# TableMASTER-mmocr **Repository Path**: glunba/TableMASTER-mmocr ## Basic Information - **Project Name**: TableMASTER-mmocr - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-28 - **Last Updated**: 2025-04-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

TableMASTER-mmocr

## New + 2021/09: **TableMASTER** pretrained model release. + 2021/10: **Lmdb** training data release. + 2021/11: **tablemaster_mmocr** docker environment release. + 2021/12: End2end **demo** inference release, **pretrained model** release.

Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Result
  5. Pretrained model
  6. Lmdb data
  7. License
  8. Acknowledgements
## About The Project This project presents our 2nd place solution for **ICDAR 2021 Competition on Scientific Literature Parsing, Task B**. We reimplement our solution by [MMOCR](https://github.com/open-mmlab/mmocr)，which is an open-source toolbox based on PyTorch. You can click [here](https://aieval.draco.res.ibm.com/challenge/40/overview) for more details about this competition. Our original implementation is based on **FastOCR** (one of our internal toolbox similar with MMOCR). ### Method Description In our solution, we divide the table content recognition task into four sub-tasks: **table structure recognition**, **text line detection**, **text line recognition**, and **box assignment**. Based on [MASTER](https://arxiv.org/abs/1910.02562), we propose a novel table structure recognition architrcture, which we call **TableMASTER**. The difference between **MASTER** and **TableMASTER** will be shown below. You can click [here](https://arxiv.org/pdf/2105.01848.pdf) for more details about this solution. ![MASTER's architecture](./imgs/architecture.jpg) ### Dependency * [MMOCR-0.2.0](https://github.com/open-mmlab/mmocr/tree/v0.2.0) * [MMDetection-2.11.0](https://github.com/open-mmlab/mmdetection/tree/v2.11.0) * [mmcv-full-1.3.4](https://github.com/open-mmlab/mmcv/tree/v1.3.4) ## Getting Started ### Prerequisites + Competition dataset **PubTabNet**, click [here](https://developer.ibm.com/exchanges/data/all/pubtabnet/) for downloading. + About **PubTabNet**, check their [github](https://github.com/ibm-aur-nlp/PubTabNet) and [paper](https://arxiv.org/abs/1911.10683). * About the metric **TEDS**, see [github](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) ### Installation 2. Install mmdetection. click [here](https://github.com/open-mmlab/mmdetection/blob/v2.11.0/docs/get_started.md) for details. ```sh # We embed mmdetection-2.11.0 source code into this project. # You can cd and install it (recommend). cd ./mmdetection-2.11.0 pip install -v -e . ``` 3. Install mmocr. click [here](https://github.com/open-mmlab/mmocr/blob/main/docs/install.md) for details. ```sh # install mmocr cd {Path to TableMASTER_mmocr} pip install -v -e . ``` 4. Install mmcv-full-1.3.4. click [here](https://github.com/open-mmlab/mmcv) for details. ```sh pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html # install mmcv-full-1.3.4 with torch version 1.8.0 cuda_version 10.2 pip install mmcv-full==1.3.4 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html ``` ### Docker environment ```shell docker pull jiaquanye/tablemaster_mmocr:v1 ``` ## Usage ### Data preprocess Run [data_preprocess.py](./table_recognition/data_preprocess.py) to get valid train data. Remember to change the **'raw_img_root'** and **‘save_root’** property of **PubtabnetParser** to your path. ```shell python ./table_recognition/data_preprocess.py ``` It will about 8 hours to finish parsing 500777 train files. After finishing the train set parsing, change the property of **'split'** folder in **PubtabnetParser** to **'val'** and get formatted val data. Directory structure of parsed train data is : ```shell . ├── StructureLabelAddEmptyBbox_train │ ├── PMC1064074_007_00.txt │ ├── PMC1064076_003_00.txt │ ├── PMC1064076_004_00.txt │ └── ... ├── recognition_train_img │ ├── 0 │ ├── PMC1064100_007_00_0.png │ ├── PMC1064100_007_00_10.png │ ├── ... │ └── PMC1064100_007_00_108.png │ ├── 1 │ ├── ... │ └── 15 ├── recognition_train_txt │ ├── 0.txt │ ├── 1.txt │ ├── ... │ └── 15.txt ├── structure_alphabet.txt └── textline_recognition_alphabet.txt ``` We also transfer the raw **Pubtanet** data to **Lmdb** files by the script [lmdb_maker.py](https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/lmdb_maker.py). Click [here](https://pan.baidu.com/s/1X3P2zFpEBN1T_r22l_9zSA) to download the **Pubtanet** data **Lmdb** files (code:uxl1) If you want to train your model via **Lmdb** files, please take a look at the [TableMASTER lmdb config file](https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/configs/textrecog/master/table_master_lmdb_ResnetExtract_Ranger_0930.py) and [text-line MASTER lmdb config file](https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/configs/textrecog/master/master_lmdb_ResnetExtra_tableRec_dataset_dynamic_mmfp16.py) ### Train 1. Train text line detection model with [PSENet](https://arxiv.org/pdf/1806.02559.pdf). ```shell sh ./table_recognition/expr/table_text_line_detection_dist_train.sh ``` We don't offer PSENet train data here, you can create the text line annotations by open source label software. In our experiment, we only use 2,500 table images to train our model. It gets a perfect text line detection result on validation set. 2. Train text-line recognition model with [MASTER](https://arxiv.org/abs/1910.02562). ```shell sh ./table_recognition/expr/table_text_line_recognition_dist_train.sh ``` We can get about 30,000,000 text line images from 500,777 training images and 550,000 text line images from 9115 validation images. But we only select 20,000 text line images from 550,000 dataset for evaluatiing after each trainig epoch, to pick up the best text line recognition model. Note that our MASTER OCR is directly trained on samples mixed with single-line texts and multiple-line texts. 3. Train table structure recognition model, with **TableMASTER**. ```shell sh ./table_recognition/expr/table_recognition_dist_train.sh ``` ### Inference To get final results, firstly, we need to forward the three up-mentioned models, respectively. Secondly, we merge the results by our matching algorithm, to generate the final HTML code. 1. Models inference. We do this to speed up the inference. ```shell python ./table_recognition/run_table_inference.py ``` [run_table_inference.py](./table_recognition/run_table_inference.py) wil call [table_inference.py](./table_recognition/table_inference.py) and use multiple gpu devices to do model inference. Before running this script, you should change the value of **cfg** in [table_inference.py](./table_recognition/table_inference.py) . Directory structure of text line detection and text line recognition inference results are: ```shell # If you use 8 gpu devices to inference, you will get 8 detection results pickle files, one end2end_result pickle files and 8 structure recognition results pickle files. . ├── end2end_caches │ ├── end2end_results.pkl │ ├── detection_results_0.pkl │ ├── detection_results_1.pkl │ ├── ... │ └── detection_results_7.pkl ├── structure_master_caches │ ├── structure_master_results_0.pkl │ ├── structure_master_results_1.pkl │ ├── ... │ └── structure_master_results_7.pkl ``` 2. Merge results. ```shell python ./table_recognition/match.py ``` After matching, congratulations, you will get final result pickle file. ### Get TEDS score 1. Installation. ```shell pip install -r ./table_recognition/PubTabNet-master/src/requirements.txt ``` 2. Get **gtVal.json**. ```shell python ./table_recognition/get_val_gt.py ``` 3. Calcutate TEDS score. Before run this script, modify pred file path and gt file path in [mmocr_teds_acc_mp.py](./table_recognition/PubTabNet-master/src/mmocr_teds_acc_mp.py) ```shell python ./table_recognition/PubTabNet-master/src/mmocr_teds_acc_mp.py ``` ## Result **Text line end2end recognition accuracy** | Models | Accuracy | | :-------------: | :--------: | | PSENet + MASTER | **0.9885** | **Structure recognition accuracy** | Model architecture | Accuracy | | :-----------------------------------: | :--------: | | TableMASTER_maxlength_500 | 0.7808 | | TableMASTER_ConcatLayer_maxlength_500 | **0.7821** | | TableMASTER_ConcatLayer_maxlength_600 | 0.7799 | **TEDS score** | Models | TEDS | | :-----------------------------------------------------: | :--------: | | PSENet + MASTER + TableMASTER_maxlength_500 | 0.9658 | | PSENet + MASTER + TableMASTER_ConcatLayer_maxlength_500 | 0.9669 | | PSENet + MASTER + ensemble_TableMASTER | **0.9676** | In [this paper](https://arxiv.org/pdf/2105.01848.pdf), we reported 0.9684 TEDS score in validation set (9115 samples). The gap between **0.9676** and **0.9684** comes from that we ensemble three text line models in the competition, but here, we only use one model. Of course, hyperparameter tuning will also affect TEDS score. ## Pretrained Model The **TableMASTER** (TableMASTER_maxlength_500) pretrained model. In the validation set, the accuracy is **0.7767**. [[Google Drive]](https://drive.google.com/file/d/1LSuVQJ0J8WFtXhLfcCKyzGqcCYmcwEk6/view?usp=sharing) [[BaiduYun Drive]](https://pan.baidu.com/s/1G2tBpycZY6c6wzfE3V9khw) code:**irp6**
The table textline detection model **PSENet** pretrained model. [[Google Drive]](https://drive.google.com/file/d/13vni9GH6cxr5jTiOdiRu--Q6AZojB2p2/view?usp=sharing) [[BaiduYun Drive]](https://pan.baidu.com/s/1fPdkS6iTA8CKmjsQ7noKLw) code:**6b30** We also release the **PSENet train data**. [[Google Drive]](https://drive.google.com/file/d/1IjNM7GZKZEioFz7yoA4ZVF4SUY8IP2Qi/view?usp=sharing) [[BaiduYun Drive]](https://pan.baidu.com/s/1jI1JhhvSASo54kvu7QPJ5g) code:**rzu2**
The table textline recognition model **MASTER** pretrained model. In the textline validation subset, the accuracy is **0.9798**. [[Google Drive]](https://drive.google.com/file/d/1bqTRaUlV2UViIZ5qlJR5VlTMLMf03oGA/view?usp=sharing) [[BaiduYun Drive]](https://pan.baidu.com/s/1_Ezm67marVeHZ-xm7Fk_Lw) code:**kp25** **PS:** Due to some mistakes occur in making text-line recognition lmdb file, the accuracy of the MASTER pretrained model, is lower than the accuracy reported in [this paper](https://arxiv.org/pdf/2105.01848.pdf). In this version, we don't filter the text-line images of **table head**. In **pubtabnet**, content in **table head** is always surrounded by **b /b**, which means **overstriking** in visual. So, training with text-line images of **table head** will make model failed to recognition **overstriking** character. **It will drop the word accuracy of text-line recognition model. Of course, it will drop the final TEDS score**. We will fix this bug when next version pretrained model releases. You can also train a text-line recognition model, after filtering the text-line images of table head.
The final **TEDS** score is **0.9618**. You can get the log file in the following link. [[Google Drive]](https://drive.google.com/file/d/1PGq9Y12eMfXMWiHJMi-_SgHyO1ACxnzp/view?usp=sharing) [[BaiduYun Drive]](https://pan.baidu.com/s/1wT0QttmmPDBnViVAMqDGKA) code:**g3gx** ## Lmdb Data We convert the raw **Pubtabnet** data to **Lmdb** files. You can download by this link: [[BaiduYun Drive]](https://pan.baidu.com/s/1X3P2zFpEBN1T_r22l_9zSA) code: **uxl1** ## License This project is licensed under the MIT License. See LICENSE for more details. ## Citations ```latex @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} } @article{He2021PingAnVCGroupsSF, title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex}, author={Yelin He and Xianbiao Qi and Jiaquan Ye and Peng Gao and Yihao Chen and Bingcong Li and Xin Tang and Rong Xiao}, journal={ArXiv}, year={2021}, volume={abs/2105.01846} } @article{Lu2021MASTER, title={{MASTER}: Multi-Aspect Non-local Network for Scene Text Recognition}, author={Ning Lu and Wenwen Yu and Xianbiao Qi and Yihao Chen and Ping Gong and Rong Xiao and Xiang Bai}, journal={Pattern Recognition}, year={2021} } @article{li2018shape, title={Shape robust text detection with progressive scale expansion network}, author={Li, Xiang and Wang, Wenhai and Hou, Wenbo and Liu, Ruo-Ze and Lu, Tong and Yang, Jian}, journal={arXiv preprint arXiv:1806.02559}, year={2018} } ``` ## Acknowledgements * [MASTER-pytorch](https://github.com/wenwenyu/MASTER-pytorch) * [MASTER-TF](https://github.com/jiangxiluning/MASTER-TF) * [OpenMMLab](https://github.com/open-mmlab) * [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) * [PSENet](https://github.com/whai362/PSENet) * [MASTER-mmocr](https://github.com/JiaquanYe/MASTER-mmocr) * [TableMASTER Report PPT](./imgs/table2html.pdf) * [Bilibili Report Video](https://www.bilibili.com/video/BV1pU4y1E7Fq?spm_id_from=333.999.0.0)