Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee (Submitted on 3 Apr 2019)
The full paper is available at: https://arxiv.org/pdf/1904.01941.pdf
1、PyTroch>=0.4.1
2、torchvision>=0.2.1
3、opencv-python>=3.4.2
4、check requiremtns.txt
5、4 nvidia GPUs(we use 4 nvidia titanX)
Syndata:Syndata for baidu drive || Syndata for google drive
Syndata+IC15:Syndata+IC15 for baidu drive || Syndata+IC15 for google
drive
Syndata+IC13+IC17:Syndata+IC13+IC17 for baidu drive|| Syndata+IC13+IC17 for google drive
- download the Syndata(I will give the link)
- change the path in basernet/vgg16_bn.py file:
(/data/CRAFT-pytorch/vgg16_bn-6c64b313.pth -> /your_path/vgg16_bn-6c64b313.pth).You can download the model here.
baidu||google
- change the path in trainSyndata.py file:
(1、/data/CRAFT-pytorch/SynthText -> /your_path/SynthText 2、/data/CRAFT-pytorch/synweights/synweights -> /your_path/real_weights)
- Run
python trainSyndata.py
- download the IC15 data, rename the image file and the gt file for ch4_training_images and ch4_training_localization_transcription_gt,respectively.
- change the path in basernet/vgg16_bn.py file:
(/data/CRAFT-pytorch/vgg16_bn-6c64b313.pth -> /your_path/vgg16_bn-6c64b313.pth).You can download the model here.
baidu||google
- change the path in trainic15data.py file:
(1、/data/CRAFT-pytorch/SynthText -> /your_path/SynthText 2、/data/CRAFT-pytorch/real_weights -> /your_path/real_weights)
- change the path in trainic15data.py file:
(1、/data/CRAFT-pytorch/1-7.pth -> /your_path/your_pre-trained_model_name 2、/data/CRAFT-pytorch/icdar1317 -> /your_ic15data_path/)
- Run
python trainic15data.py
coming soon
1、You should first download the pre_trained model trained in the Syndata baidu||google.
2、change the data path and pre-trained model path.
3、run python trainic15data.py
This code supprts for Syndata and icdar2015, and we will release the training code for IC13 and IC17 as soon as possible.
Methods | dataset | Recall | precision | H-mean |
---|---|---|---|---|
Syndata | ICDAR13 | 71.93% | 81.31% | 76.33% |
Syndata+IC15 | ICDAR15 | 76.12% | 84.55% | 80.11% |
Syndata+IC13+IC17(deteval) | ICDAR13 | 86.81% | 95.28% | 90.85% |
There are our detection results with bad samples. We found that it's so terrible for detecting the big word. And the gaussian map can not split the character level gaussian region score map. We are trying to solve it, and any issues or advice are welcome.
We will release training code as soon as possible, and we have not yet reached the results given in the author's paper. Any pull requests or issues are welcome. We also hope that you could give us some advice for the project.
Thanks for Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee excellent work and code for test. In this repo, we use the author repo's basenet and test code.
For commercial use, please contact us.