Just for fun.
- python = 3.6
- torchvision = 0.6.1
- tabulate = 0.8.7
- overrides = 3.0.0
- opencv_python = 4.3.0.36
- numpy = 1.16.4
- pandas = 1.0.5
- allennlp = 1.0.0
- torchtext = 0.6.0
- tqdm = 4.47.0
- torch = 1.5.1shapely
- scikit-image
- imgaug==0.4.0
- pyclipper
- lmdb
- tqdm
- numpy
- visualdl
- python-Levenshtein
- opencv-contrib-python==4.4.0.46
- cython
- lxml
- premailer
- openpyxl
- paddle
pip install -r requirements.txt
Are you having fun with installing the required environment variables? Here's a good news: besides the above stuffs, you also have to install paddle-gpu (by visiting this link)
In order to run smoothly, you have to:
- Configurating your weight and dataset folders in
config.yml
file. (for each module please read the README file in that folder) - Run the
predictor.py
file
If you find this code useful please cite the authors, not us (we just reimplement)
@inproceedings{Yu2020PICKPK,
title={{PICK}: Processing Key Information Extraction from Documents using
Improved Graph Learning-Convolutional Networks},
author={Wenwen Yu and Ning Lu and Xianbiao Qi and Ping Gong and Rong Xiao},
booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
year={2020}
}
- PaddleOCR
- Many thanks to Khanh Tran and Karl Horky for contributing and revising the English documentation.
- Many thanks to zhangxin for contributing the new visualize function、add .gitignore and discard set PYTHONPATH manually.
- Many thanks to lyl120117 for contributing the code for printing the network structure.
- Thanks xiangyubo for contributing the handwritten Chinese OCR datasets.
- Thanks authorfu for contributing Android demo and xiadeye contributing iOS demo, respectively.
- Thanks BeyondYourself for contributing many great suggestions and simplifying part of the code style.
- Thanks tangmq for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
- Thanks lijinhan for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
- Thanks Mejans for contributing the Occitan corpus and character set.
- Thanks LKKlein for contributing a new deploying package with the Golang program language.
- Thanks Evezerest, ninetailskim, edencfc, BeyondYourself and 1084667371 for contributing a new data annotation tool, i.e., PPOCRLabel。
This project is licensed under the MIT License. See LICENSE for more details.
This project structure takes example by PyTorch Template Project.