Text Prior Guided Scene Text Image Super-Resolution (TIP 2023)

https://arxiv.org/abs/2106.15368

Jianqi Ma, Shi Guo, Lei Zhang
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

Recovering TextZoom samples

Environment:

Other possible python packages like pyyaml, cv2, Pillow and imgaug

Main idea

Single stage with loss

Multi-stage version

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 'TPGSR_ROOT/', place the pretrained weights from recognizer in 'TPGSR_ROOT/'.

Download the TextZoom dataset:

https://github.com/JasonBoy1/TextZoom

Train the corresponding model (e.g. TPGSR-TSRN):

chmod a+x train_TPGSR-TSRN.sh
./train_TPGSR-TSRN.sh
or
python3 main.py --arch="tsrn_tl_cascade" \       # The architecture
                --batch_size=48 \                # The batch size
                --STN \                          # Using STN net for alignment
		--mask \                         # Using the contour mask
		--use_distill \                  # Using the TP loss
		--gradient \                     # Using the Gradient Prior Loss
		--sr_share \                     # Sharing weights for SR Module
		--stu_iter=1 \                   # The number of interations in multi-stage version
		--vis_dir='vis_TPGSR-TSRN' \     # The checkpoint directory

Run the test-prefixed shell to test the corresponding model.

Adding '--go_test' in the shell file

Cite this paper:

@article{ma2021text,
title={Text Prior Guided Scene Text Image Super-resolution},
author={Ma, Jianqi and Guo, Shi and Zhang, Lei},
journal={IEEE Transactions on Image Processing},
year={2023}
}