Awesome
CRAFT: Character-Region Awareness For Text detection
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.
Sample Results
Overview
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.
<img width="1000" alt="teaser" src="./figures/craft_example.gif">Updates
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
Getting started
Install dependencies
Requirements
- PyTorch>=0.4.1
- torchvision>=0.2.1
- opencv-python>=3.4.2
- check requiremtns.txt
pip install -r requirements.txt
Training
The code for training is not included in this repository, and we cannot release the full training code for IP reason.
Test instruction using pretrained model
- 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.
Arguments
--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
Links
- WebDemo : https://demo.ocr.clova.ai/
- Repo of recognition : https://github.com/clovaai/deep-text-recognition-benchmark
Citation
@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}
}
License
Copyright (c) 2019-present NAVER Corp.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.