This is an implementation of “Deep relational reasoning graph network for arbitrary shape text detection”.
News
- Our new work at https://github.com/GXYM/TextBPN-Plus-Plus.
- This project is reproduced in MMOCR.
- This project is reproduced in PaddleOCR.
- This project is reproduced by Paddle implementation in DRRG_Paddle. Description of reproduce is in Paddle AI Studio
Prerequisites
python 3.7;
PyTorch 1.2.0;
Numpy >=1.16;
CUDA 10.1;
GCC >=9.0;
opencv-python < 4.5.0
NVIDIA GPU(with 10G or larger GPU memory for inference);
Compile
cd ./csrc and make
cd ./nmslib/lanms and make
Data Links
Note: download the data and put it under the data file
Models
- The trained models of Total-Text, CTW-1500 model, MSRA-TD500, MLT2017, Icdar2015 all in here.
Google Drive or Baidu Drive (download code: cfat)
Train
cd tool
sh train_CTW1500.sh # run or other shell script
you should modify the relevant training parameters according to the environment， such as gpu_id and input_size:
#!/bin/bash
cd ../
CUDA_LAUNCH_BLOCKING=1 python train_TextGraph.py --exp_name Ctw1500 --max_epoch 600 --batch_size 6 --gpu 0 --input_size 640 --optim SGD --lr 0.001 --start_epoch 0 --viz --net vgg
# --resume pretrained/mlt2017_pretain/textgraph_vgg_100.pth ### load the pretrain model, You should change this path to your own
Eval
First, you can modify the relevant parameters in the config.py and option.py
python eval_TextGraph.py # Testing single round model
or
python batch_eval.py # Testing multi round models
Qualitative results(
)
Citing the related works
@inproceedings{DBLP:conf/cvpr/ZhangZHLYWY20,
author = {Shi{-}Xue Zhang and
Xiaobin Zhu and
Jie{-}Bo Hou and
Chang Liu and
Chun Yang and
Hongfa Wang and
Xu{-}Cheng Yin},
title = {Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection},
booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2020, Seattle, WA, USA, June 13-19, 2020},
pages = {9696--9705},
publisher = {Computer Vision Foundation / {IEEE}},
year = {2020},
doi = {10.1109/CVPR42600.2020.00972},
}
@inproceedings{DBLP:conf/iccv/Zhang0YWY21,
author = {Shi{-}Xue Zhang and
Xiaobin Zhu and
Chun Yang and
Hongfa Wang and
Xu{-}Cheng Yin},
title = {Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection},
booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV} 2021, Montreal, QC, Canada, October 10-17, 2021},
pages = {1285--1294},
publisher = {{IEEE}},
year = {2021},
}
@article{zhang2023arbitrary,
title={Arbitrary shape text detection via boundary transformer},
author={Zhang, Shi-Xue and Yang, Chun and Zhu, Xiaobin and Yin, Xu-Cheng},
journal={IEEE Transactions on Multimedia},
year={2023},
publisher={IEEE}
}
@article{DBLP:journals/pami/ZhangZCHY23,
author = {Shi{-}Xue Zhang and
Xiaobin Zhu and
Lei Chen and
Jie{-}Bo Hou and
Xu{-}Cheng Yin},
title = {Arbitrary Shape Text Detection via Segmentation With Probability Maps},
journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
volume = {45},
number = {3},
pages = {2736--2750},
year = {2023},
url = {https://doi.org/10.1109/TPAMI.2022.3176122},
doi = {10.1109/TPAMI.2022.3176122},
}
@article{zhang2022kernel,
title={Kernel proposal network for arbitrary shape text detection},
author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Yang, Chun and Yin, Xu-Cheng},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022},
publisher={IEEE}
}
License
This project is licensed under the MIT License - see the LICENSE.md file for details