chore: import upstream snapshot with attribution

2026-07-13 11:59:26 +08:00
commit e904b667c6
2490 changed files with 596352 additions and 0 deletions
@@ -0,0 +1,93 @@
+---
+typora-copy-images-to: images
+comments: true
+---
+
+
+# Table Recognition Algorithm-TableMASTER
+
+## 1. Introduction
+
+Paper:
+> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf)
+> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong
+> 2021
+
+On the PubTabNet table recognition public data set, the algorithm reproduction acc is as follows:
+
+|Model|Backbone|Cnnfig|Acc|Download link|
+| --- | --- | --- | --- | --- |
+|TableMaster|TableResNetExtra|[configs/table/table_master.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)|
+
+## 2. Environment
+
+Please refer to ["Environment Preparation"](../../ppocr/environment.en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](../../ppocr/blog/clone.en.md)to clone the project code.
+
+## 3. Model Training / Evaluation / Prediction
+
+The above TableMaster model is trained using the PubTabNet table recognition public dataset. For the download of the dataset, please refer to [table_datasets](../../../datasets/table_datasets.en.md).
+
+After the data download is complete, please refer to [Text Recognition Training Tutorial](../../ppocr/model_train/recognition.en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models.
+
+## 4. Inference and Deployment
+
+### 4.1 Python Inference
+
+First, convert the model saved in the TableMaster table recognition training process into an inference model. Taking the model based on the TableResNetExtra backbone network and trained on the PubTabNet dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/table_master.tar)), you can use the following command to convert:
+
+```bash linenums="1"
+python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master
+```
+
+**Note:**
+
+- If you trained the model on your own dataset and adjusted the dictionary file, please pay attention to whether the `character_dict_path` in the modified configuration file is the correct dictionary file
+
+Execute the following command for model inference:
+
+```bash linenums="1"
+cd ppstructure/
+# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'.
+python3 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
+```
+
+After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows:
+
+result：
+
+```bash linenums="1"
+[2022/06/16 13:06:54] ppocr INFO: result: ['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344],
+...
+[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]]
+[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg
+[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246
+```
+
+**Note**:
+
+- TableMaster is relatively slow during inference, and it is recommended to use GPU for use.
+
+### 4.2 C++ Inference
+
+Since the post-processing is not written in CPP, the TableMaster does not support CPP inference.
+
+### 4.3 Serving
+
+Not supported
+
+### 4.4 More
+
+Not supported
+
+## 5. FAQ
+
+## Citation
+
+```bibtex
+@article{ye2021pingan,
+  title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML},
+  author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong},
+  journal={arXiv preprint arXiv:2105.01848},
+  year={2021}
+}
+```
@@ -0,0 +1,100 @@
+---
+typora-copy-images-to: images
+comments: true
+---
+
+
+# 表格识别算法-TableMASTER
+
+## 1. 算法简介
+
+论文信息：
+> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf)
+> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong
+> 2021
+
+在PubTabNet表格识别公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|acc|下载链接|
+| --- | --- | --- | --- | --- |
+|TableMaster|TableResNetExtra|[configs/table/table_master.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)|
+
+## 2. 环境配置
+
+请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
+
+## 3. 模型训练、评估、预测
+
+上述TableMaster模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](../../../datasets/table_datasets.md)。
+
+数据下载完成后，请参考[文本识别教程](../../ppocr/model_train/recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。
+
+## 4. 推理部署
+
+### 4.1 Python推理
+
+首先将训练得到best模型，转换成inference model。以基于TableResNetExtra骨干网络，在PubTabNet数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/contribution/table_master.tar))，可以使用如下命令进行转换：
+
+```bash linenums="1"
+# 注意将pretrained_model的路径设置为本地路径。
+python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master
+```
+
+**注意：** 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。
+
+转换成功后，在目录下有三个文件：
+
+```text linenums="1"
+./inference/table_master/
+    ├── inference.pdiparams         # 识别inference模型的参数文件
+    ├── inference.pdiparams.info    # 识别inference模型的参数信息，可忽略
+    └── inference.pdmodel           # 识别inference模型的program文件
+```
+
+执行如下命令进行模型推理：
+
+```bash linenums="1"
+cd ppstructure/
+python3 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg
+# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。
+```
+
+执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下：
+结果如下：
+
+```bash linenums="1"
+[2022/06/16 13:06:54] ppocr INFO: result: ['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344],
+...
+[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]]
+[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg
+[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246
+```
+
+**注意**：
+
+- TableMaster在推理时比较慢，建议使用GPU进行使用。
+
+### 4.2 C++推理部署
+
+由于C++预处理后处理还未支持TableMaster，所以暂未支持
+
+### 4.3 Serving服务化部署
+
+暂不支持
+
+### 4.4 更多推理部署
+
+暂不支持
+
+## 5. FAQ
+
+## 引用
+
+```bibtex
+@article{ye2021pingan,
+  title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML},
+  author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong},
+  journal={arXiv preprint arXiv:2105.01848},
+  year={2021}
+}
+```
@@ -0,0 +1,97 @@
+---
+comments: true
+---
+
+# 表格识别算法-SLANet-LCNetV2
+
+## 1. 算法简介
+
+该算法由来自北京交通大学机器学习与认识计算研究团队的ocr识别队研发，其在PaddleOCR算法模型挑战赛 - 赛题二：通用表格识别任务中排行榜荣获一等奖，排行榜精度相比PP-Structure表格识别模型提升0.8%，推理速度提升3倍。优化思路如下：
+
+1. 改善推理过程，至EOS停止，速度提升3倍；
+2. 升级Backbone为LCNetV2（SSLD版本）；
+3. 行列特征增强模块；
+4. 提升分辨率488至512；
+5. 三阶段训练策略。
+
+在PubTabNet表格识别公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|acc|下载链接|
+| --- | --- | --- | --- | --- |
+|SLANet|LCNetV2|[configs/table/SLANet_lcnetv2.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/table/SLANet_lcnetv2.yml)|76.67%| [训练模型](https://paddleocr.bj.bcebos.com/openatom/ch_ppstructure_openatom_SLANetv2_train.tar) /[推理模型](https://paddleocr.bj.bcebos.com/openatom/ch_ppstructure_openatom_SLANetv2_infer.tar) |
+
+## 2. 环境配置
+
+请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
+
+## 3. 模型训练、评估、预测
+
+上述SLANet_LCNetv2模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](../../../datasets/table_datasets.md)。
+
+### 启动训练
+
+数据下载完成后，请参考[文本识别教程](../../ppocr/model_train/recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。
+
+训练命令如下：
+
+```bash linenums="1"
+# stage1
+python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/table/SLANet_lcnetv2.yml
+# stage2 加载stage1的best model作为预训练模型，学习率调整为0.0001;
+# stage3 加载stage2的best model作为预训练模型，不调整学习率，将配置文件中所有的488修改为512.
+```
+
+## 4. 推理部署
+
+### 4.1 Python推理
+
+将训练得到best模型，转换成inference model，可以使用如下命令进行转换：
+
+```bash linenums="1"
+# 注意将pretrained_model的路径设置为本地路径。
+python3 tools/export_model.py -c configs/table/SLANet_lcnetv2.yml -o Global.pretrained_model=path/best_accuracy Global.save_inference_dir=./inference/slanet_lcnetv2_infer
+```
+
+**注意：** 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。
+
+转换成功后，在目录下有三个文件：
+
+```text linenums="1"
+./inference/slanet_lcnetv2_infer/
+    ├── inference.pdiparams         # 识别inference模型的参数文件
+    ├── inference.pdiparams.info    # 识别inference模型的参数信息，可忽略
+    └── inference.pdmodel           # 识别inference模型的program文件
+```
+
+执行如下命令进行模型推理：
+
+```bash linenums="1"
+cd ppstructure/
+python table/predict_structure.py --table_model_dir=../inference/slanet_lcnetv2_infer/ --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --image_dir=docs/table/table.jpg --output=../output/table_slanet_lcnetv2 --use_gpu=False --benchmark=True --enable_mkldnn=True --table_max_len=512
+# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。
+```
+
+执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下：
+结果如下：
+
+```bash linenums="1"
+[2022/06/16 13:06:54] ppocr INFO: result: ['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344],
+...
+[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]]
+[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg
+[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246
+```
+
+### 4.2 C++推理部署
+
+由于C++预处理后处理还未支持SLANet
+
+### 4.3 Serving服务化部署
+
+暂不支持
+
+### 4.4 更多推理部署
+
+暂不支持
+
+## 5. FAQ