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.
13 Jun, 2019: Initial update 20 Jul, 2019: Added post-processing for polygon result 28 Sep, 2019: Added the trained model on IC15 and the link refiner
- 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 |
- Run with pretrained model
python test.py --trained_model=[weightfile] --test_folder=[folder path to test images]
The result image and socre maps will be saved to ./result by default.
--trained_model: pretrained model--text_threshold: text confidence threshold--low_text: text low-bound score--link_threshold: link confidence threshold--cuda: use cuda for inference (default:True)--canvas_size: max image size for inference--mag_ratio: image magnification ratio--poly: enable polygon type result--show_time: show processing time--test_folder: folder path to input images--refine: use link refiner for sentense-level dataset--refiner_model: pretrained refiner model
- WebDemo : https://demo.ocr.clova.ai/
- Repo of recognition : https://github.com/clovaai/deep-text-recognition-benchmark
@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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