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feat: Use new TableFormer model weights and default to accurate model version (#1100)

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* feat: New tableformer model weights [WIP]

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>

* Updated TF version

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Updated tests, after merging with Main, Switched to Accurate TF model by default

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

---------

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>
Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>
Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
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Christoph Auer
2025-03-11 10:53:49 +01:00
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parent 5e30381c0d
commit eb97357b05
43 changed files with 213 additions and 229 deletions
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tests/data/groundtruth/docling_v1/2203.01017v2.doctags.txt
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</figure>
<table>
<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
<row_0><col_0><col_header>3</col_0><col_1><col_header>1</col_1></row_0>
<row_0><col_0><col_header>1</col_0></row_0>
</table>
<paragraph><location><page_1><loc_52><loc_58><loc_79><loc_60></location>- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer</paragraph>
<paragraph><location><page_1><loc_52><loc_46><loc_80><loc_47></location>- c. Structure predicted by TableFormer:</paragraph>
@@ -25,11 +25,11 @@
</figure>
<table>
<location><page_1><loc_52><loc_37><loc_88><loc_45></location>
<row_0><col_0><col_header>0</col_0><col_1><col_header>1</col_1><col_2><col_header>1</col_2><col_3><col_header>2 1</col_3><col_4><col_header>2 1</col_4><col_5><body></col_5></row_0>
<row_1><col_0><body>3</col_0><col_1><body>4</col_1><col_2><body>5 3</col_2><col_3><body>6</col_3><col_4><body>7</col_4><col_5><body></col_5></row_1>
<row_2><col_0><body>8</col_0><col_1><body>9</col_1><col_2><body>10</col_2><col_3><body>11</col_3><col_4><body>12</col_4><col_5><body>2</col_5></row_2>
<row_3><col_0><body></col_0><col_1><body>13</col_1><col_2><body>14</col_2><col_3><body>15</col_3><col_4><body>16</col_4><col_5><body>2</col_5></row_3>
<row_4><col_0><body></col_0><col_1><body>17</col_1><col_2><body>18</col_2><col_3><body>19</col_3><col_4><body>20</col_4><col_5><body>2</col_5></row_4>
<row_0><col_0><body>0</col_0><col_1><body>1 2 1</col_1><col_2><body>1 2 1</col_2><col_3><body>1 2 1</col_3><col_4><body>1 2 1</col_4></row_0>
<row_1><col_0><body>3</col_0><col_1><body>4 3</col_1><col_2><body>5</col_2><col_3><body>6</col_3><col_4><body>7</col_4></row_1>
<row_2><col_0><body>8 2</col_0><col_1><body>9</col_1><col_2><body>10</col_2><col_3><body>11</col_3><col_4><body>12</col_4></row_2>
<row_3><col_0><body>13</col_0><col_1><body></col_1><col_2><body>14</col_2><col_3><body>15</col_3><col_4><body>16</col_4></row_3>
<row_4><col_0><body>17</col_0><col_1><body>18</col_1><col_2><body></col_2><col_3><body>19</col_3><col_4><body>20</col_4></row_4>
</table>
<paragraph><location><page_1><loc_50><loc_16><loc_89><loc_26></location>Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.</paragraph>
<paragraph><location><page_1><loc_50><loc_10><loc_89><loc_16></location>The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be</paragraph>
@@ -138,9 +138,9 @@
<location><page_7><loc_50><loc_62><loc_87><loc_69></location>
<caption>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
<row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>mAP</col_2><col_3><col_header>mAP (PP)</col_3></row_0>
<row_1><col_0><body>EDD+BBox</col_0><col_1><body>PubTabNet</col_1><col_2><body>79.2</col_2><col_3><body>82.7</col_3></row_1>
<row_2><col_0><body>TableFormer</col_0><col_1><body>PubTabNet</col_1><col_2><body>82.1</col_2><col_3><body>86.8</col_3></row_2>
<row_3><col_0><body>TableFormer</col_0><col_1><body>SynthTabNet</col_1><col_2><body>87.7</col_2><col_3><body>-</col_3></row_3>
<row_1><col_0><row_header>EDD+BBox</col_0><col_1><body>PubTabNet</col_1><col_2><body>79.2</col_2><col_3><body>82.7</col_3></row_1>
<row_2><col_0><row_header>TableFormer</col_0><col_1><body>PubTabNet</col_1><col_2><body>82.1</col_2><col_3><body>86.8</col_3></row_2>
<row_3><col_0><row_header>TableFormer</col_0><col_1><body>SynthTabNet</col_1><col_2><body>87.7</col_2><col_3><body>-</col_3></row_3>
</table>
<caption><location><page_7><loc_50><loc_57><loc_89><loc_60></location>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
<paragraph><location><page_7><loc_50><loc_34><loc_89><loc_54></location>Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.</paragraph>
@@ -179,7 +179,7 @@
<row_6><col_0><row_header>第 17 回人工知能学会全国大会 (2003)</col_0><col_1><body>208</col_1><col_2><body>5</col_2><col_3><body>203</col_3><col_4><body>152</col_4><col_5><body>244</col_5></row_6>
<row_7><col_0><row_header>自然言語処理研究会第 146 〜 155 回</col_0><col_1><body>98</col_1><col_2><body>2</col_2><col_3><body>96</col_3><col_4><body>150</col_4><col_5><body>232</col_5></row_7>
<row_8><col_0><row_header>WWW から収集した論文</col_0><col_1><body>107</col_1><col_2><body>73</col_2><col_3><body>34</col_3><col_4><body>147</col_4><col_5><body>96</col_5></row_8>
<row_9><col_0><body></col_0><col_1><body>945</col_1><col_2><body>294</col_2><col_3><body>651</col_3><col_4><body>1122</col_4><col_5><body>955</col_5></row_9>
<row_9><col_0><row_header>計</col_0><col_1><body>945</col_1><col_2><body>294</col_2><col_3><body>651</col_3><col_4><body>1122</col_4><col_5><body>955</col_5></row_9>
</table>
<caption><location><page_8><loc_62><loc_62><loc_90><loc_63></location>Text is aligned to match original for ease of viewing</caption>
<table>
tests/data/groundtruth/docling_v1/2203.01017v2.json
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tests/data/groundtruth/docling_v1/2203.01017v2.md
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Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
<!-- image -->
| 0 | 1 | 1 | 2 1 | 2 1 | |
|-----|-----|-----|-------|-------|----|
| 3 | 4 | 5 3 | 6 | 7 | |
| 8 | 9 | 10 | 11 | 12 | 2 |
| | 13 | 14 | 15 | 16 | 2 |
| | 17 | 18 | 19 | 20 | 2 |
| 0 | 1 2 1 | 1 2 1 | 1 2 1 | 1 2 1 |
|-----|---------|---------|---------|---------|
| 3 | 4 3 | 5 | 6 | 7 |
| 8 2 | 9 | 10 | 11 | 12 |
| 13 | | 14 | 15 | 16 |
| 17 | 18 | | 19 | 20 |
Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.
@@ -241,7 +241,7 @@ Text is aligned to match original for ease of viewing
| 第 17 回人工知能学会全国大会 (2003) | 208 | 5 | 203 | 152 | 244 |
| 自然言語処理研究会第 146 〜 155 回 | 98 | 2 | 96 | 150 | 232 |
| WWW から収集した論文 | 107 | 73 | 34 | 147 | 96 |
| | 945 | 294 | 651 | 1122 | 955 |
| 計 | 945 | 294 | 651 | 1122 | 955 |
| | Shares (in millions) | Shares (in millions) | Weighted Average Grant Date Fair Value | Weighted Average Grant Date Fair Value |
|--------------------------|------------------------|------------------------|------------------------------------------|------------------------------------------|
tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
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tests/data/groundtruth/docling_v1/2206.01062.doctags.txt
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<table>
<location><page_4><loc_16><loc_63><loc_84><loc_83></location>
<caption>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>% of Total</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>% of Total</col_2><col_3><col_header>% of Total</col_3><col_4><col_header>% of Total</col_4><col_5><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_5><col_6><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_6><col_7><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_7><col_8><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_8><col_9><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_9><col_10><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_10><col_11><col_header>triple inter-annotator mAP @ 0.5-0.95 (%)</col_11></row_0>
<row_1><col_0><col_header>class label</col_0><col_1><col_header>Count</col_1><col_2><col_header>Train</col_2><col_3><col_header>Test</col_3><col_4><col_header>Val</col_4><col_5><col_header>All</col_5><col_6><col_header>Fin</col_6><col_7><col_header>Man</col_7><col_8><col_header>Sci</col_8><col_9><col_header>Law</col_9><col_10><col_header>Pat</col_10><col_11><col_header>Ten</col_11></row_1>
<row_2><col_0><row_header>Caption</col_0><col_1><body>22524</col_1><col_2><body>2.04</col_2><col_3><body>1.77</col_3><col_4><body>2.32</col_4><col_5><body>84-89</col_5><col_6><body>40-61</col_6><col_7><body>86-92</col_7><col_8><body>94-99</col_8><col_9><body>95-99</col_9><col_10><body>69-78</col_10><col_11><body>n/a</col_11></row_2>
<row_3><col_0><row_header>Footnote</col_0><col_1><body>6318</col_1><col_2><body>0.60</col_2><col_3><body>0.31</col_3><col_4><body>0.58</col_4><col_5><body>83-91</col_5><col_6><body>n/a</col_6><col_7><body>100</col_7><col_8><body>62-88</col_8><col_9><body>85-94</col_9><col_10><body>n/a</col_10><col_11><body>82-97</col_11></row_3>
@@ -102,7 +102,7 @@
<table>
<location><page_6><loc_10><loc_56><loc_47><loc_75></location>
<row_0><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>MRCNN</col_2><col_3><col_header>MRCNN</col_3><col_4><col_header>FRCNN</col_4><col_5><col_header>YOLO</col_5></row_0>
<row_1><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>R50</col_2><col_3><col_header>R101</col_3><col_4><col_header>R101</col_4><col_5><col_header>v5x6</col_5></row_1>
<row_1><col_0><body></col_0><col_1><body></col_1><col_2><col_header>R50</col_2><col_3><col_header>R101</col_3><col_4><col_header>R101</col_4><col_5><col_header>v5x6</col_5></row_1>
<row_2><col_0><row_header>Caption</col_0><col_1><body>84-89</col_1><col_2><body>68.4</col_2><col_3><body>71.5</col_3><col_4><body>70.1</col_4><col_5><body>77.7</col_5></row_2>
<row_3><col_0><row_header>Footnote</col_0><col_1><body>83-91</col_1><col_2><body>70.9</col_2><col_3><body>71.8</col_3><col_4><body>73.7</col_4><col_5><body>77.2</col_5></row_3>
<row_4><col_0><row_header>Formula</col_0><col_1><body>83-85</col_1><col_2><body>60.1</col_2><col_3><body>63.4</col_3><col_4><body>63.5</col_4><col_5><body>66.2</col_5></row_4>
@@ -130,7 +130,7 @@
<paragraph><location><page_7><loc_9><loc_84><loc_48><loc_89></location>Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.</paragraph>
<table>
<location><page_7><loc_13><loc_63><loc_44><loc_81></location>
<row_0><col_0><col_header>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>6</col_2><col_3><col_header>5</col_3><col_4><col_header>4</col_4></row_0>
<row_0><col_0><body>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>6</col_2><col_3><col_header>5</col_3><col_4><col_header>4</col_4></row_0>
<row_1><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_1>
<row_2><col_0><row_header>Footnote</col_0><col_1><body>71</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_2>
<row_3><col_0><row_header>Formula</col_0><col_1><body>60</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_3>
@@ -178,17 +178,17 @@
<row_1><col_0><col_header>Training on</col_0><col_1><col_header>labels</col_1><col_2><col_header>PLN</col_2><col_3><col_header>DB</col_3><col_4><col_header>DLN</col_4></row_1>
<row_2><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
<row_3><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>87</col_2><col_3><body>-</col_3><col_4><body>32</col_4></row_3>
<row_4><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
<row_5><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
<row_6><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
<row_4><col_0><body></col_0><col_1><row_header>Table</col_1><col_2><body>95</col_2><col_3><body>24</col_3><col_4><body>49</col_4></row_4>
<row_5><col_0><body></col_0><col_1><row_header>Text</col_1><col_2><body>96</col_2><col_3><body>-</col_3><col_4><body>42</col_4></row_5>
<row_6><col_0><body></col_0><col_1><row_header>total</col_1><col_2><body>93</col_2><col_3><body>34</col_3><col_4><body>30</col_4></row_6>
<row_7><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Figure</col_1><col_2><body>77</col_2><col_3><body>71</col_3><col_4><body>31</col_4></row_7>
<row_8><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>Table</col_1><col_2><body>19</col_2><col_3><body>65</col_3><col_4><body>22</col_4></row_8>
<row_9><col_0><row_header>DocBank (DB)</col_0><col_1><row_header>total</col_1><col_2><body>48</col_2><col_3><body>68</col_3><col_4><body>27</col_4></row_9>
<row_10><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>67</col_2><col_3><body>51</col_3><col_4><body>72</col_4></row_10>
<row_11><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Sec-header</col_1><col_2><body>53</col_2><col_3><body>-</col_3><col_4><body>68</col_4></row_11>
<row_12><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
<row_13><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
<row_14><col_0><row_header>DocLayNet (DLN)</col_0><col_1><row_header>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
<row_12><col_0><body></col_0><col_1><row_header>Table</col_1><col_2><body>87</col_2><col_3><body>43</col_3><col_4><body>82</col_4></row_12>
<row_13><col_0><body></col_0><col_1><row_header>Text</col_1><col_2><body>77</col_2><col_3><body>-</col_3><col_4><body>84</col_4></row_13>
<row_14><col_0><body></col_0><col_1><row_header>total</col_1><col_2><body>59</col_2><col_3><body>47</col_3><col_4><body>78</col_4></row_14>
</table>
<paragraph><location><page_8><loc_9><loc_44><loc_48><loc_51></location>Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .</paragraph>
<paragraph><location><page_8><loc_9><loc_26><loc_48><loc_44></location>For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.</paragraph>
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tests/data/groundtruth/docling_v1/2206.01062.md
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@@ -98,21 +98,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif
Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
| | | % of Total | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) |
|----------------|---------|--------------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten |
| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a |
| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 |
| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a |
| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 |
| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 |
| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 |
| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 |
| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 |
| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 |
| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 |
| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 |
| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 |
| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) |
|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten |
| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a |
| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 |
| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a |
| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 |
| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 |
| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 |
| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 |
| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 |
| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 |
| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 |
| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 |
| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 |
Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
<!-- image -->
@@ -161,7 +161,7 @@ Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on D
| | human | MRCNN | MRCNN | FRCNN | YOLO |
|----------------|---------|---------|---------|---------|--------|
| | human | R50 | R101 | R101 | v5x6 |
| | | R50 | R101 | R101 | v5x6 |
| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 |
| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 |
| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 |
@@ -252,17 +252,17 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros
| Training on | labels | PLN | DB | DLN |
| PubLayNet (PLN) | Figure | 96 | 43 | 23 |
| PubLayNet (PLN) | Sec-header | 87 | - | 32 |
| PubLayNet (PLN) | Table | 95 | 24 | 49 |
| PubLayNet (PLN) | Text | 96 | - | 42 |
| PubLayNet (PLN) | total | 93 | 34 | 30 |
| | Table | 95 | 24 | 49 |
| | Text | 96 | - | 42 |
| | total | 93 | 34 | 30 |
| DocBank (DB) | Figure | 77 | 71 | 31 |
| DocBank (DB) | Table | 19 | 65 | 22 |
| DocBank (DB) | total | 48 | 68 | 27 |
| DocLayNet (DLN) | Figure | 67 | 51 | 72 |
| DocLayNet (DLN) | Sec-header | 53 | - | 68 |
| DocLayNet (DLN) | Table | 87 | 43 | 82 |
| DocLayNet (DLN) | Text | 77 | - | 84 |
| DocLayNet (DLN) | total | 59 | 47 | 78 |
| | Table | 87 | 43 | 82 |
| | Text | 77 | - | 84 |
| | total | 59 | 47 | 78 |
Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .
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<table>
<location><page_1><loc_23><loc_41><loc_78><loc_57></location>
<caption>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.</caption>
<row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
<row_0><col_0><col_header># enc-layers</col_0><col_1><col_header># dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
<row_1><col_0><col_header># enc-layers</col_0><col_1><col_header># dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
<row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
<row_4><col_0><body></col_0><col_1><body></col_1><col_2><body>OTSL</col_2><col_3><body>0.952 0.923</col_3><col_4><body>0.909</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>HTML</col_2><col_3><body>0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904 0.909</col_4><col_5><body>0.927 0.938</col_5><col_6><body>0.853 0.843</col_6><col_7><body>1.97 3.77</col_7></row_3>
<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_4>
<row_5><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_5>
</table>
<caption><location><page_1><loc_22><loc_59><loc_79><loc_66></location>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.</caption>
<subtitle-level-1><location><page_1><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</subtitle-level-1>
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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.
| # | # | Language | TEDs | TEDs | TEDs | mAP | Inference |
|------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
| enc-layers | dec-layers | Language | simple | complex | all | (0.75) | time (secs) |
| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 |
| 4 | 4 | OTSL HTML | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 |
| | | OTSL | 0.952 0.923 | 0.909 | 0.938 | 0.843 | 3.77 |
| 2 | 4 | HTML | 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 |
| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 |
| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference |
|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------|
| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) |
| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 |
| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 |
| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 |
| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 |
## 5.2 Quantitative Results
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<table>
<location><page_9><loc_23><loc_41><loc_78><loc_57></location>
<caption>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.</caption>
<row_0><col_0><col_header>#</col_0><col_1><col_header>#</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
<row_1><col_0><col_header>enc-layers</col_0><col_1><col_header>dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
<row_0><col_0><col_header># enc-layers</col_0><col_1><col_header># dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>TEDs</col_5><col_6><col_header>mAP</col_6><col_7><col_header>Inference</col_7></row_0>
<row_1><col_0><col_header># enc-layers</col_0><col_1><col_header># dec-layers</col_1><col_2><col_header>Language</col_2><col_3><col_header>simple</col_3><col_4><col_header>complex</col_4><col_5><col_header>all</col_5><col_6><col_header>(0.75)</col_6><col_7><col_header>time (secs)</col_7></row_1>
<row_2><col_0><body>6</col_0><col_1><body>6</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.965 0.969</col_3><col_4><body>0.934 0.927</col_4><col_5><body>0.955 0.955</col_5><col_6><body>0.88 0.857</col_6><col_7><body>2.73 5.39</col_7></row_2>
<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904</col_4><col_5><body>0.927</col_5><col_6><body>0.853</col_6><col_7><body>1.97</col_7></row_3>
<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.909 0.897</col_4><col_5><body>0.938</col_5><col_6><body>0.843</col_6><col_7><body>3.77</col_7></row_4>
<row_5><col_0><body></col_0><col_1><body></col_1><col_2><body>HTML</col_2><col_3><body></col_3><col_4><body>0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
<row_3><col_0><body>4</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.938 0.952</col_3><col_4><body>0.904 0.909</col_4><col_5><body>0.927 0.938</col_5><col_6><body>0.853 0.843</col_6><col_7><body>1.97 3.77</col_7></row_3>
<row_4><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.923 0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_4>
<row_5><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_5>
</table>
<caption><location><page_9><loc_22><loc_59><loc_79><loc_65></location>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.</caption>
<subtitle-level-1><location><page_9><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</subtitle-level-1>
@@ -92,14 +91,11 @@
<table>
<location><page_10><loc_23><loc_67><loc_77><loc_80></location>
<caption>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).</caption>
<row_0><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_0>
<row_1><col_0><body></col_0><col_1><col_header>Language</col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_1>
<row_2><col_0><row_header>PubTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.965</col_2><col_3><body>0.934</col_3><col_4><body>0.955</col_4><col_5><body>0.88</col_5><col_6><body>2.73</col_6></row_2>
<row_3><col_0><row_header>PubTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.969</col_2><col_3><body>0.927</col_3><col_4><body>0.955</col_4><col_5><body>0.857</col_5><col_6><body>5.39</col_6></row_3>
<row_4><col_0><row_header>FinTabNet</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.955</col_2><col_3><body>0.961</col_3><col_4><body>0.959</col_4><col_5><body>0.862</col_5><col_6><body>1.85</col_6></row_4>
<row_5><col_0><row_header>FinTabNet</col_0><col_1><row_header>HTML</col_1><col_2><body>0.917</col_2><col_3><body>0.922</col_3><col_4><body>0.92</col_4><col_5><body>0.722</col_5><col_6><body>3.26</col_6></row_5>
<row_6><col_0><row_header>PubTables-1M</col_0><col_1><row_header>OTSL</col_1><col_2><body>0.987</col_2><col_3><body>0.964</col_3><col_4><body>0.977</col_4><col_5><body>0.896</col_5><col_6><body>1.79</col_6></row_6>
<row_7><col_0><row_header>PubTables-1M</col_0><col_1><row_header>HTML</col_1><col_2><body>0.983</col_2><col_3><body>0.944</col_3><col_4><body>0.966</col_4><col_5><body>0.889</col_5><col_6><body>3.26</col_6></row_7>
<row_0><col_0><col_header>Data set</col_0><col_1><col_header>Language</col_1><col_2><col_header>TEDs</col_2><col_3><col_header>TEDs</col_3><col_4><col_header>TEDs</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_0>
<row_1><col_0><col_header>Data set</col_0><col_1><col_header>Language</col_1><col_2><col_header>simple</col_2><col_3><col_header>complex</col_3><col_4><col_header>all</col_4><col_5><col_header>mAP(0.75)</col_5><col_6><col_header>Inference time (secs)</col_6></row_1>
<row_2><col_0><body>PubTabNet</col_0><col_1><body>OTSL HTML</col_1><col_2><body>0.965 0.969</col_2><col_3><body>0.934 0.927</col_3><col_4><body>0.955 0.955</col_4><col_5><body>0.88 0.857</col_5><col_6><body>2.73 5.39</col_6></row_2>
<row_3><col_0><body>FinTabNet</col_0><col_1><body>OTSL HTML</col_1><col_2><body>0.955 0.917</col_2><col_3><body>0.961 0.922</col_3><col_4><body>0.959 0.92</col_4><col_5><body>0.862 0.722</col_5><col_6><body>1.85 3.26</col_6></row_3>
<row_4><col_0><body>PubTables-1M</col_0><col_1><body>OTSL HTML</col_1><col_2><body>0.987 0.983</col_2><col_3><body>0.964 0.944</col_3><col_4><body>0.977 0.966</col_4><col_5><body>0.896 0.889</col_5><col_6><body>1.79 3.26</col_6></row_4>
</table>
<caption><location><page_10><loc_22><loc_82><loc_79><loc_85></location>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).</caption>
<subtitle-level-1><location><page_10><loc_22><loc_62><loc_42><loc_64></location>5.3 Qualitative Results</subtitle-level-1>
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@@ -130,14 +130,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly
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.
| # | # | Language | TEDs | TEDs | TEDs | mAP | Inference |
|------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
| enc-layers | dec-layers | Language | simple | complex | all | (0.75) | time (secs) |
| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 |
| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 | 0.927 | 0.853 | 1.97 |
| 2 | 4 | OTSL | 0.923 0.945 | 0.909 0.897 | 0.938 | 0.843 | 3.77 |
| | | HTML | | 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 |
| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 |
| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference |
|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------|
| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) |
| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 |
| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 |
| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 |
| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 |
## 5.2 Quantitative Results
@@ -147,15 +146,12 @@ Additionally, the results show that OTSL has an advantage over HTML when applied
Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).
| | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) |
|--------------|------------|--------|---------|--------|-------------|-------------------------|
| | Language | simple | complex | all | mAP(0.75) | Inference time (secs) |
| PubTabNet | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 |
| PubTabNet | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 |
| FinTabNet | OTSL | 0.955 | 0.961 | 0.959 | 0.862 | 1.85 |
| FinTabNet | HTML | 0.917 | 0.922 | 0.92 | 0.722 | 3.26 |
| PubTables-1M | OTSL | 0.987 | 0.964 | 0.977 | 0.896 | 1.79 |
| PubTables-1M | HTML | 0.983 | 0.944 | 0.966 | 0.889 | 3.26 |
| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) |
|--------------|------------|-------------|-------------|-------------|-------------|-------------------------|
| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) |
| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 |
| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 |
| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 |
## 5.3 Qualitative Results
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<table>
<location><page_9><loc_11><loc_9><loc_89><loc_50></location>
<caption>Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority</caption>
<row_0><col_0><row_header>User action</col_0><col_1><body>*JOBCTL</col_1><col_2><body>QIBM_DB_SECADM</col_2><col_3><body>QIBM_DB_SQLADM</col_3><col_4><body>QIBM_DB_SYSMON</col_4><col_5><body>No Authority</col_5></row_0>
<row_0><col_0><body>User action</col_0><col_1><col_header>*JOBCTL</col_1><col_2><col_header>QIBM_DB_SECADM</col_2><col_3><col_header>QIBM_DB_SQLADM</col_3><col_4><col_header>QIBM_DB_SYSMON</col_4><col_5><col_header>No Authority</col_5></row_0>
<row_1><col_0><row_header>SET CURRENT DEGREE (SQL statement)</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_1>
<row_2><col_0><row_header>CHGQRYA command targeting a different user’s job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_2>
<row_3><col_0><row_header>STRDBMON or ENDDBMON commands targeting a different user’s job</col_0><col_1><body>X</col_1><col_2><body></col_2><col_3><body>X</col_3><col_4><body></col_4><col_5><body></col_5></row_3>
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