Merge pull request #8066 from WenmuZhou/doc2

update PP-Structurev to PP-StructureV

StyleText/README.md

@@ -120,7 +120,7 @@ In actual application scenarios, it is often necessary to synthesize pictures in
      * `with_label`：Whether the `label_file` is label file list.
    * `CorpusGenerator`：
      * `method`：Method of CorpusGenerator，supports `FileCorpus` and `EnNumCorpus`. If `EnNumCorpus` is used，No other configuration is needed，otherwise you need to set `corpus_file` and `language`.
-     * `language`：Language of the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko). 
+     * `language`：Language of the corpus. Currently, the tool only supports English(en), Simplified Chinese(ch) and Korean(ko).
      * `corpus_file`: Filepath of the corpus. Corpus file should be a text file which will be split by line-endings（'\n'）. Corpus generator samples one line each time.
 
 
@@ -171,9 +171,8 @@ After adding the above synthetic data for training, the accuracy of the recognit
 
 | Scenario | Characters | Raw Data | Test Data | Only Use Raw Data</br>Recognition Accuracy | New Synthetic Data | Simultaneous Use of Synthetic Data</br>Recognition Accuracy | Index Improvement |
 | -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
-| Metal surface | English and numbers | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
-| Random background | Korean       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
-
+| Metal surface | English and numbers | 2203     | 650      | 59.38%                     | 20000        | 75.46%                 | 16.08%      |
+| Random background | Korean       | 5631     | 1230     | 30.12%                     | 100000       | 50.57%                 | 20.45%      |
 
 <a name="Code_structure"></a>
 ### Code Structure

StyleText/README_ch.md

@@ -156,8 +156,8 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
 
 | 场景     | 字符       | 原始数据 | 测试数据 | 只使用原始数据</br>识别准确率 | 新增合成数据 | 同时使用合成数据</br>识别准确率 | 指标提升 |
 | -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
-| 金属表面 | 英文和数字 | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
-| 随机背景 | 韩语       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
+| 金属表面 | 英文和数字 | 2203     | 650      | 59.38%                     | 20000        | 75.46%                 | 16.08%      |
+| 随机背景 | 韩语       | 5631     | 1230     | 30.12%                     | 100000       | 50.57%                 | 20.45%      |
 
 
 <a name="代码结构"></a>

applications/PCB字符识别/PCB字符识别.md

@@ -266,8 +266,8 @@ python3 tools/eval.py \
 | 序号 | 方案 | hmean  |  效果提升  |   实验分析  |
 | -------- | -------- | -------- | -------- | -------- |
 |   1 |  PP-OCRv3英文超轻量检测预训练模型   | 64.64%     |     -     |    提供的预训练模型具有泛化能力       |
-|   2 | PP-OCRv3英文超轻量检测预训练模型 + 验证集padding    |  72.13%  |+7.5% | padding可以提升尺寸较小图片的检测效果|
-|   3 | PP-OCRv3英文超轻量检测预训练模型  + fine-tune   | 100% |  +27.9%     | fine-tune会提升垂类场景效果 |
+|   2 | PP-OCRv3英文超轻量检测预训练模型 + 验证集padding    |  72.13%  |+7.49% | padding可以提升尺寸较小图片的检测效果|
+|   3 | PP-OCRv3英文超轻量检测预训练模型  + fine-tune   | 100.00% |  +27.87%     | fine-tune会提升垂类场景效果 |
 
 
 ```
@@ -420,9 +420,9 @@ python3 tools/eval.py \
 | 序号 | 方案 | acc    |  效果提升  |   实验分析  |
 | -------- | -------- | -------- | -------- | -------- |
 |   1 | PP-OCRv3中英文超轻量识别预训练模型直接评估 | 46.67%     |     -     |    提供的预训练模型具有泛化能力       |
-|   2 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune   |  42.02%  |-4.6% | 在数据量不足的情况，反而比预训练模型效果低(也可以通过调整超参数再试试)|
-|   3 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 公开通用识别数据集   | 77% |  +30%     | 在数据量不足的情况下，可以考虑补充公开数据训练 |
-|   4 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量   | 99.99% |  +23%     | 如果能获取更多数据量的情况，可以通过增加数据量提升效果 |
+|   2 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune   |  42.02%  |-4.65% | 在数据量不足的情况，反而比预训练模型效果低(也可以通过调整超参数再试试)|
+|   3 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 公开通用识别数据集   | 77.00% |  +30.33%     | 在数据量不足的情况下，可以考虑补充公开数据训练 |
+|   4 | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量   | 99.99% |  +22.99%     | 如果能获取更多数据量的情况，可以通过增加数据量提升效果 |
 
 ```
 注：上述实验结果均是在1500张图片（1200张训练集，300张测试集）、2W张图片、添加公开通用识别数据集上训练、评估的得到，AIstudio只提供了100张数据，所以指标有所差异属于正常，只要策略有效、规律相同即可。
@@ -614,23 +614,23 @@ python3 tools/end2end/eval_end2end.py ./save_gt_label/ ./save_PPOCRV2_infer/
 | 序号 | 方案                                                     | hmean  | 效果提升 | 实验分析                              |
 | ---- | -------------------------------------------------------- | ------ | -------- | ------------------------------------- |
 | 1    | PP-OCRv3英文超轻量检测预训练模型直接评估                 | 64.64% | -        | 提供的预训练模型具有泛化能力          |
-| 2    | PP-OCRv3英文超轻量检测预训练模型 + 验证集padding直接评估 | 72.13% | +7.5%    | padding可以提升尺寸较小图片的检测效果 |
-| 3    | PP-OCRv3英文超轻量检测预训练模型  + fine-tune            | 100%   | +27.9%   | fine-tune会提升垂类场景效果           |
+| 2    | PP-OCRv3英文超轻量检测预训练模型 + 验证集padding直接评估 | 72.13% | +7.49%    | padding可以提升尺寸较小图片的检测效果 |
+| 3    | PP-OCRv3英文超轻量检测预训练模型  + fine-tune            | 100.00%   | +27.87%   | fine-tune会提升垂类场景效果           |
 
 * 识别
 
 | 序号 | 方案                                                         | acc    | 效果提升 | 实验分析                                                     |
 | ---- | ------------------------------------------------------------ | ------ | -------- | ------------------------------------------------------------ |
 | 1    | PP-OCRv3中英文超轻量识别预训练模型直接评估                   | 46.67% | -        | 提供的预训练模型具有泛化能力                                 |
-| 2    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune               | 42.02% | -4.6%    | 在数据量不足的情况，反而比预训练模型效果低(也可以通过调整超参数再试试) |
-| 3    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 公开通用识别数据集 | 77%    | +30%     | 在数据量不足的情况下，可以考虑补充公开数据训练               |
-| 4    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量 | 99.99% | +23%     | 如果能获取更多数据量的情况，可以通过增加数据量提升效果       |
+| 2    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune               | 42.02% | -4.65%    | 在数据量不足的情况，反而比预训练模型效果低(也可以通过调整超参数再试试) |
+| 3    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 公开通用识别数据集 | 77.00%    | +30.33%     | 在数据量不足的情况下，可以考虑补充公开数据训练               |
+| 4    | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量 | 99.99% | +22.99%     | 如果能获取更多数据量的情况，可以通过增加数据量提升效果       |
 
 * 端到端
 
 | det                                           | rec                                                          | fmeasure |
 | --------------------------------------------- | ------------------------------------------------------------ | -------- |
-| PP-OCRv3英文超轻量检测预训练模型  + fine-tune | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量 | 93.3%    |
+| PP-OCRv3英文超轻量检测预训练模型  + fine-tune | PP-OCRv3中英文超轻量识别预训练模型 + fine-tune + 增加PCB图像数量 | 93.30%    |
 
 *结论*
 

applications/中文表格识别.md

@@ -34,7 +34,7 @@
 ![](https://ai-studio-static-online.cdn.bcebos.com/5ffff2093a144a6993a75eef71634a52276015ee43a04566b9c89d353198c746)
 
 
-当前的表格识别算法不能很好的处理这些场景下的表格图像。在本例中，我们使用PP-Structurev2最新发布的表格识别模型SLANet来演示如何进行中文表格是识别。同时，为了方便作业流程，我们使用表格属性识别模型对表格图像的属性进行识别，对表格的难易程度进行判断，加快人工进行校对速度。
+当前的表格识别算法不能很好的处理这些场景下的表格图像。在本例中，我们使用PP-StructureV2最新发布的表格识别模型SLANet来演示如何进行中文表格是识别。同时，为了方便作业流程，我们使用表格属性识别模型对表格图像的属性进行识别，对表格的难易程度进行判断，加快人工进行校对速度。
 
 本项目AI Studio链接：https://aistudio.baidu.com/aistudio/projectdetail/4588067
 
@@ -192,14 +192,14 @@ plt.show()
 
 ### 2.3 训练
 
-这里选用PP-Structurev2中的表格识别模型[SLANet](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/table/SLANet.yml)
+这里选用PP-StructureV2中的表格识别模型[SLANet](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/table/SLANet.yml)
 
-SLANet是PP-Structurev2全新推出的表格识别模型，相比PP-Structurev1中TableRec-RARE，在速度不变的情况下精度提升4.7%。TEDS提升2%
+SLANet是PP-StructureV2全新推出的表格识别模型，相比PP-StructureV1中TableRec-RARE，在速度不变的情况下精度提升4.7%。TEDS提升2%
 
 
 |算法|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
 | --- | --- | --- | ---|
-| EDD<sup>[2]</sup> |x| 88.3% |x|
+| EDD<sup>[2]</sup> |x| 88.30% |x|
 | TableRec-RARE(ours) | 71.73%| 93.88% |779ms|
 | SLANet(ours) | 76.31%|    95.89%|766ms|
 

applications/光功率计数码管字符识别/光功率计数码管字符识别.md

@@ -182,15 +182,15 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P
 
 | ID | 策略 |  模型大小 | 精度 | 预测耗时（CPU + MKLDNN)|
 |-----|-----|--------|----| --- |
-| 01 | PP-OCRv2 | 8M | 74.8% | 8.54ms |
-| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
-| 03 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
-| 04 | SVTR_LCNet(h48) | 12M | 73.98% | 7.6ms |
-| 05 | + GTC | 12M | 75.8% | 7.6ms |
-| 06 | + TextConAug | 12M | 76.3% | 7.6ms |
-| 07 | + TextRotNet | 12M | 76.9% | 7.6ms |
-| 08 | + UDML | 12M | 78.4% | 7.6ms |
-| 09 | + UIM | 12M | 79.4% | 7.6ms |
+| 01 | PP-OCRv2 | 8M | 74.80% | 8.54ms |
+| 02 | SVTR_Tiny | 21M | 80.10% | 97.00ms |
+| 03 | SVTR_LCNet(h32) | 12M | 71.90% | 6.60ms |
+| 04 | SVTR_LCNet(h48) | 12M | 73.98% | 7.60ms |
+| 05 | + GTC | 12M | 75.80% | 7.60ms |
+| 06 | + TextConAug | 12M | 76.30% | 7.60ms |
+| 07 | + TextRotNet | 12M | 76.90% | 7.60ms |
+| 08 | + UDML | 12M | 78.40% | 7.60ms |
+| 09 | + UIM | 12M | 79.40% | 7.60ms |
 
 
 ### 3.3 开始训练

applications/印章弯曲文字识别.md

@@ -30,9 +30,9 @@
 
 | 任务 | 训练数据数量 | 精度 |
 | -------- | - | -------- |
-| 印章检测 | 1000    | 95%  |
-| 印章文字识别-端对端OCR方法 | 700    | 47%  |
-| 印章文字识别-两阶段OCR方法 |  700   | 55%  |
+| 印章检测 | 1000    | 95.00%  |
+| 印章文字识别-端对端OCR方法 | 700    | 47.00%  |
+| 印章文字识别-两阶段OCR方法 |  700   | 55.00%  |
 
 点击进入 [AI Studio 项目](https://aistudio.baidu.com/aistudio/projectdetail/4586113)
 

applications/发票关键信息抽取.md

@@ -145,8 +145,8 @@ LayoutXLM与VI-LayoutXLM针对该场景的训练结果如下所示。
 
 | 模型 | 迭代轮数 | Hmean |
 | :---: | :---: | :---: |
-| LayoutXLM | 50 | 100% |
-| VI-LayoutXLM | 50 | 100% |
+| LayoutXLM | 50 | 100.00% |
+| VI-LayoutXLM | 50 | 100.00% |
 
 可以看出，由于当前数据量较少，场景比较简单，因此2个模型的Hmean均达到了100%。
 
@@ -274,8 +274,8 @@ LayoutXLM与VI-LayoutXLM针对该场景的训练结果如下所示。
 
 | 模型 | 迭代轮数 | Hmean |
 | :---: | :---: | :---: |
-| LayoutXLM | 50 | 98.0% |
-| VI-LayoutXLM | 50 | 99.3% |
+| LayoutXLM | 50 | 98.00% |
+| VI-LayoutXLM | 50 | 99.30% |
 
 可以看出，对于VI-LayoutXLM相比LayoutXLM的Hmean高了1.3%。
 

applications/液晶屏读数识别.md

@@ -110,7 +110,7 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Globa
 
 |   | 方案                        |hmeans|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.5%|
+| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.50%|
 
 #### 4.3.2 预训练模型直接finetune
 ##### 修改配置文件
@@ -143,8 +143,8 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Globa
 结果如下：
 |   | 方案                        |hmeans|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.5%|
-| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.2%|
+| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.50%|
+| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.20%|
 
 #### 4.3.3 基于预训练模型Finetune_student模型
 
@@ -175,9 +175,9 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o G
 结果如下：
 |   | 方案                        |hmeans|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.5%|
-| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.2%|
-| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.0%|
+| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.50%|
+| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.20%|
+| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.00%|
 
 #### 4.3.4 基于预训练模型Finetune_teacher模型
 
@@ -233,10 +233,10 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml -o Globa
 结果如下：
 |   | 方案                        |hmeans|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.5%|
-| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.2%|
-| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.0%|
-| 3 | PP-OCRv3中英文超轻量检测预训练模型fintune教师模型 |84.8%|
+| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.50%|
+| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.20%|
+| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.00%|
+| 3 | PP-OCRv3中英文超轻量检测预训练模型fintune教师模型 |84.80%|
 
 #### 4.3.5 采用CML蒸馏进一步提升student模型精度
 
@@ -294,11 +294,11 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Globa
 结果如下：
 |   | 方案                        |hmeans|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.5%|
-| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.2%|
-| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.0%|
-| 3 | PP-OCRv3中英文超轻量检测预训练模型fintune教师模型 |84.8%|
-| 4 | 基于2和3训练好的模型fintune |82.7%|
+| 0 | PP-OCRv3中英文超轻量检测预训练模型直接预测 |47.50%|
+| 1 | PP-OCRv3中英文超轻量检测预训练模型fintune |65.20%|
+| 2 | PP-OCRv3中英文超轻量检测预训练模型fintune学生模型 |80.00%|
+| 3 | PP-OCRv3中英文超轻量检测预训练模型fintune教师模型 |84.80%|
+| 4 | 基于2和3训练好的模型fintune |82.70%|
 
 如需获取已训练模型，请扫码填写问卷，加入PaddleOCR官方交流群获取全部OCR垂类模型下载链接、《动手学OCR》电子书等全套OCR学习资料🎁
 <div align="left">
@@ -445,7 +445,7 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o
 结果如下：
 |   | 方案                        |accuracy|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.4%|
+| 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.40%|
 
 ####  开始训练
 我们使用上面修改好的配置文件configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml，预训练模型，数据集路径，学习率，训练轮数等都已经设置完毕后，可以使用下面命令开始训练。
@@ -465,8 +465,8 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o
 结果如下：
 |   | 方案                        |accuracy|
 |---|---------------------------|---|
-| 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.4%|
-| 1 | PP-OCRv3中英文超轻量识别预训练模型finetune |82.2%|
+| 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.40%|
+| 1 | PP-OCRv3中英文超轻量识别预训练模型finetune |82.20%|
 
 如需获取已训练模型，请扫码填写问卷，加入PaddleOCR官方交流群获取全部OCR垂类模型下载链接、《动手学OCR》电子书等全套OCR学习资料🎁
 <div align="left">

applications/轻量级车牌识别.md

@@ -329,7 +329,7 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \
 |方案|hmeans|
 |---|---|
 |PP-OCRv3中英文超轻量检测预训练模型直接预测|76.12%|
-|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99%|
+|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99.00%|
 
 可以看到进行fine-tune能显著提升车牌检测的效果。
 
@@ -357,8 +357,8 @@ python3.7 deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv3/ch_PP-OCR
 
 |方案|hmeans| 模型大小 | 预测速度(lite) |
 |---|---|------|------------|
-|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99%| 2.5M | 223ms      |
-|PP-OCRv3中英文超轻量检测预训练模型 fine-tune+量化|98.91%| 1M   | 189ms      |
+|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99.00%| 2.5M | 223ms      |
+|PP-OCRv3中英文超轻量检测预训练模型 fine-tune+量化|98.91%| 1.0M   | 189ms      |
 
 可以看到通过量化训练在精度几乎无损的情况下，降低模型体积60%并且推理速度提升15%。
 
@@ -492,7 +492,7 @@ text = text.replace('·','')
 
 |方案|acc|
 |---|---|
-|PP-OCRv3中英文超轻量识别预训练模型直接预测|0.2%|
+|PP-OCRv3中英文超轻量识别预训练模型直接预测|0.20%|
 |PP-OCRv3中英文超轻量识别预训练模型直接预测+后处理去掉多识别的`·`|90.97%|
 
 可以看到，去掉多余的`·`能大幅提高精度。
@@ -547,7 +547,7 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \
 
 |方案| acc    |
 |---|--------|
-|PP-OCRv3中英文超轻量识别预训练模型直接预测| 0%     |
+|PP-OCRv3中英文超轻量识别预训练模型直接预测| 0.00%     |
 |PP-OCRv3中英文超轻量识别预训练模型直接预测+后处理去掉多识别的`·`| 90.97% |
 |PP-OCRv3中英文超轻量识别预训练模型 fine-tune| 94.54% |
 
@@ -578,7 +578,7 @@ python3.7 deploy/slim/quantization/quant.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_
 |方案| acc    | 模型大小  | 预测速度(lite) |
 |---|--------|-------|------------|
 |PP-OCRv3中英文超轻量识别预训练模型 fine-tune| 94.54% | 10.3M | 4.2ms      |
-|PP-OCRv3中英文超轻量识别预训练模型 fine-tune + 量化| 93.4%  | 4.8M  | 1.8ms      |
+|PP-OCRv3中英文超轻量识别预训练模型 fine-tune + 量化| 93.40%  | 4.8M  | 1.8ms      |
 
 可以看到量化后能降低模型体积53%并且推理速度提升57%，但是由于识别数据过少，量化带来了1%的精度下降。
 
@@ -738,7 +738,7 @@ fmeasure: 87.36%
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型| 0.04%  |
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型 + 后处理去掉多识别的`·`| 78.27% |
 |PP-OCRv3中英文超轻量检测预训练模型+fine-tune <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune| 87.14% |
-|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化| 88%    |
+|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化| 88.00%    |
 
 从结果中可以看到对预训练模型不做修改，只根据场景下的具体情况进行后处理的修改就能大幅提升端到端指标到78.27%，在CCPD数据集上进行 fine-tune 后指标进一步提升到87.14%, 在经过量化训练之后，由于检测模型的recall变高，指标进一步提升到88%。但是这个结果仍旧不符合检测模型+识别模型的真实性能(99%*94%=93%)，因此我们需要对 base case 进行具体分析。
 
@@ -763,8 +763,8 @@ if len(txt) != 8: # 车牌字符串长度为8
 |---|---|---|---|---|---|
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型|0.04%|0.08%|0.02%|0.05%|0.00%(A)|
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型 + 后处理去掉多识别的`·`|78.27%|90.84%|78.61%|79.43%|91.66%(A+B+C)|
-|PP-OCRv3中英文超轻量检测预训练模型+fine-tune <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune|87.14%|90.40%|87.66%|89.98|92.5%(A+B+C)|
-|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化|88%|90.54%|88.5%|89.46%|92.02%(A+B+C)|
+|PP-OCRv3中英文超轻量检测预训练模型+fine-tune <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune|87.14%|90.40%|87.66%|89.98%|92.50%(A+B+C)|
+|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化|88.00%|90.54%|88.50%|89.46%|92.02%(A+B+C)|
 
 
 从结果中可以看到对预训练模型不做修改，只根据场景下的具体情况进行后处理的修改就能大幅提升端到端指标到91.66%，在CCPD数据集上进行 fine-tune 后指标进一步提升到92.5%, 在经过量化训练之后，指标变为92.02%。
@@ -800,17 +800,17 @@ python tools/infer/predict_system.py \
 |方案|hmeans| 模型大小 | 预测速度(lite) |
 |---|---|------|------------|
 |PP-OCRv3中英文超轻量检测预训练模型直接预测|76.12%|2.5M| 233ms      |
-|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99%| 2.5M | 233ms      |
-|PP-OCRv3中英文超轻量检测预训练模型 fine-tune + 量化|98.91%| 1M   | 189ms      |fine-tune
+|PP-OCRv3中英文超轻量检测预训练模型 fine-tune|99.00%| 2.5M | 233ms      |
+|PP-OCRv3中英文超轻量检测预训练模型 fine-tune + 量化|98.91%| 1.0M   | 189ms      |fine-tune
 
 - 识别
 
 |方案| acc    | 模型大小  | 预测速度(lite) |
 |---|--------|-------|------------|
-|PP-OCRv3中英文超轻量识别预训练模型直接预测| 0%     |10.3M| 4.2ms      |
+|PP-OCRv3中英文超轻量识别预训练模型直接预测| 0.00%     |10.3M| 4.2ms      |
 |PP-OCRv3中英文超轻量识别预训练模型直接预测+后处理去掉多识别的`·`| 90.97% |10.3M| 4.2ms      |
-|PP-OCRv3中英文超轻量识别预训练模型 fine-tune| 94.54% | 10.3M | 4,2ms      |
-|PP-OCRv3中英文超轻量识别预训练模型 fine-tune + 量化| 93.4%  | 4.8M  | 1.8ms      |
+|PP-OCRv3中英文超轻量识别预训练模型 fine-tune| 94.54% | 10.3M | 4.2ms      |
+|PP-OCRv3中英文超轻量识别预训练模型 fine-tune + 量化| 93.40%  | 4.8M  | 1.8ms      |
 
 
 - 端到端指标如下：
@@ -819,8 +819,8 @@ python tools/infer/predict_system.py \
 |---|---|---|---|
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型|0.08%|12.8M|298ms|
 |PP-OCRv3中英文超轻量检测预训练模型 <br> PP-OCRv3中英文超轻量识别预训练模型 + 后处理去掉多识别的`·`|91.66%|12.8M|298ms|
-|PP-OCRv3中英文超轻量检测预训练模型+fine-tune <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune|92.5%|12.8M|298ms|
-|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化|92.02%|5.8M|224ms|
+|PP-OCRv3中英文超轻量检测预训练模型+fine-tune <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune|92.50%|12.8M|298ms|
+|PP-OCRv3中英文超轻量检测预训练模型+fine-tune+量化 <br> PP-OCRv3中英文超轻量识别预训练模型+fine-tune+量化|92.02%|5.80M|224ms|
 
 
 **结论**

applications/高精度中文识别模型.md

@@ -13,9 +13,9 @@ PP-OCRv3是百度开源的超轻量级场景文本检测识别模型库，其中
 
 |中文识别算法|模型|UIM|精度|
 | --- | --- | --- |--- |
-|PP-OCRv3|SVTR_LCNet| w/o |78.4%|
-|PP-OCRv3|SVTR_LCNet| w |79.4%|
-|SVTR|SVTR-Tiny|-|82.5%|
+|PP-OCRv3|SVTR_LCNet| w/o |78.40%|
+|PP-OCRv3|SVTR_LCNet| w |79.40%|
+|SVTR|SVTR-Tiny|-|82.50%|
 
 aistudio项目链接: [高精度中文场景文本识别模型SVTR](https://aistudio.baidu.com/aistudio/projectdetail/4263032)
 

doc/doc_ch/PP-OCRv3_introduction.md

@@ -53,13 +53,13 @@ PP-OCRv3检测模型是对PP-OCRv2中的[CML](https://arxiv.org/pdf/2109.03144.p
 
 |序号|策略|模型大小|hmean|速度（cpu + mkldnn)|
 |-|-|-|-|-|
-|baseline teacher|PP-OCR server|49M|83.2%|171ms|
-|teacher1|DB-R50-LK-PAN|124M|85.0%|396ms|
-|teacher2|DB-R50-LK-PAN-DML|124M|86.0%|396ms|
-|baseline student|PP-OCRv2|3M|83.2%|117ms|
-|student0|DB-MV3-RSE-FPN|3.6M|84.5%|124ms|
-|student1|DB-MV3-CML（teacher2）|3M|84.3%|117ms|
-|student2|DB-MV3-RSE-FPN-CML（teacher2）|3.6M|85.4%|124ms|
+|baseline teacher|PP-OCR server|49.0M|83.20%|171ms|
+|teacher1|DB-R50-LK-PAN|124.0M|85.00%|396ms|
+|teacher2|DB-R50-LK-PAN-DML|124.0M|86.00%|396ms|
+|baseline student|PP-OCRv2|3.0M|83.20%|117ms|
+|student0|DB-MV3-RSE-FPN|3.6M|84.50%|124ms|
+|student1|DB-MV3-CML（teacher2）|3.0M|84.30%|117ms|
+|student2|DB-MV3-RSE-FPN-CML（teacher2）|3.60M|85.40%|124ms|
 
 测试环境： Intel Gold 6148 CPU，预测时开启MKLDNN加速。
 
@@ -101,15 +101,15 @@ PP-OCRv3的识别模块是基于文本识别算法[SVTR](https://arxiv.org/abs/2
 
 | ID | 策略 |  模型大小 | 精度 | 预测耗时（CPU + MKLDNN)|
 |-----|-----|--------|----| --- |
-| 01 | PP-OCRv2 | 8M | 74.8% | 8.54ms |
-| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
-| 03 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
-| 04 | SVTR_LCNet(h48) | 12M | 73.98% | 7.6ms |
-| 05 | + GTC | 12M | 75.8% | 7.6ms |
-| 06 | + TextConAug | 12M | 76.3% | 7.6ms |
-| 07 | + TextRotNet | 12M | 76.9% | 7.6ms |
-| 08 | + UDML | 12M | 78.4% | 7.6ms |
-| 09 | + UIM | 12M | 79.4% | 7.6ms |
+| 01 | PP-OCRv2 | 8.0M | 74.80% | 8.54ms |
+| 02 | SVTR_Tiny | 21.0M | 80.10% | 97.00ms |
+| 03 | SVTR_LCNet(h32) | 12.0M | 71.90% | 6.60ms |
+| 04 | SVTR_LCNet(h48) | 12.0M | 73.98% | 7.60ms |
+| 05 | + GTC | 12.0M | 75.80% | 7.60ms |
+| 06 | + TextConAug | 12.0M | 76.30% | 7.60ms |
+| 07 | + TextRotNet | 12.0M | 76.90% | 7.60ms |
+| 08 | + UDML | 12.0M | 78.40% | 7.60ms |
+| 09 | + UIM | 12.0M | 79.40% | 7.60ms |
 
 注： 测试速度时，实验01-03输入图片尺寸均为(3,32,320)，04-08输入图片尺寸均为(3,48,320)。在实际预测时，图像为变长输入，速度会有所变化。测试环境： Intel Gold 6148 CPU，预测时开启MKLDNN加速。
 
@@ -144,12 +144,12 @@ SVTR_Tiny 网络结构如下所示：
 
 | ID | 策略 |  模型大小 | 精度 | 速度（CPU + MKLDNN)|
 |-----|-----|--------|----| --- |
-| 01 | PP-OCRv2-baseline | 8M | 69.3%  | 8.54ms |
-| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
-| 03 | SVTR_LCNet(G4) | 9.2M | 76% | 30ms |
-| 04 | SVTR_LCNet(G2) | 13M | 72.98% | 9.37ms |
-| 05 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
-| 06 | SVTR_LCNet(h48)  | 12M | 73.98% | 7.6ms |
+| 01 | PP-OCRv2-baseline | 8.0M | 69.30%  | 8.54ms |
+| 02 | SVTR_Tiny | 21.0M | 80.10% | 97.00ms |
+| 03 | SVTR_LCNet(G4) | 9.2M | 76.00% | 30.00ms |
+| 04 | SVTR_LCNet(G2) | 13.0M | 72.98% | 9.37ms |
+| 05 | SVTR_LCNet(h32) | 12.0M | 71.90% | 6.60ms |
+| 06 | SVTR_LCNet(h48)  | 12.0M | 73.98% | 7.60ms |
 
 注： 测试速度时，01-05输入图片尺寸均为(3,32,320)； PP-OCRv2-baseline 代表没有借助蒸馏方法训练得到的模型
 
@@ -199,10 +199,10 @@ UIM（Unlabeled Images Mining）是一种非常简单的无标注数据挖掘方
 
 | Model | Hmean |  Model Size (M) | Time Cost (CPU, ms) | Time Cost (T4 GPU, ms) |
 |-----|-----|--------|----| --- |
-| PP-OCR mobile | 50.3% | 8.1 | 356  | 116 |
-| PP-OCR server | 57.0% | 155.1 | 1056 | 200 |
-| PP-OCRv2 | 57.6% | 11.6 | 330 | 111 |
-| PP-OCRv3 | 62.9% | 15.6 | 331 | 86.64 |
+| PP-OCR mobile | 50.30% | 8.1 | 356.00  | 116.00 |
+| PP-OCR server | 57.00% | 155.1 | 1056.00 | 200.00 |
+| PP-OCRv2 | 57.60% | 11.6 | 330.00 | 111.00 |
+| PP-OCRv3 | 62.90% | 15.6 | 331.00 | 86.64 |
 
 测试环境：CPU型号为Intel Gold 6148，CPU预测时开启MKLDNN加速。
 
@@ -218,5 +218,5 @@ UIM（Unlabeled Images Mining）是一种非常简单的无标注数据挖掘方
 
 | Model | 拉丁语系 |  阿拉伯语系 | 日语 | 韩语 |
 |-----|-----|--------|----| --- |
-| PP-OCR_mul | 69.6% | 40.5% | 38.5%  | 55.4% |
-| PP-OCRv3_mul | 75.2%| 45.37% | 45.8% | 60.1% |
+| PP-OCR_mul | 69.60% | 40.50% | 38.50%  | 55.40% |
+| PP-OCRv3_mul | 75.20%| 45.37% | 45.80% | 60.10% |

doc/doc_ch/algorithm_det_east.md

@@ -27,7 +27,7 @@
 |模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|88.71%|    81.36%|    84.88%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|    MobileNetV3| 78.2%|    79.1%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|    MobileNetV3| 78.20%|    79.10%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_e2e_pgnet.md

@@ -34,7 +34,7 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|下载|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
+|Paper|85.30|86.80|86.10|-|-|61.70|38.20 (size=640)|-|
 |Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
 
 *note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度*

doc/doc_ch/algorithm_kie_sdmgr.md

@@ -33,7 +33,7 @@
 
 |模型|骨干网络|配置文件|hmean|下载链接|
 | --- | --- | --- | --- | --- |
-|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[训练模型]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[推理模型(coming soon)]()|
+|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[训练模型]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[推理模型(coming soon)]()|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_overview.md

@@ -36,7 +36,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|88.71%|81.36%|84.88%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|MobileNetV3|78.2%|79.1%|78.65%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
+|EAST|MobileNetV3|78.20%|79.10%|78.65%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
@@ -143,7 +143,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 
 |模型|骨干网络|配置文件|hmean|下载链接|
 | --- | --- | --- | --- | --- |
-|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
 
 
 在XFUND_zh公开数据集上，算法效果如下：

doc/doc_ch/algorithm_rec_can.md

@@ -27,7 +27,7 @@
 
 |模型    |骨干网络|配置文件|ExpRate|下载链接|
 | ----- | ----- | ----- | ----- | ----- |
-|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
 
 <a name="2"></a>
 ## 2. 环境配置

doc/doc_ch/algorithm_rec_rare.md

@@ -25,8 +25,8 @@
 
 |模型|骨干网络|配置文件|Avg Accuracy|下载链接|
 | --- | --- | --- | --- | --- |
-|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.6%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
-|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.5%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.60%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
+|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.50%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_rec_seed.md

@@ -27,7 +27,7 @@
 
 |模型|骨干网络|Avg Accuracy|配置文件|下载链接|
 |---|---|---|---|---|
-|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
+|SEED|Aster_Resnet| 85.20% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
 
 <a name="2"></a>
 ## 2. 环境配置

doc/doc_ch/algorithm_rec_spin.md

@@ -26,7 +26,7 @@ SPIN收录于AAAI2020。主要用于OCR识别任务。在任意形状文本识
 
 |模型|骨干网络|配置文件|Acc|下载链接|
 | --- | --- | --- | --- | --- |
-|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar)|
+|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.00%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_rec_visionlan.md

@@ -27,7 +27,7 @@
 
 |模型|骨干网络|配置文件|Acc|下载链接|
 | --- | --- | --- | --- | --- |
-|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
+|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.30%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
 
 <a name="2"></a>
 ## 2. 环境配置

doc/doc_ch/models_list.md

@@ -42,12 +42,12 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 |模型名称|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- | --- |
 |ch_PP-OCRv3_det_slim|【最新】slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
-|ch_PP-OCRv3_det| 【最新】原始超轻量模型，支持中英文、多语种文本检测 |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)|
-|ch_PP-OCRv2_det_slim| slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
-|ch_PP-OCRv2_det| 原始超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
-|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 2.6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
-|ch_ppocr_mobile_v2.0_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
-|ch_ppocr_server_v2.0_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
+|ch_PP-OCRv3_det| 【最新】原始超轻量模型，支持中英文、多语种文本检测 |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.80M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)|
+|ch_PP-OCRv2_det_slim| slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3.0M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
+|ch_PP-OCRv2_det| 原始超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3.0M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
+|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 2.60M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
+|ch_ppocr_mobile_v2.0_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3.0M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
+|ch_ppocr_server_v2.0_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47.0M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
 
 <a name="1.2"></a>
 
@@ -83,10 +83,10 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 | --- | --- | --- | --- | --- |
 |ch_PP-OCRv3_rec_slim |【最新】slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
 |ch_PP-OCRv3_rec|【最新】原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 12.4M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
-|ch_PP-OCRv2_rec_slim| slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
-|ch_PP-OCRv2_rec| 原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
-|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
-|ch_ppocr_mobile_v2.0_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
+|ch_PP-OCRv2_rec_slim| slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9.0M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
+|ch_PP-OCRv2_rec| 原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.50M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
+|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6.0M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
+|ch_ppocr_mobile_v2.0_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.20M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
 |ch_ppocr_server_v2.0_rec|通用模型，支持中英文、数字识别|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
 
 **说明：** `训练模型`是基于预训练模型在真实数据与竖排合成文本数据上finetune得到的模型，在真实应用场景中有着更好的表现，`预训练模型`则是直接基于全量真实数据与合成数据训练得到，更适合用于在自己的数据集上finetune。
@@ -107,9 +107,9 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 
 |模型名称|字典文件|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- |--- | --- |
-| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_train.tar) |
-| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_train.tar) |
-| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
+| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11.0M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_train.tar) |
+| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11.0M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_train.tar) |
+| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12.0M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
 | te_PP-OCRv3_rec | ppocr/utils/dict/te_dict.txt | 泰卢固文识别|[te_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/te_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_train.tar) |
 | ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) |
 | ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) |
@@ -140,9 +140,9 @@ Paddle-Lite 是一个高性能、轻量级、灵活性强且易于扩展的深
 
 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
 |---|---|---|---|---|---|---|
-|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11.0M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
 |PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
-|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11.0M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
 |PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
 |V2.0|ppocr_v2.0超轻量中文OCR移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
 |V2.0(slim)|ppocr_v2.0超轻量中文OCR移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|

doc/doc_en/PP-OCRv3_introduction_en.md

@@ -55,13 +55,13 @@ The ablation experiments are as follows:
 
 |ID|Strategy|Model Size|Hmean|The Inference Time（cpu + mkldnn)|
 |-|-|-|-|-|
-|baseline teacher|PP-OCR server|49M|83.2%|171ms|
-|teacher1|DB-R50-LK-PAN|124M|85.0%|396ms|
-|teacher2|DB-R50-LK-PAN-DML|124M|86.0%|396ms|
-|baseline student|PP-OCRv2|3M|83.2%|117ms|
-|student0|DB-MV3-RSE-FPN|3.6M|84.5%|124ms|
-|student1|DB-MV3-CML（teacher2）|3M|84.3%|117ms|
-|student2|DB-MV3-RSE-FPN-CML（teacher2）|3.6M|85.4%|124ms|
+|baseline teacher|PP-OCR server|49.0M|83.20%|171ms|
+|teacher1|DB-R50-LK-PAN|124.0M|85.00%|396ms|
+|teacher2|DB-R50-LK-PAN-DML|124.0M|86.00%|396ms|
+|baseline student|PP-OCRv2|3.0M|83.20%|117ms|
+|student0|DB-MV3-RSE-FPN|3.6M|84.50%|124ms|
+|student1|DB-MV3-CML（teacher2）|3.0M|84.30%|117ms|
+|student2|DB-MV3-RSE-FPN-CML（teacher2）|3.6M|85.40%|124ms|
 
 Testing environment: Intel Gold 6148 CPU, with MKLDNN acceleration enabled during inference.
 
@@ -111,15 +111,15 @@ Based on the above strategy, compared with PP-OCRv2, the PP-OCRv3 recognition mo
 
 | ID | strategy |  Model size | accuracy | prediction speed（CPU + MKLDNN)|
 |-----|-----|--------|----| --- |
-| 01 | PP-OCRv2 | 8M | 74.8% | 8.54ms |
-| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
-| 03 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
-| 04 | SVTR_LCNet(h48) | 12M | 73.98% | 7.6ms |
-| 05 | + GTC | 12M | 75.8% | 7.6ms |
-| 06 | + TextConAug | 12M | 76.3% | 7.6ms |
-| 07 | + TextRotNet | 12M | 76.9% | 7.6ms |
-| 08 | + UDML | 12M | 78.4% | 7.6ms |
-| 09 | + UIM | 12M | 79.4% | 7.6ms |
+| 01 | PP-OCRv2 | 8.0M | 74.80% | 8.54ms |
+| 02 | SVTR_Tiny | 21.0M | 80.10% | 97.00ms |
+| 03 | SVTR_LCNet(h32) | 12.0M | 71.90% | 6.60ms |
+| 04 | SVTR_LCNet(h48) | 12.0M | 73.98% | 7.60ms |
+| 05 | + GTC | 12.0M | 75.80% | 7.60ms |
+| 06 | + TextConAug | 12.0M | 76.30% | 7.60ms |
+| 07 | + TextRotNet | 12.0M | 76.90% | 7.60ms |
+| 08 | + UDML | 12.0M | 78.40% | 7.60ms |
+| 09 | + UIM | 12.0M | 79.40% | 7.60ms |
 
 Note: When testing the speed, the input image shape of Experiment 01-03 is (3, 32, 320), and the input image shape of 04-08 is (3, 48, 320). In the actual prediction, the image is a variable-length input, and the speed will vary. Testing environment: Intel Gold 6148 CPU, with MKLDNN acceleration enabled during prediction.
 
@@ -158,12 +158,12 @@ The ablation experiments are as follows:
 
 | ID | strategy |  Model size | accuracy | prediction speed（CPU + MKLDNN)|
 |-----|-----|--------|----| --- |
-| 01 | PP-OCRv2-baseline | 8M | 69.3%  | 8.54ms |
-| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
-| 03 | SVTR_LCNet(G4) | 9.2M | 76% | 30ms |
-| 04 | SVTR_LCNet(G2) | 13M | 72.98% | 9.37ms |
-| 05 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
-| 06 | SVTR_LCNet(h48)  | 12M | 73.98% | 7.6ms |
+| 01 | PP-OCRv2-baseline | 8.0M | 69.30%  | 8.54ms |
+| 02 | SVTR_Tiny | 21.0M | 80.10% | 97.00ms |
+| 03 | SVTR_LCNet(G4) | 9.2M | 76.00% | 30.00ms |
+| 04 | SVTR_LCNet(G2) | 13.0M | 72.98% | 9.37ms |
+| 05 | SVTR_LCNet(h32) | 12.0M | 71.90% | 6.60ms |
+| 06 | SVTR_LCNet(h48)  | 12.0M | 73.98% | 7.60ms |
 
 Note: When testing the speed, the input image shape of 01-05 are all (3, 32, 320); PP-OCRv2-baseline represents the model trained without distillation method
 
@@ -210,21 +210,21 @@ UIM (Unlabeled Images Mining) is a very simple unlabeled data mining strategy. T
 
 ## 4. End-to-end Evaluation
 
-With the optimization strategies mentioned above, PP-OCRv3 outperforms PP-OCRv2 by 5% in terms of end-to-end Hmean for Chinese scenarios with comparable speed. The specific metrics are shown as follows.
+With the optimization strategies mentioned above, PP-OCRv3 outperforms PP-OCRv2 by 5.00% in terms of end-to-end Hmean for Chinese scenarios with comparable speed. The specific metrics are shown as follows.
 
 | Model | Hmean |  Model Size (M) | Time Cost (CPU, ms) | Time Cost (T4 GPU, ms) |
 |-----|-----|--------|----| --- |
-| PP-OCR mobile | 50.3% | 8.1 | 356  | 116 |
-| PP-OCR server | 57.0% | 155.1 | 1056 | 200 |
-| PP-OCRv2 | 57.6% | 11.6 | 330 | 111 |
-| PP-OCRv3 | 62.9% | 15.6 | 331 | 86.64 |
+| PP-OCR mobile | 50.30% | 8.1 | 356.00  | 116.00 |
+| PP-OCR server | 57.00% | 155.1 | 1056.00 | 200.00 |
+| PP-OCRv2 | 57.60% | 11.6 | 330.00 | 111.00 |
+| PP-OCRv3 | 62.90% | 15.6 | 331.00 | 86.64 |
 
 
 Testing environment:
 - CPU: Intel Gold 6148, and MKLDNN acceleration is enabled during CPU inference.
 
 
-In addition to Chinese scenarios, the recognition model for English is also optimized with an increasement of 11% for end-to-end Hmean, which is shown as follows.
+In addition to Chinese scenarios, the recognition model for English is also optimized with an increasement of 11.00% for end-to-end Hmean, which is shown as follows.
 
 | Model | Recall |  Precision | Hmean |
 |-----|-----|--------|----|
@@ -235,5 +235,5 @@ At the same time, recognition models for more than 80 language are also upgraded
 
 | Model | Latin | Arabic | Japanese | Korean |
 |-----|-----|--------|----| --- |
-| PP-OCR_mul | 69.6% | 40.5% | 38.5% | 55.4% |
-| PP-OCRv3_mul | 75.2% | 45.37% | 45.8% | 60.1% |
+| PP-OCR_mul | 69.60% | 40.50% | 38.50% | 55.40% |
+| PP-OCRv3_mul | 75.20% | 45.37% | 45.80% | 60.10% |

doc/doc_en/algorithm_det_east_en.md

@@ -27,7 +27,7 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
 | --- | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|88.71%|    81.36%|    84.88%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|    MobileNetV3| 78.2%|    79.1%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|    MobileNetV3| 78.20%|    79.10%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_en/algorithm_e2e_pgnet_en.md

@@ -29,7 +29,7 @@ The results of detection and recognition are as follows:
 #### Test environment: NVIDIA Tesla V100-SXM2-16GB
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|download|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
+|Paper|85.30|86.80|86.10|-|-|61.70|38.20 (size=640)|-|
 |Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
 
 *note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*

doc/doc_en/algorithm_kie_sdmgr_en.md

@@ -26,7 +26,7 @@ On wildreceipt dataset, the algorithm reproduction Hmean is as follows.
 
 |Model|Backbone |Cnnfig|Hmean|Download link|
 | --- | --- | --- | --- | --- |
-|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[trained model]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[inference model(coming soon)]()|
+|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[trained model]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[inference model(coming soon)]()|
 
 
 

doc/doc_en/algorithm_overview_en.md

@@ -34,7 +34,7 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |Model|Backbone|Precision|Recall|Hmean|Download link|
 | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|88.71%|81.36%|84.88%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|MobileNetV3|78.2%|79.1%|78.65%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
+|EAST|MobileNetV3|78.20%|79.10%|78.65%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
@@ -141,7 +141,7 @@ On wildreceipt dataset, the algorithm result is as follows:
 
 |Model|Backbone|Config|Hmean|Download link|
 | --- | --- | --- | --- | --- |
-|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
 
 On XFUND_zh dataset, the algorithm result is as follows:
 

doc/doc_en/algorithm_rec_can_en.md

@@ -25,7 +25,7 @@ Using CROHME handwrittem mathematical expression recognition datasets for traini
 
 |Model|Backbone|config|exprate|Download link|
 | --- | --- | --- | --- | --- |
-|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar)|
 
 <a name="2"></a>
 ## 2. Environment

doc/doc_en/algorithm_rec_rare_en.md

@@ -25,8 +25,8 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
 
 |Models|Backbone Networks|Configuration Files|Avg Accuracy|Download Links|
 | --- | --- | --- | --- | --- |
-|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.6%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
-|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.5%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|[configs/rec/rec_r34_vd_tps_bilstm_att.yml](../../configs/rec/rec_r34_vd_tps_bilstm_att.yml)|83.60%|[training model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
+|RARE|MobileNetV3|[configs/rec/rec_mv3_tps_bilstm_att.yml](../../configs/rec/rec_mv3_tps_bilstm_att.yml)|82.50%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_en/algorithm_rec_seed_en.md

@@ -27,7 +27,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
 
 |Model|Backbone|ACC|config|Download link|
 | --- | --- | --- | --- | --- |
-|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
+|SEED|Aster_Resnet| 85.20% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
 
 <a name="2"></a>
 ## 2. Environment

doc/doc_en/algorithm_rec_spin_en.md

@@ -25,7 +25,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
 
 |Model|Backbone|config|Acc|Download link|
 | --- | --- | --- | --- | --- |
-|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
+|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.00%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
 
 
 <a name="2"></a>

doc/doc_en/algorithm_rec_visionlan_en.md

@@ -25,7 +25,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
 
 |Model|Backbone|config|Acc|Download link|
 | --- | --- | --- | --- | --- |
-|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
+|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.30%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
 
 <a name="2"></a>
 ## 2. Environment

doc/doc_en/distributed_training_en.md

@@ -47,14 +47,14 @@ python3 -m paddle.distributed.launch \
 
 | Model    | Configuration | Configuration   | 8 GPU training time / Accuracy | 3x8 GPU training time / Accuracy | Acceleration ratio  |
 |:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
-| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) |  260k Chinese dataset | 2.50d/66.7%   | 1.67d/67.0%  | **1.5** |
+| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) |  260k Chinese dataset | 2.50d/66.70%   | 1.67d/67.00%  | **1.5** |
 
 
 * We conducted model training on 3x8 V100 GPUs. Accuracy, training time, and multi machine acceleration ratio of different models are shown below.
 
 | Model    | Configuration | Configuration   | 8 GPU training time / Accuracy | 3x8 GPU training time / Accuracy | Acceleration ratio  |
 |:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
-| SLANet | [SLANet.yml](../../configs/table/SLANet.yml) |  PubTabNet | 49.8h/76.2%   | 19.75h/74.77%  | **2.52** |
+| SLANet | [SLANet.yml](../../configs/table/SLANet.yml) |  PubTabNet | 49.80h/76.20%   | 19.75h/74.77%  | **2.52** |
 
 
     > Note: when training with 3x8 GPUs, the single card batch size is unchanged compared with the 1x8 GPUs' training process, and the learning rate is multiplied by 2 (if it is multiplied by 3 by default, the accuracy is only 73.42%).
@@ -65,4 +65,4 @@ python3 -m paddle.distributed.launch \
 
 | Model    | Configuration | Configuration   | 8 GPU training time / Accuracy | 4x8 GPU training time / Accuracy | Acceleration ratio  |
 |:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
-| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) |  PP-OCRv3_rec data | 10d/-   | 2.84d/74.0%  | **3.5** |
+| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) |  PP-OCRv3_rec data | 10d/-   | 2.84d/74.00%  | **3.5** |

doc/doc_en/models_list_en.md

@@ -39,11 +39,11 @@ Relationship of the above models is as follows.
 | --- | --- | --- | --- | --- |
 |ch_PP-OCRv3_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
 |ch_PP-OCRv3_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)|
-|ch_PP-OCRv2_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
-|ch_PP-OCRv2_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
+|ch_PP-OCRv2_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3.0M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
+|ch_PP-OCRv2_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3.0M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
 |ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|2.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
-|ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
-|ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
+|ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3.0M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
+|ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47.0M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
 
 <a name="1.2"></a>
 
@@ -77,9 +77,9 @@ Relationship of the above models is as follows.
 | --- | --- | --- | --- | --- |
 |ch_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
 |ch_PP-OCRv3_rec| [New] Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 12.4M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
-|ch_PP-OCRv2_rec_slim| Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
+|ch_PP-OCRv2_rec_slim| Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9.0M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
 |ch_PP-OCRv2_rec| Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
-|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
+|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6.0M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
 |ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
 |ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
 
@@ -101,9 +101,9 @@ Relationship of the above models is as follows.
 
 |model name| dict file | description|config|model size|download|
 | --- | --- | --- |--- | --- | --- |
-| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |Lightweight model for Korean recognition|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_train.tar) |
-| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |Lightweight model for Japanese recognition|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_train.tar) |
-| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
+| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |Lightweight model for Korean recognition|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11.0M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_train.tar) |
+| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |Lightweight model for Japanese recognition|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11.0M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_train.tar) |
+| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12.0M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
 | te_PP-OCRv3_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition |[te_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/te_PP-OCRv3_rec.yml)|9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_train.tar) |
 | ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition |[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) |
 | ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |Lightweight model for Tamil recognition|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) |
@@ -131,9 +131,9 @@ This chapter lists OCR nb models with PP-OCRv2 or earlier versions. You can acce
 
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
-|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2|extra-lightweight chinese OCR optimized model|11.0M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
 |PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
-|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
+|PP-OCRv2|extra-lightweight chinese OCR optimized model|11.0M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
 |PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
 |V2.0|ppocr_v2.0 extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
 |V2.0(slim)|ppovr_v2.0 extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|

paddleocr.py

@@ -53,8 +53,8 @@ BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
 DEFAULT_OCR_MODEL_VERSION = 'PP-OCRv3'
 SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2', 'PP-OCRv3']
-DEFAULT_STRUCTURE_MODEL_VERSION = 'PP-Structurev2'
-SUPPORT_STRUCTURE_MODEL_VERSION = ['PP-Structure', 'PP-Structurev2']
+DEFAULT_STRUCTURE_MODEL_VERSION = 'PP-StructureV2'
+SUPPORT_STRUCTURE_MODEL_VERSION = ['PP-Structure', 'PP-StructureV2']
 MODEL_URLS = {
     'OCR': {
         'PP-OCRv3': {
@@ -272,7 +272,7 @@ MODEL_URLS = {
                 }
             }
         },
-        'PP-Structurev2': {
+        'PP-StructureV2': {
             'table': {
                 'en': {
                     'url':
@@ -326,10 +326,10 @@ def parse_args(mMain=True):
         "--structure_version",
         type=str,
         choices=SUPPORT_STRUCTURE_MODEL_VERSION,
-        default='PP-Structurev2',
+        default='PP-StructureV2',
         help='Model version, the current model support list is as follows:'
         ' 1. PP-Structure Support en table structure model.'
-        ' 2. PP-Structurev2 Support ch and en table structure model.')
+        ' 2. PP-StructureV2 Support ch and en table structure model.')
 
     for action in parser._actions:
         if action.dest in [

ppstructure/docs/PP-StructureV2_introduction.md

@@ -16,11 +16,11 @@
 
 现实场景中包含大量的文档图像，它们以图片等非结构化形式存储。基于文档图像的结构化分析与信息抽取对于数据的数字化存储以及产业的数字化转型至关重要。基于该考虑，PaddleOCR自研并发布了PP-Structure智能文档分析系统，旨在帮助开发者更好的完成版面分析、表格识别、关键信息抽取等文档理解相关任务。
 
-近期，PaddleOCR团队针对PP-Structurev1的版面分析、表格识别、关键信息抽取模块，进行了共计8个方面的升级，同时新增整图方向矫正、文档复原等功能，打造出一个全新的、效果更优的文档分析系统：PP-StructureV2。
+近期，PaddleOCR团队针对PP-StructureV1的版面分析、表格识别、关键信息抽取模块，进行了共计8个方面的升级，同时新增整图方向矫正、文档复原等功能，打造出一个全新的、效果更优的文档分析系统：PP-StructureV2。
 
 ## 2. 简介
 
-PP-StructureV2在PP-Structurev1的基础上进一步改进，主要有以下3个方面升级：
+PP-StructureV2在PP-StructureV1的基础上进一步改进，主要有以下3个方面升级：
 
  * **系统功能升级** ：新增图像矫正和版面复原模块，图像转word/pdf、关键信息抽取能力全覆盖！
  * **系统性能优化** ：
@@ -52,7 +52,7 @@ PP-StructureV2系统流程图如下所示，文档图像首先经过图像矫正
 	* TB-YX：考虑阅读顺序的文本行排序逻辑
 	* UDML：联合互学习知识蒸馏策略
 
-最终，与PP-Structurev1相比：
+最终，与PP-StructureV1相比：
 
 - 版面分析模型参数量减少95.6%，推理速度提升11倍，精度提升0.4%；
 - 表格识别预测耗时不变，模型精度提升6%，端到端TEDS提升2%；
@@ -74,17 +74,17 @@ PP-StructureV2系统流程图如下所示，文档图像首先经过图像矫正
 
 ### 4.1 版面分析
 
-版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等，PP-Structurev1使用了PaddleDetection中开源的高效检测算法PP-YOLOv2完成版面分析的任务。
+版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等，PP-StructureV1使用了PaddleDetection中开源的高效检测算法PP-YOLOv2完成版面分析的任务。
 
 在PP-StructureV2中，我们发布基于PP-PicoDet的轻量级版面分析模型，并针对版面分析场景定制图像尺度，同时使用FGD知识蒸馏算法，进一步提升模型精度。最终CPU上`41ms`即可完成版面分析过程(仅包含模型推理时间，数据预处理耗时大约50ms左右)。在公开数据集PubLayNet 上，消融实验如下：
 
 | 实验序号 | 策略                          | 模型存储(M) | mAP     | CPU预测耗时(ms) |
 |:------:|:------:|:------:|:------:|:------:|
-| 1    |  PP-YOLOv2(640*640)  |  221  | 93.6% |  512  |
-| 2    | PP-PicoDet-LCNet2.5x(640*640) |  29.7 | 92.5% |53.2|
-| 3    | PP-PicoDet-LCNet2.5x(800*608) |   29.7  | 94.2% |83.1 |
-| 4    | PP-PicoDet-LCNet1.0x(800*608) |    9.7  | 93.5% | 41.2|
-| 5    | PP-PicoDet-LCNet1.0x(800*608) + FGD |  9.7  | 94% |41.2|
+| 1    |  PP-YOLOv2(640*640)  |  221.0  | 93.60% |  512.00  |
+| 2    | PP-PicoDet-LCNet2.5x(640*640) |  29.7 | 92.50% |53.20|
+| 3    | PP-PicoDet-LCNet2.5x(800*608) |   29.7  | 94.20% |83.10 |
+| 4    | PP-PicoDet-LCNet1.0x(800*608) |    9.7  | 93.50% | 41.20|
+| 5    | PP-PicoDet-LCNet1.0x(800*608) + FGD |  9.7  | 94.00% |41.20|
 
 * 测试条件
 	* paddle版本：2.3.0
@@ -94,8 +94,8 @@ PP-StructureV2系统流程图如下所示，文档图像首先经过图像矫正
 
 | 模型                | mAP | CPU预测耗时   |
 |-------------------|-----------|------------|
-| layoutparser (Detectron2)   | 88.98%    | 2.9s    |
-| PP-StructureV2 (PP-PicoDet) | **94%**    |   41.2ms   |
+| layoutparser (Detectron2)   | 88.98%    | 2.90s    |
+| PP-StructureV2 (PP-PicoDet) | **94.00%**    |   41.20ms   |
 
 [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)数据集是一个大型的文档图像数据集，包含Text、Title、Tale、Figure、List，共5个类别。数据集中包含335,703张训练集、11,245张验证集和11,405张测试集。训练数据与标注示例图如下所示：
 
@@ -108,7 +108,7 @@ PP-StructureV2系统流程图如下所示，文档图像首先经过图像矫正
 
 **（1）轻量级版面分析模型PP-PicoDet**
 
-`PP-PicoDet`是PaddleDetection中提出的轻量级目标检测模型，通过使用PP-LCNet骨干网络、CSP-PAN特征融合模块、SimOTA标签分配方法等优化策略，最终在CPU与移动端具有卓越的性能。我们将PP-Structurev1中采用的PP-YOLOv2模型替换为`PP-PicoDet`，同时针对版面分析场景优化预测尺度，从针对目标检测设计的`640*640`调整为更适配文档图像的`800*608`，在`1.0x`配置下，模型精度与PP-YOLOv2相当，CPU平均预测速度可提升11倍。
+`PP-PicoDet`是PaddleDetection中提出的轻量级目标检测模型，通过使用PP-LCNet骨干网络、CSP-PAN特征融合模块、SimOTA标签分配方法等优化策略，最终在CPU与移动端具有卓越的性能。我们将PP-StructureV1中采用的PP-YOLOv2模型替换为`PP-PicoDet`，同时针对版面分析场景优化预测尺度，从针对目标检测设计的`640*640`调整为更适配文档图像的`800*608`，在`1.0x`配置下，模型精度与PP-YOLOv2相当，CPU平均预测速度可提升11倍。
 
 **（1）FGD知识蒸馏**
 
@@ -130,10 +130,10 @@ FGD（Focal and Global Knowledge Distillation for Detectors），是一种兼顾
 
 | 实验序号 | 策略             |  mAP     |
 |:------:|:------:|:------:|
-| 1    |  PP-YOLOv2 |  84.7% |
-| 2    |  PP-PicoDet-LCNet2.5x(800*608) |  87.8% |
-| 3    |  PP-PicoDet-LCNet1.0x(800*608) | 84.5% |
-| 4    |  PP-PicoDet-LCNet1.0x(800*608) + FGD |  86.8% |
+| 1    |  PP-YOLOv2 |  84.70% |
+| 2    |  PP-PicoDet-LCNet2.5x(800*608) |  87.80% |
+| 3    |  PP-PicoDet-LCNet1.0x(800*608) | 84.50% |
+| 4    |  PP-PicoDet-LCNet1.0x(800*608) + FGD |  86.80% |
 
 
 **（2）表格版面分析**
@@ -144,10 +144,10 @@ FGD（Focal and Global Knowledge Distillation for Detectors），是一种兼顾
 
 | 实验序号 | 策略            |  mAP     |
 |:------:|:------:|:------:|
-| 1    |  PP-YOLOv2  |91.3% |
-| 2    |  PP-PicoDet-LCNet2.5x(800*608) |  95.9% |
-| 3    |  PP-PicoDet-LCNet1.0x(800*608) |   95.2% |
-| 4    |  PP-PicoDet-LCNet1.0x(800*608) + FGD |  95.7% |
+| 1    |  PP-YOLOv2  |91.30% |
+| 2    |  PP-PicoDet-LCNet2.5x(800*608) |  95.90% |
+| 3    |  PP-PicoDet-LCNet1.0x(800*608) |   95.20% |
+| 4    |  PP-PicoDet-LCNet1.0x(800*608) + FGD |  95.70% |
 
 表格检测效果示意图如下：
 
@@ -157,7 +157,7 @@ FGD（Focal and Global Knowledge Distillation for Detectors），是一种兼顾
 
 ### 4.2 表格识别
 
-基于深度学习的表格识别算法种类丰富，PP-Structurev1中，我们基于文本识别算法RARE研发了端到端表格识别算法TableRec-RARE，模型输出为表格结构的HTML表示，进而可以方便地转化为Excel文件。PP-StructureV2中，我们对模型结构和损失函数等5个方面进行升级，提出了 SLANet (Structure Location Alignment Network) ，模型结构如下图所示：
+基于深度学习的表格识别算法种类丰富，PP-StructureV1中，我们基于文本识别算法RARE研发了端到端表格识别算法TableRec-RARE，模型输出为表格结构的HTML表示，进而可以方便地转化为Excel文件。PP-StructureV2中，我们对模型结构和损失函数等5个方面进行升级，提出了 SLANet (Structure Location Alignment Network) ，模型结构如下图所示：
 
 <div align="center">
     <img src="https://user-images.githubusercontent.com/14270174/185940811-089c9265-4be9-4776-b365-6d1125606b4b.png" width="1200">
@@ -170,7 +170,7 @@ FGD（Focal and Global Knowledge Distillation for Detectors），是一种兼顾
 |TableRec-RARE|	71.73% | 93.88% |779ms	|6.8M|
 |+PP-LCNet|	74.71% |94.37%	|778ms|	8.7M|
 |+CSP-PAN|	75.68%| 94.72%	|708ms|	9.3M|
-|+SLAHead|	77.7%|94.85%|	766ms|	9.2M|
+|+SLAHead|	77.70%|94.85%|	766ms|	9.2M|
 |+MergeToken|	76.31%|	95.89%|766ms|	9.2M|
 
 * 测试环境
@@ -181,7 +181,7 @@ FGD（Focal and Global Knowledge Distillation for Detectors），是一种兼顾
 
 |策略|Acc|TEDS|推理速度(CPU+MKLDNN)|模型大小|
 |---|---|---|---|---|
-|TableMaster|77.9%|96.12%|2144ms|253M|
+|TableMaster|77.90%|96.12%|2144ms|253.0M|
 |TableRec-RARE|	71.73% | 93.88% |779ms	|6.8M|
 |SLANet|76.31%|	95.89%|766ms|9.2M|
 
@@ -218,7 +218,7 @@ PP-StructureV2中，我们参考TableMaster中的token处理方法，将`<td>`
 除了上述模型策略的升级外，本次升级还开源了中文表格识别模型。在实际应用场景中，表格图像存在着各种各样的倾斜角度（PubTabNet数据集不存在该问题），因此在中文模型中，我们将单元格坐标回归的点数从2个（左上，右下）增加到4个(左上，右上，右下，左下)。在内部测试集上，模型升级前后指标如下：
 |模型|acc|
 |---|---|
-|TableRec-RARE|44.3%|
+|TableRec-RARE|44.30%|
 |SLANet|59.35%|
 
 可视化结果如下，左为输入图像，右为识别的html表格
@@ -307,8 +307,8 @@ LayoutLMv2以及LayoutXLM中引入视觉骨干网络，用于提取视觉特征
 |-----------------|----------|---------|--------|
 | LayoutLMv2      | 0.76     | 84.20%  | -      |
 | VI-LayoutLMv2 | 0.42     | 82.10%  | -2.10% |
-| LayoutXLM       | 1.4      | 89.50%  | -      |
-| VI-LayouXLM   | 1.1      | 90.46%  | +0.96%  |
+| LayoutXLM       | 1.40     | 89.50%  | -      |
+| VI-LayouXLM   | 1.10      | 90.46%  | +0.96%  |
 
 同时，基于XFUND数据集，VI-LayoutXLM在RE任务上的精度也进一步提升了`1.06%`。
 

ppstructure/docs/models_list.md

@@ -13,11 +13,11 @@
 |模型名称|模型简介|推理模型大小|下载地址|dict path|
 | --- | --- | --- | --- | --- |
 | picodet_lcnet_x1_0_fgd_layout | 基于PicoDet LCNet_x1_0和FGD蒸馏在PubLayNet 数据集训练的英文版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
-| ppyolov2_r50vd_dcn_365e_publaynet | 基于PP-YOLOv2在PubLayNet数据集上训练的英文版面分析模型 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [训练模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | 同上 |
+| ppyolov2_r50vd_dcn_365e_publaynet | 基于PP-YOLOv2在PubLayNet数据集上训练的英文版面分析模型 | 221.0M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [训练模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | 同上 |
 | picodet_lcnet_x1_0_fgd_layout_cdla | CDLA数据集训练的中文版面分析模型，可以划分为**表格、图片、图片标题、表格、表格标题、页眉、脚本、引用、公式**10类区域 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
 | picodet_lcnet_x1_0_fgd_layout_table | 表格数据集训练的版面分析模型，支持中英文文档表格区域的检测 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
-| ppyolov2_r50vd_dcn_365e_tableBank_word | 基于PP-YOLOv2在TableBank Word 数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | 同上 |
-| ppyolov2_r50vd_dcn_365e_tableBank_latex | 基于PP-YOLOv2在TableBank Latex数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | 同上 |
+| ppyolov2_r50vd_dcn_365e_tableBank_word | 基于PP-YOLOv2在TableBank Word 数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221.0M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | 同上 |
+| ppyolov2_r50vd_dcn_365e_tableBank_latex | 基于PP-YOLOv2在TableBank Latex数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221.0M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | 同上 |
 
 <a name="2"></a>
 
@@ -54,9 +54,9 @@
 |re_VI-LayoutXLM_xfund_zh|基于VI-LayoutXLM在xfund中文数据集上训练的RE模型|1.1G| 83.92% | 15.49 |[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar) |
 |ser_LayoutXLM_xfund_zh|基于LayoutXLM在xfund中文数据集上训练的SER模型|1.4G| 90.38% | 19.49 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
 |re_LayoutXLM_xfund_zh|基于LayoutXLM在xfund中文数据集上训练的RE模型|1.4G| 74.83% | 19.49 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfund中文数据集上训练的SER模型|778M| 85.44% | 31.46 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
-|re_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M| 67.77% | 31.46 |[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfund_zh|基于LayoutLM在xfund中文数据集上训练的SER模型|430M| 77.31% | - |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfund中文数据集上训练的SER模型|778.0M| 85.44% | 31.46 |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|re_LayoutLMv2_xfund_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765.0M| 67.77% | 31.46 |[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutLM_xfund_zh|基于LayoutLM在xfund中文数据集上训练的SER模型|430.0M| 77.31% | - |[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
 * 注：上述预测耗时信息仅包含了inference模型的推理耗时，没有统计预处理与后处理耗时，测试环境为`V100 GPU + CUDA 10.2 + CUDNN 8.1.1 + TRT 7.2.3.4`。
 
@@ -65,4 +65,4 @@
 
 |模型名称|模型简介|模型大小|精度|下载地址|
 | --- | --- | --- |--- | --- |
-|SDMGR|关键信息提取模型|78M| 86.70% | [推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|关键信息提取模型|78.0M| 86.70% | [推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|

ppstructure/docs/models_list_en.md

@@ -13,11 +13,11 @@
 |model name| description                                                                                                                                             | inference model size                                                                                                                         |download|dict path|
 | --- |---------------------------------------------------------------------------------------------------------------------------------------------------------| --- | --- | --- |
 | picodet_lcnet_x1_0_fgd_layout | The layout analysis English model trained on the PubLayNet dataset based on PicoDet LCNet_x1_0 and FGD . the model can recognition 5 types of areas such as **Text, Title, Table, Picture and List** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
-| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | same as above |
+| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221.0M | [inference_moel](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | same as above |
 | picodet_lcnet_x1_0_fgd_layout_cdla | The layout analysis Chinese model trained on the CDLA dataset, the model can recognition 10 types of areas such as **Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
 | picodet_lcnet_x1_0_fgd_layout_table | The layout analysis model trained on the table dataset, the model can detect tables in Chinese and English documents                     | 9.7M                                                  | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
-| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221M | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | same as above |
-| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221M                 | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | same as above |
+| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221.0M | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | same as above |
+| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221.0M                 | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | same as above |
 
 <a name="2"></a>
 ## 2. OCR and Table Recognition
@@ -63,4 +63,4 @@ On wildreceipt dataset, the algorithm result is as follows:
 
 |Model|Backbone|Config|Hmean|Download link|
 | --- | --- | --- | --- | --- |
-|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.7%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|

ppstructure/table/README.md

@@ -32,7 +32,7 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the
 
 |Method|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
 | --- | --- | --- | ---|
-| EDD<sup>[2]</sup> |x| 88.3 |x|
+| EDD<sup>[2]</sup> |x| 88.30% |x|
 | TableRec-RARE(ours) | 71.73%| 93.88% |779ms|
 | SLANet(ours) | 76.31%|    95.89%|766ms|
 

ppstructure/table/README_ch.md

@@ -38,7 +38,7 @@
 
 |算法|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
 | --- | --- | --- | ---|
-| EDD<sup>[2]</sup> |x| 88.3% |x|
+| EDD<sup>[2]</sup> |x| 88.30% |x|
 | TableRec-RARE(ours) | 71.73%| 93.88% |779ms|
 | SLANet(ours) |76.31%|	95.89%|766ms|
 

tools/program.py

@@ -642,7 +642,8 @@ def preprocess(is_train=False):
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
         'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
         'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
-        'Gestalt', 'SLANet', 'RobustScanner', 'CT', 'RFL', 'DRRG', 'CAN'
+        'Gestalt', 'SLANet', 'RobustScanner', 'CT', 'RFL', 'DRRG', 'CAN',
+        'Telescope'
     ]
 
     if use_xpu: