +7463,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7030,6 +7481,7 @@ "$ref": "#/texts/200" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7047,6 +7499,7 @@ "$ref": "#/texts/208" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7064,6 +7517,7 @@ "$ref": "#/texts/213" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7081,6 +7535,7 @@ "$ref": "#/texts/217" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7098,6 +7553,7 @@ "$ref": "#/texts/217" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7115,6 +7571,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7132,6 +7589,7 @@ "$ref": "#/texts/223" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7149,6 +7607,7 @@ "$ref": "#/texts/232" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7166,6 +7625,7 @@ "$ref": "#/texts/238" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7183,6 +7643,7 @@ "$ref": "#/texts/246" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7200,6 +7661,7 @@ "$ref": "#/texts/249" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [ @@ -7217,6 +7679,7 @@ "$ref": "#/texts/341" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -7232,6 +7695,7 @@ "$ref": "#/texts/39" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -7830,6 +8294,7 @@ "$ref": "#/texts/341" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/word_sample.docx.json b/tests/data/groundtruth/docling_v2/word_sample.docx.json index 8c6e629..4b78a46 100644 --- a/tests/data/groundtruth/docling_v2/word_sample.docx.json +++ b/tests/data/groundtruth/docling_v2/word_sample.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "word_sample", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -23,6 +24,7 @@ "$ref": "#/texts/1" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -43,6 +45,7 @@ "$ref": "#/texts/8" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -62,6 +65,7 @@ "$ref": "#/texts/12" } ], + "content_layer": "body", "name": "list", "label": "list" }, @@ -81,6 +85,7 @@ "$ref": "#/texts/22" } ], + "content_layer": "body", "name": "list", "label": "list" } @@ -92,6 +97,7 @@ "$ref": "#/body" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Summer activities", @@ -116,6 +122,7 @@ "$ref": "#/texts/4" } ], + "content_layer": "body", "label": "title", "prov": [], "orig": "Swimming in the lake", @@ -127,6 +134,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Duck", @@ -138,6 +146,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Figure 1: This is a cute duckling", @@ -168,6 +177,7 @@ "$ref": "#/texts/14" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Let\u2019s swim!", @@ -180,6 +190,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "To get started with swimming, first lay down in a water and try not to drown:", @@ -191,6 +202,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "You can relax and look around", @@ -204,6 +216,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Paddle about", @@ -217,6 +230,7 @@ "$ref": "#/groups/0" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Enjoy summer warmth", @@ -230,6 +244,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Also, don\u2019t forget:", @@ -241,6 +256,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Wear sunglasses", @@ -254,6 +270,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Don\u2019t forget to drink water", @@ -267,6 +284,7 @@ "$ref": "#/groups/1" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Use sun cream", @@ -280,6 +298,7 @@ "$ref": "#/texts/4" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Hmm, what else\u2026", @@ -313,6 +332,7 @@ "$ref": "#/groups/2" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Let\u2019s eat", @@ -325,6 +345,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "After we had a good day of swimming in the lake, it\u2019s important to eat something nice", @@ -336,6 +357,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "I like to eat leaves", @@ -347,6 +369,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "Here are some interesting things a respectful duck could eat:", @@ -358,6 +381,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -369,6 +393,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "And let\u2019s add another list in the end:", @@ -380,6 +405,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Leaves", @@ -393,6 +419,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Berries", @@ -406,6 +433,7 @@ "$ref": "#/groups/2" }, "children": [], + "content_layer": "body", "label": "list_item", "prov": [], "orig": "Grain", @@ -421,6 +449,7 @@ "$ref": "#/texts/1" }, "children": [], + "content_layer": "body", "label": "picture", "prov": [], "captions": [], @@ -445,6 +474,7 @@ "$ref": "#/texts/14" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.json b/tests/data/groundtruth/docling_v2/word_tables.docx.json index 957a83c..2b2d04c 100644 --- a/tests/data/groundtruth/docling_v2/word_tables.docx.json +++ b/tests/data/groundtruth/docling_v2/word_tables.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.0.0", + "version": "1.1.0", "name": "word_tables", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", @@ -10,6 +10,7 @@ "furniture": { "self_ref": "#/furniture", "children": [], + "content_layer": "furniture", "name": "_root_", "label": "unspecified" }, @@ -20,6 +21,7 @@ "$ref": "#/groups/0" } ], + "content_layer": "body", "name": "_root_", "label": "unspecified" }, @@ -34,6 +36,7 @@ "$ref": "#/texts/0" } ], + "content_layer": "body", "name": "header-0", "label": "section" } @@ -94,6 +97,7 @@ "$ref": "#/texts/11" } ], + "content_layer": "body", "label": "section_header", "prov": [], "orig": "Test with tables", @@ -106,6 +110,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A uniform table", @@ -117,6 +122,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -128,6 +134,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A non-uniform table with horizontal spans", @@ -139,6 +146,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -150,6 +158,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A non-uniform table with horizontal spans in inner columns", @@ -161,6 +170,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -172,6 +182,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A non-uniform table with vertical spans", @@ -183,6 +194,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -194,6 +206,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "A non-uniform table with all kinds of spans and empty cells", @@ -205,6 +218,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -216,6 +230,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "paragraph", "prov": [], "orig": "", @@ -230,6 +245,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -472,6 +488,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -690,6 +707,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -980,6 +998,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], @@ -1346,6 +1365,7 @@ "$ref": "#/texts/0" }, "children": [], + "content_layer": "body", "label": "table", "prov": [], "captions": [], diff --git a/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json b/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json index dcaf59c..426ee19 100644 --- a/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json +++ b/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling 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"coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}] \ No newline at end of file diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json index 8bf4654..95472e2 100644 --- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json +++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "ocr_test", "origin": {"mimetype": "application/pdf", "binary_hash": 14853448746796404529, "filename": "ocr_test.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": 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{"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 69.6796646118164, "t": 764.9216918945312, "r": 504.87200927734375, "b": 689.012451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "pictures": [], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 595.201171875, "height": 841.9216918945312}, "image": null, "page_no": 1}}} \ No newline at end of file diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json b/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json index dcaf59c..426ee19 100644 --- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json +++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 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"coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}] \ No newline at end of file diff --git a/tests/test_backend_patent_uspto.py b/tests/test_backend_patent_uspto.py index 21bc88c..0e95a4d 100644 --- a/tests/test_backend_patent_uspto.py +++ b/tests/test_backend_patent_uspto.py @@ -14,7 +14,7 @@ from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend, XmlTab from docling.datamodel.base_models import InputFormat from docling.datamodel.document import InputDocument -GENERATE: bool = True +GENERATE: bool = False DATA_PATH: Path = Path("./tests/data/uspto/") GT_PATH: Path = Path("./tests/data/groundtruth/docling_v2/")