modify PP-DocTranslation.md (#4276)

docs/pipeline_usage/tutorials/ocr_pipelines/PP-DocTranslation.md

@@ -27,10 +27,11 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>PP-LCNet_x1_0_doc_ori</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">训练模型</a></td>
+<td>PP-LCNet_x1_0_doc_ori</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">训练模型</a></td>
 <td>99.06</td>
-<td>2.31 / 0.43</td>
-<td>3.37 / 1.27</td>
+<td>2.62 / 0.59</td>
+<td>3.24 / 1.19</td>
 <td>7</td>
 <td>基于PP-LCNet_x1_0的文档图像分类模型，含有四个类别，即0度，90度，180度，270度</td>
 </tr>
@@ -48,7 +49,8 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>UVDoc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UVDoc_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UVDoc_pretrained.pdparams">训练模型</a></td>
+<td>UVDoc</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UVDoc_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UVDoc_pretrained.pdparams">训练模型</a></td>
 <td>0.179</td>
 <td>30.3 M</td>
 <td>高精度文本图像矫正模型</td>
@@ -69,33 +71,37 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>PP-DocLayout_plus-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout_plus-L_pretrained.pdparams">训练模型</a></td>
+<td>PP-DocLayout_plus-L</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout_plus-L_pretrained.pdparams">训练模型</a></td>
 <td>83.2</td>
-<td>34.6244 / 10.3945</td>
-<td>510.57 / - </td>
+<td>53.03 / 17.23</td>
+<td>634.62 / 378.32</td>
 <td>126.01 M</td>
 <td>基于RT-DETR-L在包含中英文论文、多栏杂志、报纸、PPT、合同、书本、试卷、研报、古籍、日文文档、竖版文字文档等场景的自建数据集训练的更高精度版面区域定位模型</td>
 </tr>
-<td>PP-DocLayout-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
+<td>PP-DocLayout-L</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td>
 <td>90.4</td>
-<td>34.6244 / 10.3945</td>
-<td>510.57 / -</td>
+<td>33.59 / 33.59</td>
+<td>503.01 / 251.08</td>
 <td>123.76 M</td>
 <td>基于RT-DETR-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的高精度版面区域定位模型</td>
 </tr>
 <tr>
-<td>PP-DocLayout-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
+<td>PP-DocLayout-M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td>
 <td>75.2</td>
-<td>13.3259 / 4.8685</td>
-<td>44.0680 / 44.0680</td>
+<td>13.03 / 4.72</td>
+<td>43.39 / 24.44</td>
 <td>22.578</td>
 <td>基于PicoDet-L在包含中英文论文、杂志、合同、书本、试卷和研报等场景的自建数据集训练的精度效率平衡的版面区域定位模型</td>
 </tr>
 <tr>
-<td>PP-DocLayout-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
+<td>PP-DocLayout-S</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td>
 <td>70.9</td>
-<td>8.3008 / 2.3794</td>
-<td>10.0623 / 9.9296</td>
+<td>11.54 / 3.86</td>
+<td>18.53 / 6.29</td>
 <td>4.834</td>
 <td>基于PicoDet-S在中英文论文、杂志、合同、书本、试卷和研报等场景上自建数据集训练的高效率版面区域定位模型</td>
 </tr>
@@ -115,8 +121,8 @@ comments: true
 <td>SLANeXt_wired</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANeXt_wired_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wired_pretrained.pdparams">训练模型</a></td>
 <td rowspan="2">69.65</td>
-<td rowspan="2">--</td>
-<td rowspan="2">--</td>
+<td rowspan="2">85.92 / 85.92</td>
+<td rowspan="2">- / 501.66</td>
 <td rowspan="2">351M</td>
 <td rowspan="2">SLANeXt 系列是百度飞桨视觉团队自研的新一代表格结构识别模型。相较于 SLANet 和 SLANet_plus，SLANeXt 专注于对表格结构进行识别，并且对有线表格(wired)和无线表格(wireless)的识别分别训练了专用的权重，对各类型表格的识别能力都得到了明显提高，特别是对有线表格的识别能力得到了大幅提升。</td>
 </tr>
@@ -136,10 +142,11 @@ comments: true
 <th>模型存储大小 (M)</th>
 </tr>
 <tr>
-<td>PP-LCNet_x1_0_table_cls</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/CLIP_vit_base_patch16_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td>
+<td>PP-LCNet_x1_0_table_cls</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/CLIP_vit_base_patch16_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td>
 <td>94.2</td>
-<td>2.35 / 0.47</td>
-<td>4.03 / 1.35</td>
+<td>2.62 / 0.60</td>
+<td>3.17 / 1.14</td>
 <td>6.6M</td>
 </tr>
 </table>
@@ -158,8 +165,8 @@ comments: true
 <td>RT-DETR-L_wired_table_cell_det</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td>
 <td rowspan="2">82.7</td>
-<td rowspan="2">35.00 / 10.45</td>
-<td rowspan="2">495.51 / 495.51</td>
+<td rowspan="2">33.47 / 27.02</td>
+<td rowspan="2">402.55 / 256.56</td>
 <td rowspan="2">124M</td>
 <td rowspan="2">RT-DETR 是第一个实时的端到端目标检测模型。百度飞桨视觉团队基于 RT-DETR-L 作为基础模型，在自建表格单元格检测数据集上完成预训练，实现了对有线表格、无线表格均有较好性能的表格单元格检测。
 </td>
@@ -184,50 +191,56 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>PP-OCRv5_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv5_server_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">训练模型</a></td>
 <td>83.8</td>
 <td>89.55 / 70.19</td>
-<td>371.65 / 371.65</td>
+<td>383.15 / 383.15</td>
 <td>84.3</td>
 <td>PP-OCRv5 的服务端文本检测模型，精度更高，适合在性能较好的服务器上部署</td>
 </tr>
 <tr>
-<td>PP-OCRv5_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv5_mobile_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">训练模型</a></td>
 <td>79.0</td>
-<td>8.79 / 3.13</td>
-<td>51.00 / 28.58</td>
+<td>10.67 / 6.36</td>
+<td>57.77 / 28.15</td>
 <td>4.7</td>
 <td>PP-OCRv5 的移动端文本检测模型，效率更高，适合在端侧设备部署</td>
 </tr>
 <tr>
-<td>PP-OCRv4_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_server_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">训练模型</a></td>
 <td>69.2</td>
-<td>83.34 / 80.91</td>
-<td>442.58 / 442.58</td>
+<td>127.82 / 98.87</td>
+<td>585.95 / 489.77</td>
 <td>109</td>
 <td>PP-OCRv4 的服务端文本检测模型，精度更高，适合在性能较好的服务器上部署</td>
 </tr>
 <tr>
-<td>PP-OCRv4_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_mobile_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">训练模型</a></td>
 <td>63.8</td>
-<td>8.79 / 3.13</td>
-<td>51.00 / 28.58</td>
+<td>9.87 / 4.17</td>
+<td>56.60 / 20.79</td>
 <td>4.7</td>
 <td>PP-OCRv4 的移动端文本检测模型，效率更高，适合在端侧设备部署</td>
 </tr>
 <tr>
-<td>PP-OCRv3_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_mobile_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv3_mobile_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_mobile_det_pretrained.pdparams">训练模型</a></td>
 <td>精度接近 PP-OCRv4_mobile_det</td>
-<td>8.44 / 2.91</td>
-<td>27.87 / 27.87</td>
+<td>9.90 / 3.60</td>
+<td>41.93 / 20.76</td>
 <td>2.1</td>
 <td>PP-OCRv3 的移动端文本检测模型，效率更高，适合在端侧设备部署</td>
 </tr>
 <tr>
-<td>PP-OCRv3_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_server_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv3_server_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_server_det_pretrained.pdparams">训练模型</a></td>
 <td>精度接近 PP-OCRv4_server_det</td>
-<td>65.41 / 13.67</td>
-<td>305.07 / 305.07</td>
+<td>119.50 / 75.00</td>
+<td>379.35 / 318.35</td>
 <td>102.1</td>
 <td>PP-OCRv3 的服务端文本检测模型，精度更高，适合在性能较好的服务器上部署</td>
 </tr>
@@ -247,53 +260,59 @@ comments: true
 <th>介绍</th>
 </tr>
 <tr>
-<td>PP-OCRv5_server_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>PP-OCRv5_server_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 PP-OCRv5_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_rec_pretrained.pdparams">训练模型</a></td>
 <td>86.38</td>
-<td> 8.45/2.36 </td>
-<td> 122.69/122.69 </td>
+<td>8.46 / 2.36</td>
+<td>31.21 / 31.21</td>
 <td>81 M</td>
 <td rowspan="2">PP-OCRv5_rec 是新一代文本识别模型。该模型致力于以单一模型高效、精准地支持简体中文、繁体中文、英文、日文四种主要语言，以及手写、竖版、拼音、生僻字等复杂文本场景的识别。在保持识别效果的同时，兼顾推理速度和模型鲁棒性，为各种场景下的文档理解提供高效、精准的技术支撑。</td>
 </tr>
 <tr>
-<td>PP-OCRv5_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>PP-OCRv5_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 PP-OCRv5_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>81.29</td>
-<td> 1.46/5.43 </td>
-<td> 5.32/91.79 </td>
+<td>5.43 / 1.46</td>
+<td>21.20 / 5.32</td>
 <td>16 M</td>
 </tr>
 <tr>
-<td>PP-OCRv4_server_rec_doc</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>PP-OCRv4_server_rec_doc</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 PP-OCRv4_server_rec_doc_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_doc_pretrained.pdparams">训练模型</a></td>
-<td>81.53</td>
-<td>6.65 / 2.38</td>
-<td>32.92 / 32.92</td>
+<td>86.58</td>
+<td>8.69 / 2.78</td>
+<td>37.93 / 37.93</td>
 <td>74.7 M</td>
 <td>PP-OCRv4_server_rec_doc是在PP-OCRv4_server_rec的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力</td>
 </tr>
 <tr>
-<td>PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>78.74</td>
-<td>4.82 / 1.20</td>
-<td>16.74 / 4.64</td>
+<td>5.26 / 1.12</td>
+<td>17.48 / 3.61</td>
 <td>10.6 M</td>
 <td>PP-OCRv4的轻量级识别模型，推理效率高，可以部署在包含端侧设备的多种硬件设备中</td>
 </tr>
 <tr>
-<td>PP-OCRv4_server_rec </td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_server_rec </td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">训练模型</a></td>
 <td>80.61 </td>
-<td>6.58 / 2.43</td>
-<td>33.17 / 33.17</td>
+<td>8.75 / 2.49</td>
+<td>36.93 / 36.93</td>
 <td>71.2 M</td>
 <td>PP-OCRv4的服务器端模型，推理精度高，可以部署在多种不同的服务器上</td>
 </tr>
 <tr>
-<td>PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>72.96</td>
-<td>5.87 / 1.19</td>
-<td>9.07 / 4.28</td>
+<td>3.89 / 1.16</td>
+<td>8.72 / 3.56</td>
 <td>9.2 M</td>
 <td>PP-OCRv3的轻量级识别模型，推理效率高，可以部署在包含端侧设备的多种硬件设备中</td>
 </tr>
@@ -309,10 +328,11 @@ PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ec
 <th>介绍</th>
 </tr>
 <tr>
-<td>ch_SVTRv2_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_SVTRv2_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">训练模型</a></td>
+<td>ch_SVTRv2_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_SVTRv2_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">训练模型</a></td>
 <td>68.81</td>
-<td>8.08 / 2.74</td>
-<td>50.17 / 42.50</td>
+<td>10.38 / 8.31</td>
+<td>66.52 / 30.83</td>
 <td>73.9 M</td>
 <td rowspan="1">
 SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队研发的服务端文本识别模型，其在PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务中荣获一等奖，A榜端到端识别精度相比PP-OCRv4提升6%。
@@ -330,10 +350,11 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 <th>介绍</th>
 </tr>
 <tr>
-<td>ch_RepSVTR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_RepSVTR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">训练模型</a></td>
+<td>ch_RepSVTR_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_RepSVTR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">训练模型</a></td>
 <td>65.07</td>
-<td>5.93 / 1.62</td>
-<td>20.73 / 7.32</td>
+<td>6.29 / 1.57</td>
+<td>20.64 / 5.40</td>
 <td>22.1 M</td>
 <td rowspan="1">    RepSVTR 文本识别模型是一种基于SVTRv2 的移动端文本识别模型，其在PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务中荣获一等奖，B榜端到端识别精度相比PP-OCRv4提升2.5%，推理速度持平。</td>
 </tr>
@@ -350,20 +371,22 @@ SVTRv2 是一种由复旦大学视觉与学习实验室（FVL）的OpenOCR团队
 <th>介绍</th>
 </tr>
 <tr>
-<td>en_PP-OCRv4_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>en_PP-OCRv4_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 en_PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/en_PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td> 70.39</td>
-<td>4.81 / 0.75</td>
-<td>16.10 / 5.31</td>
+<td>4.81 / 1.23</td>
+<td>17.20 / 4.18</td>
 <td>6.8 M</td>
 <td>基于PP-OCRv4识别模型训练得到的超轻量英文识别模型，支持英文、数字识别</td>
 </tr>
 <tr>
-<td>en_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>en_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 en_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/en_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>70.69</td>
-<td>5.44 / 0.75</td>
-<td>8.65 / 5.57</td>
+<td>3.56 / 0.78</td>
+<td>8.44 / 5.78</td>
 <td>7.8 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量英文识别模型，支持英文、数字识别</td>
 </tr>
@@ -380,92 +403,102 @@ en_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model
 <th>介绍</th>
 </tr>
 <tr>
-<td>korean_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>korean_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 korean_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/korean_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>60.21</td>
-<td>5.40 / 0.97</td>
-<td>9.11 / 4.05</td>
+<td>3.73 / 0.98</td>
+<td>8.76 / 2.91</td>
 <td>8.6 M</td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量韩文识别模型，支持韩文、数字识别</td>
 </tr>
 <tr>
-<td>japan_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>japan_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 japan_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/japan_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>45.69</td>
-<td>5.70 / 1.02</td>
-<td>8.48 / 4.07</td>
+<td>3.86 / 1.01</td>
+<td>8.62 / 2.92</td>
 <td>8.8 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量日文识别模型，支持日文、数字识别</td>
 </tr>
 <tr>
-<td>chinese_cht_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>chinese_cht_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 chinese_cht_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/chinese_cht_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>82.06</td>
-<td>5.90 / 1.28</td>
-<td>9.28 / 4.34</td>
+<td>3.90 / 1.16</td>
+<td>9.24 / 3.18</td>
 <td>9.7 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量繁体中文识别模型，支持繁体中文、数字识别</td>
 </tr>
 <tr>
-<td>te_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>te_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 te_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/te_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>95.88</td>
-<td>5.42 / 0.82</td>
-<td>8.10 / 6.91</td>
+<td>3.59 / 0.81</td>
+<td>8.28 / 6.21</td>
 <td>7.8 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量泰卢固文识别模型，支持泰卢固文、数字识别</td>
 </tr>
 <tr>
-<td>ka_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>ka_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 ka_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ka_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>96.96</td>
-<td>5.25 / 0.79</td>
-<td>9.09 / 3.86</td>
+<td>3.49 / 0.89</td>
+<td>8.63 / 2.77</td>
 <td>8.0 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量卡纳达文识别模型，支持卡纳达文、数字识别</td>
 </tr>
 <tr>
-<td>ta_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>ta_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 ta_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ta_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>76.83</td>
-<td>5.23 / 0.75</td>
-<td>10.13 / 4.30</td>
+<td>3.49 / 0.86</td>
+<td>8.35 / 3.41</td>
 <td>8.0 M </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量泰米尔文识别模型，支持泰米尔文、数字识别</td>
 </tr>
 <tr>
-<td>latin_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>latin_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 latin_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/latin_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>76.93</td>
-<td>5.20 / 0.79</td>
-<td>8.83 / 7.15</td>
+<td>3.53 / 0.78</td>
+<td>8.50 / 6.83</td>
 <td>7.8 M</td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量拉丁文识别模型，支持拉丁文、数字识别</td>
 </tr>
 <tr>
-<td>arabic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>arabic_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 arabic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/arabic_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>73.55</td>
-<td>5.35 / 0.79</td>
-<td>8.80 / 4.56</td>
+<td>3.60 / 0.83</td>
+<td>8.44 / 4.69</td>
 <td>7.8 M</td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量阿拉伯字母识别模型，支持阿拉伯字母、数字识别</td>
 </tr>
 <tr>
-<td>cyrillic_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>cyrillic_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 cyrillic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/cyrillic_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>94.28</td>
-<td>5.23 / 0.76</td>
-<td>8.89 / 3.88</td>
+<td>3.56 / 0.79</td>
+<td>8.22 / 2.76</td>
 <td>7.9 M  </td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量斯拉夫字母识别模型，支持斯拉夫字母、数字识别</td>
 </tr>
 <tr>
-<td>devanagari_PP-OCRv3_mobile_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
+<td>devanagari_PP-OCRv3_mobile_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\
 devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/devanagari_PP-OCRv3_mobile_rec_pretrained.pdparams">训练模型</a></td>
 <td>96.44</td>
-<td>5.22 / 0.79</td>
-<td>8.56 / 4.06</td>
+<td>3.60 / 0.78</td>
+<td>6.95 / 2.87</td>
 <td>7.9 M</td>
 <td>基于PP-OCRv3识别模型训练得到的超轻量梵文字母识别模型，支持梵文字母、数字识别</td>
 </tr>
@@ -486,10 +519,11 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 </thead>
 <tbody>
 <tr>
-<td>PP-LCNet_x0_25_textline_ori</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_25_textline_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_25_textline_ori_pretrained.pdparams">训练模型</a></td>
+<td>PP-LCNet_x0_25_textline_ori</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_25_textline_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_25_textline_ori_pretrained.pdparams">训练模型</a></td>
 <td>95.54</td>
-<td>-</td>
-<td>-</td>
+<td>2.16 / 0.41</td>
+<td>2.37 / 0.73</td>
 <td>0.32</td>
 <td>基于PP-LCNet_x0_25的文本行分类模型，含有两个类别，即0度，180度</td>
 </tr>
@@ -506,31 +540,33 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 <th>模型存储大小 (M)</th>
 <th>介绍</th>
 </tr>
-<td>UniMERNet</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">训练模型</a></td>
+<td>UniMERNet</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">训练模型</a></td>
 <td>86.13</td>
 <td>2266.96/-</td>
 <td>-/-</td>
 <td>1.4 G</td>
 <td>UniMERNet是由上海AI Lab研发的一款公式识别模型。该模型采用Donut Swin作为编码器，MBartDecoder作为解码器，并通过在包含简单公式、复杂公式、扫描捕捉公式和手写公式在内的一百万数据集上进行训练，大幅提升了模型对真实场景公式的识别准确率</td>
-<tr>
-<td>PP-FormulaNet-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">训练模型</a></td>
+<td>PP-FormulaNet-S</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">训练模型</a></td>
 <td>87.12</td>
-<td>202.25/-</td>
-<td>-/-</td>
+<td>1311.84 / 1311.84</td>
+<td>- / 8288.07</td>
 <td>167.9 M</td>
 <td rowspan="2">PP-FormulaNet 是由百度飞桨视觉团队开发的一款先进的公式识别模型，支持5万个常见LateX源码词汇的识别。PP-FormulaNet-S 版本采用了 PP-HGNetV2-B4 作为其骨干网络，通过并行掩码和模型蒸馏等技术，大幅提升了模型的推理速度，同时保持了较高的识别精度，适用于简单印刷公式、跨行简单印刷公式等场景。而 PP-FormulaNet-L 版本则基于 Vary_VIT_B 作为骨干网络，并在大规模公式数据集上进行了深入训练，在复杂公式的识别方面，相较于PP-FormulaNet-S表现出显著的提升，适用于简单印刷公式、复杂印刷公式、手写公式等场景。 </td>
 
 </tr>
-<td>PP-FormulaNet-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">训练模型</a></td>
+<td>PP-FormulaNet-L</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">训练模型</a></td>
 <td>92.13</td>
 <td>1976.52/-</td>
 <td>-/-</td>
 <td>535.2 M</td>
-<tr>
-<td>LaTeX_OCR_rec</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">训练模型</a></td>
+<td>LaTeX_OCR_rec</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">训练模型</a></td>
 <td>71.63</td>
-<td>-/-</td>
-<td>-/-</td>
+<td>1088.89 / 1088.89</td>
+<td>- / -</td>
 <td>89.7 M</td>
 <td>LaTeX-OCR是一种基于自回归大模型的公式识别算法，通过采用 Hybrid ViT 作为骨干网络，transformer作为解码器，显著提升了公式识别的准确性。</td>
 </tr>
@@ -551,18 +587,20 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 </thead>
 <tbody>
 <tr>
-<td>PP-OCRv4_server_seal_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_seal_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_server_seal_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_seal_det_pretrained.pdparams">训练模型</a></td>
 <td>98.21</td>
-<td>74.75 / 67.72</td>
-<td>382.55 / 382.55</td>
+<td>124.64 / 91.57</td>
+<td>545.68 / 439.86</td>
 <td>109</td>
 <td>PP-OCRv4的服务端印章文本检测模型，精度更高，适合在较好的服务器上部署</td>
 </tr>
 <tr>
-<td>PP-OCRv4_mobile_seal_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_seal_det_pretrained.pdparams">训练模型</a></td>
+<td>PP-OCRv4_mobile_seal_det</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_seal_det_pretrained.pdparams">训练模型</a></td>
 <td>96.47</td>
-<td>7.82 / 3.09</td>
-<td>48.28 / 23.97</td>
+<td>9.70 / 3.56</td>
+<td>50.38 / 19.64</td>
 <td>4.6</td>
 <td>PP-OCRv4的移动端印章文本检测模型，效率更高，适合在端侧部署</td>
 </tr>