A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)
https://arxiv.org/abs/2203.09388
Jianqi Ma, Zhetong Liang, Lei Zhang
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China & OPPO Research
Recovering TextZoom samples
Environment:
Other possible python packages like pyyaml, cv2, Pillow and imgaug
Main idea
The pipeline
TP Interpreter
Configure your training
Download the pretrained recognizer from:
Aster: https://github.com/ayumiymk/aster.pytorch
MORAN: https://github.com/Canjie-Luo/MORAN_v2
CRNN: https://github.com/meijieru/crnn.pytorch
Unzip the codes and walk into the 'TATT_ROOT/', place the pretrained weights from recognizer in 'TATT_ROOT/'.
Download the TextZoom dataset:
https://github.com/JasonBoy1/TextZoom
Train the corresponding model (e.g. TPGSR-TSRN):
chmod a+x train_TATT.sh
./train_TATT.sh
Run the test-prefixed shell to test the corresponding model.
Adding '--go_test' in the shell file
Cite this paper:
@inproceedings{ma2022text,
title={A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution},
author={Ma, Jianqi and Liang, Zhetong and Zhang, Lei},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5911--5920},
year={2022}
}