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@@ -100,32 +100,12 @@ The formula recognition pipeline is designed to solve formula recognition tasks
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-> ❗ The above list includes the <b>3 core models</b> that are key supported by the text recognition module. The module actually supports a total of <b>11 full models</b>, including several predefined models with different categories. The complete model list is as follows:
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+> ❗ The above list includes the <b>3 core models</b> that are key supported by the text recognition module. The module actually supports a total of <b>6 full models</b>, including several predefined models with different categories. The complete model list is as follows:
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<details><summary> 👉 Details of Model List</summary>
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<details><summary> 👉 Details of Model List</summary>
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-<table>
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-<thead>
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-<tr>
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-<th>Model</th><th>Model Download Link</th>
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-<th>mAP(0.5) (%)</th>
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-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
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-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
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-<th>Model Storage Size (M)</th>
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-<th>Introduction</th>
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-</tr>
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-</thead>
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-<tbody>
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-<tr>
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-<td>PicoDet_layout_1x_table</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_table_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_table_pretrained.pdparams">Training Model</a></td>
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-<td>97.5</td>
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-<td>8.02 / 3.09</td>
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-<td>23.70 / 20.41</td>
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-<td>7.4 M</td>
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-<td>A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting table regions.</td>
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-</tr>
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-</tbody></table>
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-* <b>3-Class Layout Detection Model, including Table, Image, and Stamp</b>
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+* <b>17-Class Area Detection Model, including 17 common layout categories: Paragraph Title, Image, Text, Number, Abstract, Content, Figure Caption, Formula, Table, Table Caption, References, Document Title, Footnote, Header, Algorithm, Footer, and Stamp</b>
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<table>
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<table>
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@@ -139,55 +119,34 @@ The formula recognition pipeline is designed to solve formula recognition tasks
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</thead>
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</thead>
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<tbody>
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<tbody>
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<tr>
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<tr>
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-<td>PicoDet-S_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">Training Model</a></td>
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-<td>88.2</td>
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-<td>8.99 / 2.22</td>
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-<td>16.11 / 8.73</td>
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+<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Training Model</a></td>
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+<td>87.4</td>
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+<td>9.11 / 2.12</td>
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+<td>15.42 / 9.12</td>
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<td>4.8</td>
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<td>4.8</td>
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<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
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<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
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</tr>
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</tr>
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-<td>PicoDet-L_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">Training Model</a></td>
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+<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
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<td>89.0</td>
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<td>89.0</td>
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-<td>13.05 / 4.50</td>
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-<td>41.30 / 41.30</td>
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+<td>13.50 / 4.69</td>
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+<td>43.32 / 43.32</td>
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<td>22.6</td>
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<td>22.6</td>
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<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
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<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
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</tr>
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</tr>
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<tr>
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-<td>RT-DETR-H_layout_3cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_3cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">Training Model</a></td>
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-<td>95.8</td>
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-<td>114.93 / 27.71</td>
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-<td>947.56 / 947.56</td>
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-<td>470.1</td>
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+<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
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+<td>98.3</td>
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+<td>115.29 / 104.09</td>
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+<td>995.27 / 995.27</td>
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+<td>470.2</td>
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<td>A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.</td>
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<td>A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.</td>
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</tr>
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</tr>
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-</tbody></table>
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+</tbody>
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+</table>
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-* <b>5-Class English Document Area Detection Model, including Text, Title, Table, Image, and List</b>
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-<table>
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-<thead>
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-<tr>
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-<th>Model</th><th>Model Download Link</th>
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-<th>mAP(0.5) (%)</th>
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-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
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-<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
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-<th>Model Storage Size (M)</th>
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-<th>Introduction</th>
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-</tr>
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-</thead>
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-<tbody>
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-<tr>
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-<td>PicoDet_layout_1x</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet_layout_1x_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_layout_1x_pretrained.pdparams">Training Model</a></td>
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-<td>97.8</td>
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-<td>9.03 / 3.10</td>
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-<td>25.82 / 20.70</td>
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-<td>7.4</td>
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-<td>A high-efficiency English document layout area localization model trained on the PubLayNet dataset using PicoDet-1x.</td>
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-</tr>
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-</tbody></table>
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-* <b>17-Class Area Detection Model, including 17 common layout categories: Paragraph Title, Image, Text, Number, Abstract, Content, Figure Caption, Formula, Table, Table Caption, References, Document Title, Footnote, Header, Algorithm, Footer, and Stamp</b>
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+* <b>Layout detection model, including 23 common categories: document title, paragraph title, text, page number, abstract, table of contents, references, footnotes, header, footer, algorithm, formula, formula number, image, chart title, table, table title, seal, chart title, chart, header image, footer image, sidebar text</b>
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<table>
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<table>
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<thead>
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@@ -201,31 +160,33 @@ The formula recognition pipeline is designed to solve formula recognition tasks
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</thead>
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</thead>
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<tbody>
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<tbody>
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<tr>
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-<td>PicoDet-S_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-S_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">Training Model</a></td>
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-<td>87.4</td>
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-<td>9.11 / 2.12</td>
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-<td>15.42 / 9.12</td>
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-<td>4.8</td>
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-<td>A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S.</td>
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+<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">Training Model</a></td>
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+<td>90.4</td>
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+<td>34.6244 / 10.3945</td>
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+<td>510.57 / -</td>
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+<td>123.76 M</td>
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+<td>A high-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using RT-DETR-L.</td>
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</tr>
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</tr>
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<tr>
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-<td>PicoDet-L_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PicoDet-L_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">Training Model</a></td>
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-<td>89.0</td>
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-<td>13.50 / 4.69</td>
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-<td>43.32 / 43.32</td>
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-<td>22.6</td>
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-<td>A balanced efficiency and precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-L.</td>
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+<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">Training Model</a></td>
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+<td>75.2</td>
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+<td>13.3259 / 4.8685</td>
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+<td>44.0680 / 44.0680</td>
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+<td>22.578</td>
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+<td>A layout area localization model with balanced precision and efficiency, trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-L.</td>
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</tr>
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</tr>
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-<td>RT-DETR-H_layout_17cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/RT-DETR-H_layout_17cls_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">Training Model</a></td>
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-<td>98.3</td>
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-<td>115.29 / 104.09</td>
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-<td>995.27 / 995.27</td>
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-<td>470.2</td>
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-<td>A high-precision layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using RT-DETR-H.</td>
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+<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/PP-DocLayout-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">Training Model</a></td>
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+<td>70.9</td>
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+<td>8.3008 / 2.3794</td>
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+<td>10.0623 / 9.9296</td>
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+<td>4.834</td>
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+<td>A high-efficiency layout area localization model trained on a self-built dataset containing Chinese and English papers, magazines, contracts, books, exams, and research reports using PicoDet-S.</td>
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</tr>
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</tbody>
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</tbody>
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</table>
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</table>
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</details>
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</details>
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<p><b>Formula Recognition Module </b></p>
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<p><b>Formula Recognition Module </b></p>
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@@ -274,9 +235,6 @@ The formula recognition pipeline is designed to solve formula recognition tasks
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- Document Image Orientation Classification Module: A self-built dataset using PaddleX, covering multiple scenarios such as ID cards and documents, containing 1000 images.
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- Document Image Orientation Classification Module: A self-built dataset using PaddleX, covering multiple scenarios such as ID cards and documents, containing 1000 images.
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- Text Image Rectification Module: [DocUNet](https://www3.cs.stonybrook.edu/~cvl/docunet.html).
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- Text Image Rectification Module: [DocUNet](https://www3.cs.stonybrook.edu/~cvl/docunet.html).
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- Layout Region Detection Module: A self-built layout region detection dataset using PaddleOCR, including 500 images of common document types such as Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
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- Layout Region Detection Module: A self-built layout region detection dataset using PaddleOCR, including 500 images of common document types such as Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
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- - Table Layout Detection Model: A self-built table region detection dataset using PaddleOCR, containing 7835 images of paper documents with tables in both Chinese and English.
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- - 3-Class Layout Detection Model: A self-built layout region detection dataset using PaddleOCR, including 1154 images of common document types such as Chinese and English papers, magazines, and research reports.
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- - 5-Class English Document Region Detection Model: The evaluation dataset from [PubLayNet](https://developer.ibm.com/exchanges/data/all/publaynet), containing 11245 images of English documents.
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- 17-Class Region Detection Model: A self-built layout region detection dataset using PaddleOCR, including 892 images of common document types such as Chinese and English papers, magazines, and research reports.
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- 17-Class Region Detection Model: A self-built layout region detection dataset using PaddleOCR, including 892 images of common document types such as Chinese and English papers, magazines, and research reports.
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- Formula Recognition Module: A self-built formula recognition test set using PaddleX.
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- Formula Recognition Module: A self-built formula recognition test set using PaddleX.
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- **Hardware Configuration**:
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- **Hardware Configuration**:
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@@ -288,7 +246,7 @@ The formula recognition pipeline is designed to solve formula recognition tasks
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| Mode | GPU Configuration | CPU Configuration | Acceleration Technology Combination |
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| Mode | GPU Configuration | CPU Configuration | Acceleration Technology Combination |
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|-------------|----------------------------------------|-------------------|---------------------------------------------------|
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|-------------|----------------------------------------|-------------------|---------------------------------------------------|
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-| Regular Mode| FP32 Precision / No TRT Acceleration | FP32 Precision / 8 Threads | PaddleInference |
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+| Normal Mode | FP32 Precision / No TRT Acceleration | FP32 Precision / 8 Threads | PaddleInference |
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| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
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| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
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