https://github.com/amir22010/craft-pytorch

Pytorch implementation of CRAFT text detector

https://github.com/amir22010/craft-pytorch

Repository

Pytorch implementation of CRAFT text detector

https://github.com/Amir22010/CRAFT-pytorch/blob/master/

## CRAFT: Character-Region Awareness For Text detection
Official Pytorch implementation of CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) | [Pretrained Model](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ) | [Supplementary](https://youtu.be/HI8MzpY8KMI)

**[Youngmin Baek](mailto:youngmin.baek@navercorp.com), 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. 



## Updates
**13 Jun, 2019**: Initial update


## 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
We are currently in the process of cleaning training code for disclosure.

### Test instruction using pretrained model
- Download [Trained Model on IC13,IC17](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ)
* Run with pretrained model
``` (with python 3.7)
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
* `--canvas_size`: max image size for inference
* `--mag_ratio`: image magnification ratio
* `--show_time`: show processing time
* `--test_folder`: folder path to input images

## Links
- WebDemo : https://demo.ocr.clova.ai/
- Repo of recognition : https://github.com/clovaai/deep-text-recognition-benchmark

## Citation
```
@article{baek2019character,
  title={Character Region Awareness for Text Detection},
  author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
  journal={arXiv preprint arXiv:1904.01941},
  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.
```

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