docling/tests/data/groundtruth/docling_v2/2206.01062.json

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Nassar, and Peter", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "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/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 705.202, "r": 59.405, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "level": 1}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 680.586, "r": 303.017, "b": 672.785, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Despite the substantial improvements achieved with machine-learning", "text": "Despite the substantial improvements achieved with machine-learning"}, {"self_ref": "#/texts/20", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 63.761, "t": 560.038, "r": 295.564, "b": 552.237, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Akeyproblem in the process of document conversion is to under-", "text": "Akeyproblem in the process of document conversion is to under-"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 63.761, "t": 286.066, "r": 295.564, "b": 278.265, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text": "In this paper, we present the DocLayNet dataset. It provides page-"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 206.98800000000006, "r": 74.221, "b": 199.187, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(1)", "text": "(1)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 174.111, "r": 74.221, "b": 166.30999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(2)", "text": "(2)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 152.19399999999996, "r": 74.221, "b": 144.39300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(3)", "text": "(3)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 119.31700000000001, "r": 74.221, "b": 111.51599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(4)", "text": "(4)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672, "t": 91.10299999999995, "r": 56.22, "b": 86.33600000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.095, "t": 704.206, "r": 558.432, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "This enables experimentation with annotation uncertainty", "text": "This enables experimentation with annotation uncertainty"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.865, "t": 682.288, "r": 338.378, "b": 674.487, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(5)", "text": "(5)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/29", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 623.594, "r": 559.19, "b": 615.793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 568.799, "r": 558.198, "b": 560.998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "In Section 5, we will present baseline accuracy numbers for a", "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 469.853, "r": 323.562, "b": 460.786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "level": 1}, {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.525, "t": 445.237, "r": 559.713, "b": 437.436, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "While early approaches in document-layout analysis used rule-", "text": "While early approaches in document-layout analysis used rule-"}, {"self_ref": "#/texts/33", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 324.689, "r": 558.198, "b": 316.888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Lately, new types of ML models for document-layout analysis", "text": "Lately, new types of ML models for document-layout analysis"}, {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 225.74199999999996, "r": 323.562, "b": 216.67499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "level": 1}, {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 201.12599999999998, "r": 558.202, "b": 193.32500000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 113.45500000000004, "r": 558.198, "b": 105.654, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "In addition to open intellectual property constraints for the", "text": "In addition to open intellectual property constraints for the"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "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/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "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/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 91.673, "r": 56.346, "b": 86.90599999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 555.114, "r": 294.044, "b": 547.661, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Figure 2: Distribution of DocLayNet pages across document", "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 509.766, "r": 294.046, "b": 501.965, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "to a minimum, since they introduce difficulties in annotation (see", "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 422.095, "r": 295.559, "b": 414.294, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 279.629, "r": 295.559, "b": 271.828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "We did not control the document selection with regard to lan-", "text": "We did not control the document selection with regard to lan-"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 180.99900000000002, "r": 295.564, "b": 173.19799999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "To ensure that future benchmarks in the document-layout analy-", "text": "To ensure that future benchmarks in the document-layout analy-"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 704.206, "r": 558.201, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "Table 1 shows the overall frequency and distribution of the labels", "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 627.494, "r": 558.438, "b": 619.693, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "In order to accommodate the different types of models currently", "text": "In order to accommodate the different types of models currently"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 517.905, "r": 559.185, "b": 510.104, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Despite being cost-intense and far less scalable than automation,", "text": "Despite being cost-intense and far less scalable than automation,"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.955, "t": 184.212, "r": 323.562, "b": 175.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "level": 1}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.686, "t": 159.596, "r": 558.201, "b": 151.79499999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "The annotation campaign was carried out in four phases. In phase", "text": "The annotation campaign was carried out in four phases. In phase"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "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/51", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 237.21900000000005, "r": 295.649, "b": 229.76599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.502, "t": 706.274, "r": 558.49, "b": 698.821, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.467, "t": 157.27800000000002, "r": 294.047, "b": 149.47699999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "we distributed the annotation workload and performed continuous", "text": "we distributed the annotation workload and performed continuous"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 63.761, "t": 113.21799999999996, "r": 226.725, "b": 105.76499999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Phase 1: Data selection and preparation.", "text": "Phase 1: Data selection and preparation."}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 479.49, "r": 481.034, "b": 471.689, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "include publication repositories such as arXiv", "text": "include publication repositories such as arXiv"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 327.918, "t": 413.737, "r": 558.206, "b": 405.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Preparation work included uploading and parsing the sourced", "text": "Preparation work included uploading and parsing the sourced"}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 327.918, "t": 282.006, "r": 482.418, "b": 274.553, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Phase 2: Label selection and guideline.", "text": "Phase 2: Label selection and guideline."}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 90.976, "r": 320.503, "b": 86.20900000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "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/60", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "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/61", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 704.206, "r": 294.045, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "the textual content of an element, which goes beyond visual layout", "text": "the textual content of an element, which goes beyond visual layout"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 682.288, "r": 294.043, "b": 674.487, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "At first sight, the task of visual document-layout interpretation", "text": "At first sight, the task of visual document-layout interpretation"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 539.823, "r": 294.219, "b": 532.022, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Obviously, this inconsistency in annotations is not desirable for", "text": "Obviously, this inconsistency in annotations is not desirable for"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 443.057, "r": 74.221, "b": 435.256, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(1)", "text": "(1)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 399.221, "r": 74.221, "b": 391.42, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(2)", "text": "(2)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 355.385, "r": 74.221, "b": 347.584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(3)", "text": "(3)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 333.468, "r": 74.221, "b": 325.667, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(4)", "text": "(4)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 311.55, "r": 74.221, "b": 303.749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(5)", "text": "(5)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 300.591, "r": 74.221, "b": 292.79, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(6)", "text": "(6)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.529, "t": 258.619, "r": 294.043, "b": 250.81799999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "The complete annotation guideline is over 100 pages long and a", "text": "The complete annotation guideline is over 100 pages long and a"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 214.55999999999995, "r": 136.774, "b": 207.10699999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Phase 3: Training.", "text": "Phase 3: Training."}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.955, "t": 317.735, "r": 558.389, "b": 310.282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Figure 4: Examples of plausible annotation alternatives for", "text": "Figure 4: Examples of plausible annotation alternatives for"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.623, "t": 266.072, "r": 558.204, "b": 258.27099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 327.918, "t": 243.93000000000006, "r": 456.801, "b": 236.47699999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Phase 4: Production annotation.", "text": "Phase 4: Production annotation."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "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/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.502, "t": 704.356, "r": 294.044, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 420.642, "r": 294.047, "b": 412.841, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text": "to avoid this at any cost in order to have clear, unbiased baseline"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 202.93200000000002, "r": 59.405, "b": 193.865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "level": 1}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.529, "t": 178.31600000000003, "r": 294.049, "b": 170.515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text": "The primary goal of DocLayNet is to obtain high-quality ML models"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 512.213, "r": 558.479, "b": 504.76, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 407.558, "r": 558.204, "b": 399.757, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "paper and leave the detailed evaluation of more recent methods", "text": "paper and leave the detailed evaluation of more recent methods"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 327.918, "t": 385.64, "r": 558.198, "b": 377.839, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "In this section, we will present several aspects related to the", "text": "In this section, we will present several aspects related to the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 294.24, "r": 466.853, "b": 285.173, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 279.5450000000001, "r": 558.431, "b": 271.744, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "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/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "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/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.502, "t": 704.356, "r": 294.044, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 471.492, "r": 131.056, "b": 462.425, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 456.798, "r": 294.042, "b": 448.997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "One of the fundamental questions related to any dataset is if it is", "text": "One of the fundamental questions related to any dataset is if it is"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 248.55200000000002, "r": 164.329, "b": 239.485, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.529, "t": 233.85799999999995, "r": 294.043, "b": 226.05700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "The choice and number of labels can have a significant effect on", "text": "The choice and number of labels can have a significant effect on"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.659, "t": 704.356, "r": 558.201, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text": "Table 4: Performance of a Mask R-CNN R50 network with"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 460.166, "r": 558.202, "b": 452.365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 361.667, "r": 549.861, "b": 352.6, "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/95", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 346.973, "r": 558.369, "b": 339.172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Many documents in DocLayNet have a unique styling. In order", "text": "Many documents in DocLayNet have a unique styling. In order"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 182.72000000000003, "r": 418.548, "b": 173.65300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/97", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.686, "t": 168.02600000000007, "r": 558.206, "b": 160.22500000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text": "Throughout this paper, we claim that DocLayNet's wider variety of"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "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/99", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.502, "t": 704.356, "r": 294.322, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 400.712, "r": 106.239, "b": 392.804, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Section-header", "text": "Section-header"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 63.761, "t": 345.855, "r": 294.276, "b": 338.054, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "For comparison of DocBank with DocLayNet, we trained only", "text": "For comparison of DocBank with DocLayNet, we trained only"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 186.00099999999998, "r": 156.005, "b": 176.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/103", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.529, "t": 171.30600000000004, "r": 295.557, "b": 163.505, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "To conclude this section, we illustrate the quality of layout predic-", "text": "To conclude this section, we illustrate the quality of layout predic-"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 705.202, "r": 323.562, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 690.508, "r": 558.204, "b": 682.707, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text": "In this paper, we presented the DocLayNet dataset. It provides the"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 327.918, "t": 602.836, "r": 558.198, "b": 595.035, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "From the dataset, we have derived on the one hand reference", "text": "From the dataset, we have derived on the one hand reference"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 327.918, "t": 504.206, "r": 558.435, "b": 496.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "To date, there is still a significant gap between human and ML", "text": "To date, there is still a significant gap between human and ML"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 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"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "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/124", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 62.324, "t": 348.148, "r": 70.9, "b": 344.178, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Text", "text": "Text"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 53.798, "t": 326.747, "r": 558.203, "b": 319.294, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 120]}], "orig": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on", "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 69.234, "t": 264.59899999999993, "r": 295.224, "b": 258.53099999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-", "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul-"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 9, "bbox": {"l": 317.955, "t": 264.59899999999993, "r": 329.406, "b": 258.53099999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "[20]", "text": "[20]", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 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