mirror of
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7d3302cb48
* Keep page.parsed_page.textline_cells and page.cells in sync, including OCR Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Make page.parsed_page the only source of truth for text cells Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Small fix Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Correctly compute PDF boxes from pymupdf Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Use different OCR engine order Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add type hints and fix mypy Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * One more test fix Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Remove with pypdfium2_lock from caller sites Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fix typing Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
191453 lines
6.0 MiB
Vendored
191453 lines
6.0 MiB
Vendored
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"text": "Popular table structure data-sets will be published in OTSL format to",
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"text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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"text": "approaches. In practice, this is not entirely true, because a predicted sequence",
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"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."
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"text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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"text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
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"text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines",
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"text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."
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"text": ": The upper neighbour of a \"U\" cell must be either",
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"orig": ": The upper neighbour of a \"U\" cell must be either",
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"text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."
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"confidence": 1.0,
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"text": ":",
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"text": "3. Cross cell rule :"
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"text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
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"orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
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"text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."
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"orig": "The OTSL representation follows these syntax rules:",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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}
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"children": []
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"text": "The OTSL representation follows these syntax rules:"
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"label": "list_item",
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"id": 3,
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"text": "1.",
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"orig": "1.",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"r_y3": 347.17081,
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},
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"text": "Left-looking cell rule",
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"orig": "Left-looking cell rule",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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"r_y3": 347.18079,
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"coord_origin": "TOPLEFT"
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},
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"text": ": The left neighbour of an \"L\" cell must be either",
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"orig": ": The left neighbour of an \"L\" cell must be either",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 81,
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"rgba": {
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"g": 0,
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"b": 0,
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"r_x1": 283.59387,
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"r_y1": 367.93375,
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"r_y2": 359.13678,
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"coord_origin": "TOPLEFT"
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"text": "another \"L\" cell or a \"C\" cell.",
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"orig": "another \"L\" cell or a \"C\" cell.",
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"text_direction": "left_to_right",
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"from_ocr": false
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}
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],
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"children": []
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},
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"text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."
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},
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{
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"label": "list_item",
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"id": 4,
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"confidence": 0.9585386514663696,
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"text": "2.",
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"orig": "2.",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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"r_y3": 371.08481,
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"coord_origin": "TOPLEFT"
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},
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"text": "Up-looking cell rule",
|
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"orig": "Up-looking cell rule",
|
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 84,
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"rgba": {
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"r": 0,
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"b": 0,
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"r_y1": 379.89175,
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"r_y2": 371.09479,
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"r_x3": 252.112,
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"r_y3": 371.09479,
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"coord_origin": "TOPLEFT"
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},
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"text": ": The upper neighbour of a \"U\" cell must be either",
|
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"orig": ": The upper neighbour of a \"U\" cell must be either",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 85,
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"rgba": {
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"r": 0,
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"r_x1": 284.8392,
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"r_y2": 383.04977,
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},
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"text": "another \"U\" cell or a \"C\" cell.",
|
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"orig": "another \"U\" cell or a \"C\" cell.",
|
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"text_direction": "left_to_right",
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"confidence": 1.0,
|
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"from_ocr": false
|
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}
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],
|
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"children": []
|
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},
|
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"text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."
|
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},
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{
|
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"label": "section_header",
|
|
"id": 14,
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"page_no": 6,
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"cluster": {
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"id": 14,
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"label": "section_header",
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"confidence": 0.6506174206733704,
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"cells": [
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"rgba": {
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"r_x1": 146.71991,
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"r_y1": 403.80475,
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"r_x2": 146.71991,
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"r_y2": 395.0077800000001,
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"r_x3": 138.97299,
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"r_y3": 395.0077800000001,
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"coord_origin": "TOPLEFT"
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},
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"text": "3.",
|
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"orig": "3.",
|
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 87,
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"rgba": {
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"r_y0": 403.80475,
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"r_x1": 223.3042,
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"r_y1": 403.80475,
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"r_x2": 223.3042,
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"r_y3": 394.99780000000004,
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},
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"text": "Cross cell rule",
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"orig": "Cross cell rule",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 88,
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"rgba": {
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"r": 0,
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"r_x1": 226.07360999999997,
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"r_y1": 403.80475,
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"r_x2": 226.07360999999997,
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"r_y2": 395.0077800000001,
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"r_x3": 223.30699,
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"r_y3": 395.0077800000001,
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"coord_origin": "TOPLEFT"
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},
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"text": ":",
|
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"orig": ":",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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}
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],
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},
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"text": "3. Cross cell rule :"
|
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},
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{
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"id": 13,
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"page_no": 6,
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"cluster": {
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"id": 13,
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"label": "list_item",
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"r_y1": 415.76373,
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"r_y2": 406.96677,
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"r_y3": 406.96677,
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"coord_origin": "TOPLEFT"
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},
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"text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
|
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"orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 90,
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"r": 0,
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"r_x0": 151.70099,
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"r_y0": 427.71871999999996,
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"r_x1": 480.59219,
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"r_y1": 427.71871999999996,
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"r_x2": 480.59219,
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"r_y2": 418.9217499999999,
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"r_y3": 418.9217499999999,
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"coord_origin": "TOPLEFT"
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},
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"text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
|
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"orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"r_x0": 151.70099,
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"r_y0": 439.67371,
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"r_y2": 430.87674,
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"r_y3": 430.87674,
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"coord_origin": "TOPLEFT"
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},
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"text": "or an \"L\" cell.",
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"orig": "or an \"L\" cell.",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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}
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],
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"children": []
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},
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"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."
|
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},
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{
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"label": "list_item",
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"id": 8,
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"confidence": 0.9259927272796631,
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"cells": [
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"r_x1": 146.71991,
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"r_y1": 451.63269,
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"r_x2": 146.71991,
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"r_y2": 442.83572,
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"r_x3": 138.97299,
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"r_y3": 442.83572,
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"coord_origin": "TOPLEFT"
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},
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"text": "4.",
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"orig": "4.",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 93,
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"rgba": {
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"r": 0,
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"rect": {
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"r_x0": 151.70099,
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"r_y0": 451.63269,
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"r_x1": 221.32263,
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"r_y1": 451.63269,
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"r_x2": 221.32263,
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"r_y2": 442.82574,
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"r_x3": 151.70099,
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"r_y3": 442.82574,
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"coord_origin": "TOPLEFT"
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},
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"text": "First row rule",
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"orig": "First row rule",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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},
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"index": 94,
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"rgba": {
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"rect": {
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"r_x0": 221.32700000000003,
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"r_y0": 451.63269,
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"r_x1": 474.59018,
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"r_y1": 451.63269,
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"r_x2": 474.59018,
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"r_y2": 442.83572,
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"r_x3": 221.32700000000003,
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"r_y3": 442.83572,
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"coord_origin": "TOPLEFT"
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},
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"text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.",
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"orig": ": Only \"L\" cells and \"C\" cells are allowed in the first row.",
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"text_direction": "left_to_right",
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"text": "every new-line token starts a new row. As a consequence, all rows and all columns",
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"text": "predicted token can be validated straight during sequence generation by looking",
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"text": "at the previously predicted sequence. As such, OTSL can guarantee that every",
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"text": "verted into OTSL format for this purpose, and will be made publicly available.",
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"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."
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"text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
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"orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
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"text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
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"orig": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
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"text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
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"text": "HTML",
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"text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
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"orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
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},
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"text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
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"orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"from_ocr": false
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"index": 6,
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"r": 0,
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"g": 0,
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"rect": {
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},
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"text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
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"orig": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
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"text_direction": "left_to_right",
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"confidence": 1.0,
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"text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
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"orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
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"text_direction": "left_to_right",
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"from_ocr": false
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}
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],
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"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."
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"text": "Incorrect end of HTML sequence",
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"cells": [
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},
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"text": "Incorrect end of HTML sequence",
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"orig": "Incorrect end of HTML sequence",
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"text_direction": "left_to_right",
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}
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"label": "text",
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"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."
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"text": "OTSL representation. Additional performance benefits can be obtained with",
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"text": "of encoder and decoder layers, while preserving comparatively good prediction",
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"text": "quality. This can further improve inference performance, yielding 5-6 times faster",
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"text": "on HTML (see Table 1).",
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"text": "cabulary size. This allows autoregressive models to perform better in the TED",
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"text": "metric, but especially with regards to prediction accuracy of the table-cell bound-",
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"text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-",
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"text": "tically reduces the drift for table cell bounding boxes at high row count and in",
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"text": "sparse tables. This leads to more accurate predictions and a significant reduction",
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"text": "in post-processing complexity, which is an undesired necessity in HTML-based",
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"orig": "in post-processing complexity, which is an undesired necessity in HTML-based",
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"text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,",
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"orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,",
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"text": "simple and always backwards looking. Each new token can be validated only by",
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"orig": "simple and always backwards looking. Each new token can be validated only by",
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"text": "analyzing the sequence of previous tokens, without requiring the entire sequence",
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"orig": "analyzing the sequence of previous tokens, without requiring the entire sequence",
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"text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-",
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