Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection； Accepted by ICCV2021.
The codes and paper of TextBPN++ have been released at:TextBPN-Puls-Plus.
NOTE：TextBPN++ contained the code of TextBPN，so it is recommended to refer to code in TextBPN-Puls-Plus.
python 3.9;
PyTorch 1.7.0;
Numpy >=1.2.0
CUDA 11.1;
GCC >=10.0;
opencv-python < 4.5.0
NVIDIA GPU(with 11G or larger GPU memory for inference);
NOTE: The images of each dataset can be obtained from their official website.
- Total-Text model (pretrained on ICDAR2017-MLT)
- CTW-1500 model (pretrained on ICDAR2017-MLT)
- MSRA-TD500 model (pretrained on ICDAR2017-MLT)
run:
sh eval.sh
The details are as follows:
#!/bin/bash
##################### Total-Text ###################################
# test_size=[640,1024]--cfglib/option
CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name Totaltext --checkepoch 390 --dis_threshold 0.3 --cls_threshold 0.825 --test_size 640 1024 --gpu 1
###################### CTW-1500 ####################################
# test_size=[640,1024]--cfglib/option
# CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name Ctw1500 --checkepoch 560 --dis_threshold 0.3 --cls_threshold 0.8 --test_size 640 1024 --gpu 1
#################### MSRA-TD500 ######################################
# test_size=[640,1024]--cfglib/option
#CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name TD500 --checkepoch 680 --dis_threshold 0.3 --cls_threshold 0.925 --test_size 640 1024 --gpu 1
Please cite the related works in your publications if it helps your research:
@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},
}
@inproceedings{Zhang2022ArbitraryST,
title={Arbitrary Shape Text Detection via Boundary Transformer},
author={S. Zhang and Xiaobin Zhu and Chun Yang and Xu-Cheng Yin},
year={2022}
}
This project is licensed under the MIT License - see the LICENSE.md file for details
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