Stroke-Based Scene Text Erasing

This repository is a PyTorch implementation of following paper:

Stroke-Based Scene Text Erasing Using Synthetic Data for Training | IEEE Xplore

Zhengmi Tang, Tomo Miyazaki, Yoshihiro Sugaya, and Shinichiro Omachi.

Graduate School of Engineering, Tohoku University.

Requirements

PyTorch==1.8.1
scikit_image==0.18.1
tqdm==4.55.1
torchvision==0.9.1
numpy==1.19.2
opencv-python==4.5.1.48

Training

• perpare the training dataset root as:

--train_set
     |i_s
        | 1.jpg
        | 2.jpg
     |mask_t
        | 1.png
        | 2.png
     |t_b
        | 1.jpg 
        | 2.jpg

• tune the training parameters in cfg.py. If you want to finetune the model, turn the flag of finetune and resume both in True.
• run

python train.py

Testing

• prepare the format of testing data as ./examples file shows. The names of txt files should be gt_img_?.txt or res_img_?.txt and the annotation of text bboxes should be quadrilateral.
• download our retrained pretrained model. (Note that we retrained our model in a different preprocessing strategy of training data for better visual perception. best.pth for real-world data testing and best_syn.pth for synthetic data testing)
• revise the model_path, src_img_dir and src_txt_dir with the right path in test.py
• run

python test.py

Evaluation

• prepare the names of result images and label images as same name, for example, img_?.png or img_?.jpg
• revise the result_path and label_path with the right path in eval.py
• run

python eval.py

Citation

If you find our method or code useful for your research, please cite:

@article{StrokeErase2021tang,
  author = {Tang, Zhengmi and Miyazaki, Tomo and Sugaya, Yoshihiro and Omachi, Shinichiro},
  journal = {IEEE Transactions on Image Processing},
  title = {Stroke-Based Scene Text Erasing Using Synthetic Data for Training},
  year = {2021},
  volume = {30},
  pages = {9306-9320}
}

Acknowledge

We thank tanimutomo for the excellent code.