> [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
This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).
This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).
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@@ -95,3 +96,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
Supported end-to-end algorithms (Click the link to get the tutorial):
Supported end-to-end algorithms (Click the link to get the tutorial):
-[x] [PGNet](./algorithm_e2e_pgnet_en.md)
-[x] [PGNet](./algorithm_e2e_pgnet_en.md)
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## 3. Table Recognition Algorithms
Supported table recognition algorithms (Click the link to get the tutorial):
-[3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction)
-[4. Inference and Deployment](#4-inference-and-deployment)
-[4.1 Python Inference](#41-python-inference)
-[4.2 C++ Inference](#42-c-inference)
-[4.3 Serving](#43-serving)
-[4.4 More](#44-more)
-[5. FAQ](#5-faq)
-[Citation](#citation)
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## 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:
Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
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## 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](./dataset/table_datasets_en.md).
After the data download is complete, please refer to [Text Recognition Training Tutorial](./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.
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## 4. Inference and Deployment
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### 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:
- 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:
```shell
cd ppstructure/
# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'.
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:
