diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
index 5db6b78e..4ae4da42 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
@@ -1 +1 @@
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Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [{"prov": [{"bbox": [139.6674041748047, 322.5054626464844, 475.00927734375, 454.4546203613281], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 6, "data": [[{"bbox": [144.592, 341.627, 183.828, 362.952], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.195, 341.627, 231.425, 362.952], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.798, 347.106, 278.333, 355.48], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 4, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 5, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [396.271, 341.627, 417.123, 350.001], "spans": [[0, 6]], "text": "mAP", "type": "col_header", "col": 6, "col-header": true, "col-span": [6, 7], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [430.771, 341.627, 467.141, 350.001], "spans": [[0, 7]], "text": "Inference", "type": "col_header", "col": 7, "col-header": true, "col-span": [7, 8], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [144.592, 341.627, 183.828, 362.952], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.195, 341.627, 231.425, 362.952], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.798, 347.106, 278.333, 355.48], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [286.686, 354.578, 312.325, 362.952], "spans": [[1, 3]], "text": "simple", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [320.702, 354.578, 353.718, 362.952], "spans": [[1, 4]], "text": "complex", "type": "col_header", "col": 4, "col-header": true, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [369.306, 354.578, 379.031, 362.952], "spans": [[1, 5]], "text": "all", "type": "col_header", "col": 5, "col-header": true, "col-span": [5, 6], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [394.927, 352.586, 418.473, 360.96], "spans": [[1, 6]], "text": "(0.75)", "type": "col_header", "col": 6, "col-header": true, "col-span": [6, 7], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [427.148, 352.586, 470.773, 360.96], "spans": [[1, 7]], "text": "time (secs)", "type": "col_header", "col": 7, "col-header": true, "col-span": [7, 8], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [161.906, 373.408, 166.515, 381.782], "spans": [[2, 0]], "text": "6", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [209.509, 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[2, 3]}, {"bbox": [396.206, 366.637, 417.198, 389.254], "spans": [[2, 6]], "text": "0.88 0.857", "type": "body", "col": 6, "col-header": false, "col-span": [6, 7], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [439.527, 366.637, 458.388, 389.254], "spans": [[2, 7]], "text": "2.73 5.39", "type": "body", "col": 7, "col-header": false, "col-span": [7, 8], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [161.906, 399.709, 166.515, 408.083], "spans": [[3, 0]], "text": "4", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [209.509, 399.709, 214.118, 408.083], "spans": [[3, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [245.176, 394.23, 272.94, 415.555], "spans": [[3, 2]], "text": "OTSL HTML", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": 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diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
index 309b3c4d..f1c85318 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, 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517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}]
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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, 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"confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
index 68a0b635..74027b53 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
@@ -1 +1 @@
-{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.478, "t": 698.337, "r": 447.545, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.984, "t": 698.337, "r": 480.593, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 673.126, "r": 480.597, "b": 639.919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 622.844, "r": 318.451, "b": 613.617, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 606.474, "r": 480.596, "b": 536.933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 518.264, "r": 480.599, "b": 464.763, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 283.878, "r": 264.408, "b": 274.65100000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 267.509, "r": 480.722, "b": 174.52600000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 557]}], "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 di ffi cult 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 di ffi cult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 171.86699999999996, "r": 480.599, "b": 126.70600000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.66741943359375, "t": 454.45458984375, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 341.627, "r": 417.123, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 352.586, "r": 418.473, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 341.627, "r": 467.141, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.148, "t": 352.586, "r": 470.773, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 354.578, "r": 312.325, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 354.578, "r": 353.718, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 354.578, "r": 379.031, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, 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396.206, "t": 366.637, "r": 417.198, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.88 0.857", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 366.637, "r": 458.388, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "2.73 5.39", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 399.709, "r": 166.515, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 399.709, "r": 214.118, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 394.23, "r": 272.94, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 394.23, "r": 310.009, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.938 0.952", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 394.23, "r": 347.709, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.904 0.909", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 394.23, "r": 386.252, "b": 416.027, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.927 0.938", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 392.939, "r": 418.782, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.853 0.843", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 392.939, "r": 458.388, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.97 3.77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 426.01, "r": 166.515, "b": 434.384, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 426.01, "r": 214.118, "b": 434.384, "coord_origin": "TOPLEFT"}, "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": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 420.531, "r": 272.94, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 420.531, "r": 310.009, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.923 0.945", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 420.531, "r": 347.709, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 420.531, "r": 386.252, "b": 442.328, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.915 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 419.24, "r": 418.782, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.859 0.834", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 419.24, "r": 458.388, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 452.312, "r": 166.515, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 452.312, "r": 214.118, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 446.832, "r": 272.94, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 446.832, "r": 310.009, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 446.832, "r": 347.709, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 445.541, "r": 386.252, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 445.541, "r": 418.782, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 445.541, "r": 458.388, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 341.627, "r": 417.123, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 341.627, "r": 467.141, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 354.578, "r": 312.325, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 354.578, "r": 353.718, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 354.578, "r": 379.031, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 352.586, "r": 418.473, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.148, "t": 352.586, "r": 470.773, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 373.408, "r": 166.515, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 373.408, "r": 214.118, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 367.928, "r": 272.94, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 367.928, "r": 310.009, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.965 0.969", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 367.928, "r": 347.709, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.934 0.927", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 363.676, "t": 367.928, "r": 384.668, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.955 0.955", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 396.206, "t": 366.637, "r": 417.198, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.88 0.857", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 366.637, "r": 458.388, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "2.73 5.39", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 399.709, "r": 166.515, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 399.709, "r": 214.118, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 394.23, "r": 272.94, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 394.23, "r": 310.009, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, 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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.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 622.844, "r": 318.451, "b": 613.617, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 606.474, "r": 480.596, "b": 536.933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 518.264, "r": 480.599, "b": 464.763, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 283.878, "r": 264.408, "b": 274.65100000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 267.509, "r": 480.722, "b": 174.52600000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 557]}], "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 di ffi cult 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 di ffi cult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 171.86699999999996, "r": 480.599, "b": 126.70600000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.6674041748047, "t": 454.4546203613281, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 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"coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 394.23, "r": 272.94, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 394.23, "r": 310.009, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.938 0.952", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 394.23, "r": 347.709, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.904 0.909", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 394.23, "r": 386.252, "b": 416.027, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.927 0.938", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 392.939, "r": 418.782, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.853 0.843", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 392.939, "r": 458.388, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.97 3.77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 426.01, "r": 166.515, "b": 434.384, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 426.01, "r": 214.118, "b": 434.384, "coord_origin": "TOPLEFT"}, "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": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 420.531, "r": 272.94, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 420.531, "r": 310.009, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.923 0.945", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 420.531, "r": 347.709, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 420.531, "r": 386.252, "b": 442.328, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.915 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 419.24, "r": 418.782, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.859 0.834", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 419.24, "r": 458.388, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 452.312, "r": 166.515, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 452.312, "r": 214.118, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 446.832, "r": 272.94, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 446.832, "r": 310.009, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 446.832, "r": 347.709, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 445.541, "r": 386.252, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 445.541, "r": 418.782, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 445.541, "r": 458.388, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 341.627, "r": 417.123, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 341.627, "r": 467.141, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 354.578, "r": 312.325, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 354.578, "r": 353.718, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 354.578, "r": 379.031, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 352.586, "r": 418.473, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.148, "t": 352.586, "r": 470.773, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 373.408, "r": 166.515, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 373.408, "r": 214.118, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 367.928, "r": 272.94, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 367.928, "r": 310.009, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.965 0.969", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 367.928, "r": 347.709, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.934 0.927", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 363.676, "t": 367.928, "r": 384.668, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.955 0.955", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 396.206, "t": 366.637, "r": 417.198, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.88 0.857", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 366.637, "r": 458.388, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "2.73 5.39", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 399.709, "r": 166.515, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 399.709, "r": 214.118, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 394.23, "r": 272.94, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 394.23, "r": 310.009, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
index 309b3c4d..f1c85318 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, 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"r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, 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524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, 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(about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. 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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."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}]
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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, 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"confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}]
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diff --git a/tests/data_scanned/groundtruth/docling_v1/ocr_test.json b/tests/data_scanned/groundtruth/docling_v1/ocr_test.json
index f42acdb6..f3ac4c14 100644
--- a/tests/data_scanned/groundtruth/docling_v1/ocr_test.json
+++ b/tests/data_scanned/groundtruth/docling_v1/ocr_test.json
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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 4aabb287..66ca99fb 100644
--- a/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json
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diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.doctags.txt b/tests/data_scanned/groundtruth/docling_v2/ocr_test.doctags.txt
index 99ea5520..c210e4dd 100644
--- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.doctags.txt
+++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.doctags.txt
@@ -1,2 +1,2 @@
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+<doctag><text><loc_59><loc_46><loc_424><loc_91>Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package</text>
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diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json
index ac79ac25..9b8c53df 100644
--- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json
+++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json
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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 4aabb287..66ca99fb 100644
--- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json
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