GXYM/DRRG

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

GXYM / DRRG