/C3-STISR

Official Code for 'C3-STISR: Scene Text Image Super-resolution with Triple Clues' - IJCAI 2022

Primary LanguagePythonApache License 2.0Apache-2.0

โœจ C3-STISR โœจ

Official Code for 'C3-STISR: Scene Text Image Super-resolution with Triple Clues'

IJCAI 2022 Accepted Paper

The code will be gradually open source!

Due to company policy, I can't provide the code. To guarantee reproducibility, I am working on reimplement C3-STISR. The final result will be released around September. Thank you all for your support.

Since a lot of my works (including reviewing papers) are written in one framework, I decide not to release the complete code. Instead, following items are released for reproducibility:

  • model scrits

  • training scripts

  • log

  • final pth (See Release)

You can find the rest codes in TPGSR, TATT and STT.

Current codes may have some bugs, I will fix them soon. Thanks.

๐Ÿ“ง Contact ๐Ÿ“ง

Feel free to contact me if you have any problems! zhaomy20@fudan.edu.cn

๐Ÿ˜ณ Difference between the paper ๐Ÿ˜ณ

  • Visual clue is removed since it does not help improving recognition performance.

  • Linguistical clue is trained with CTC loss.

  • Performance of CRNN is boosted from 65.2%/53.6%/39.8% to 65.7%/55.1%/38.9%.

evaling easy
[2022-11-19 16:59:48]	PSNR 21.74 | SSIM 0.8631	
save display images
sr_accuray_iter0: 65.66%
lr_accuray: 37.49%
hr_accuray: 76.41%
best_easy = 65.66%*
evaling medium
[2022-11-19 17:00:09]	PSNR 18.21 | SSIM 0.6330	
save display images
sr_accuray_iter0: 55.07%
lr_accuray: 21.40%
hr_accuray: 75.05%
best_medium = 55.71%
evaling hard
[2022-11-19 17:00:28]	PSNR 19.23 | SSIM 0.7065	
save display images
sr_accuray_iter0: 38.87%
lr_accuray: 21.15%
hr_accuray: 64.56%
best_hard = 38.87%*
saving best model

๐Ÿ”ฅ Training ๐Ÿ”ฅ

python3 main.py --arch="c3stisr" --test_model="CRNN" --batch_size=48 --STN  --sr_share --gradient  --use_distill --stu_iter=1 --vis_dir='C3-STISR-Final' --mask

๐Ÿ’ซ Testing ๐Ÿ’ซ

python3 main.py --arch="c3stisr" --test_model="CRNN" --batch_size=48 --STN  --sr_share --gradient  --use_distill --stu_iter=1 --vis_dir='C3-STISR-Final' --mask --go_test --resume='***'

๐Ÿ‘Š Performance ๐Ÿ‘Š

20220906130237

๐Ÿ˜† Citation ๐Ÿ˜†

If you find this project is useful for your research, please cite:

@inproceedings{zhao2022c3,
  title={C3-STISR: Scene Text Image Super-resolution with Triple Clues},
  author={Zhao, Minyi and Wang, Miao and Bai, Fan and Li, Bingjia and Wang, Jie and Zhou, Shuigeng},
  booktitle={IJCAI},
  pages={1707--1713},
  year={2022}
}

๐Ÿ˜‰ Related Works ๐Ÿ˜‰

ยท Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution [Paper] [Code]

ยท A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution [Paper] [Code]

ยท Scene Text Telescope: Text-Focused Scene Image Super-Resolution [Paper] [Code]

ยท Text Prior Guided Scene Text Image Super-resolution [Paper] [Code]

๐Ÿ‘ Special Thanks ๐Ÿ‘

Additionally thank JingyeChen for his help!

Our framework is based on TPGSR, STT, and TG.