- PSENet and PAN are included in MMOCR.
Official Pytorch implementations of PSENet [1], PAN [2] and PAN++ [3].
[1] W. Wang, E. Xie, X. Li, W. Hou, T. Lu, G. Yu, and S. Shao. Shape robust text detection with progressive scale expansion network. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 9336–9345, 2019.
[2] W. Wang, E. Xie, X. Song, Y. Zang, W. Wang, T. Lu, G. Yu, and C. Shen. Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In Proc. IEEE Int. Conf. Comp. Vis., pages 8440–8449, 2019.
[3] Paper is in preparation.
Python 3.6+
Pytorch 1.1.0
torchvision 0.3
mmcv 0.2.12
editdistance
Polygon3
pyclipper
opencv-python 3.4.2.17
Cython
pip install -r requirement.txt
./compile.shCUDA_VISIBLE_DEVICES=0,1,2,3 python train.py ${CONFIG_FILE}For example:
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py config/pan/pan_r18_ic15.pypython test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}
For example:
python test.py config/pan/pan_r18_ic15.py checkpoints/pan_r18_ic15/checkpoint.pth.tarpython test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --report_speedFor example:
python test.py config/pan/pan_r18_ic15.py checkpoints/pan_r18_ic15/checkpoint.pth.tar --report_speedSee eval.
Todo:
- PAN++
@inproceedings{wang2019shape,
title={Shape robust text detection with progressive scale expansion network},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={9336--9345},
year={2019}
}
@inproceedings{wang2019efficient,
title={Efficient and accurate arbitrary-shaped text detection with pixel aggregation network},
author={Wang, Wenhai and Xie, Enze and Song, Xiaoge and Zang, Yuhang and Wang, Wenjia and Lu, Tong and Yu, Gang and Shen, Chunhua},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={8440--8449},
year={2019}
}
This project is developed and maintained by IMAGINE Lab@National Key Laboratory for Novel Software Technology, Nanjing University.
This project is released under the Apache 2.0 license.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent