Introduction

Official Pytorch implementations of PSENet [1].

[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.

Recommended environment

Python 3.6+
Pytorch 1.1.0
torchvision 0.3
mmcv 0.2.12
editdistance
Polygon3
pyclipper
opencv-python 3.4.2.17
Cython

Install

pip install -r requirement.txt
./compile.sh

Training

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py ${CONFIG_FILE}

For example:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py config/psenet/psenet_r50_ic15_736.py

Test

python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}

For example:

python test.py config/psenet/psenet_r50_ic15_736.py checkpoints/psenet_r50_ic15_736/checkpoint.pth.tar

Speed

python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --report_speed

For example:

python test.py config/psenet/psenet_r50_ic15_736.py checkpoints/psenet_r50_ic15_736/checkpoint.pth.tar --report_speed

Evaluation

Introduction

The evaluation scripts of ICDAR 2015 (IC15), Total-Text (TT) and CTW1500 (CTW) datasets.

ICDAR 2015

Text detection

./eval_ic15.sh

Total-Text

Text detection

./eval_tt.sh

CTW1500

Text detection

./eval_ctw.sh

Benchmark

Results

ICDAR 2015

Method	Backbone	Config	Short_Size	Precision (%)	Recall (%)	F-measure (%)
PSENet	ResNet50	psenet_r50_ic15_736.py	736	83.6	74.0	78.5
PSENet	ResNet50	psenet_r50_ic15_1280.py	1024	83.4	75.5	79.3

Citation

@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}
}

License

This project is released under the Apache 2.0 license.