Awesome

News

PSENet is included in MMOCR.
We have implemented PSENet using Paddle. You can find the pytorch version here.
You can find code of PAN here.
Another group did the same job. You can visit it here. You can also have a try online with all the environment ready here.

Introduction

Official Paddle 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.<br>

Recommended environment

Python 3.6+
paddlepaddle-gpu 2.0.2
nccl 2.0+
mmcv 0.2.12
editdistance
Polygon3
pyclipper
opencv-python 3.4.2.17
Cython

Install

Install paddle following the official tutorial.

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

Training

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

For example:

CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_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.pdparams

Evaluation

Introduction

The evaluation scripts of ICDAR 2015 (IC15) dataset.

ICDAR 2015

Text detection

./eval_ic15.sh

Benchmark

Results

ICDAR 2015

Method	Backbone	Fine-tuning	Scale	Config	Precision (%)	Recall (%)	F-measure (%)	Model
PSENet	ResNet50	N	Shorter Side: 736	psenet_r50_ic15_736.py	82.2	79.4	80.7	Google Drive

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 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.