Official Pytorch implementation of CRAFT text detector | Paper | Pretrained Model | Supplementary
Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.
Clova AI Research, NAVER Corp.
PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores.
Run example on CRAFT_lab.ipynb file
- PyTorch>=0.4.1
- torchvision>=0.2.1
- opencv-python>=3.4.2
- check requiremtns.txt
pip install -r requirements.txt
The code for training is not included in this repository, and we cannot release the full training code for IP reason.
- Download the trained models
Model name | Used datasets | Languages | Purpose | Model Link |
---|---|---|---|---|
General | SynthText, IC13, IC17 | Eng + MLT | For general purpose | Click |
IC15 | SynthText, IC15 | Eng | For IC15 only | Click |
LinkRefiner | CTW1500 | - | Used with the General Model | Click |
@inproceedings{baek2019character,
title={Character Region Awareness for Text Detection},
author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={9365--9374},
year={2019}
}
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