SRSTS & SRSTS v2

SRSTS is an effective single shot text spotter which decouples text recognition from detection. It's published in ACM Multimedia 2022. SRSTS v2 the extended version which redesigns the text detection module to enable the collaborative optimization and mutual enhancement between text detection and recogntion.

SRSTS

Training

To be released.

Testing

You can download benchmarks from BaiduNetDisk(code: cctq) or Google Drive and change the root in config file.

You can download the trained models from BaiduNetDisk (code: hf8a) or Google Drive.

Performance Evaluation

python scripts/test_model.py --config-file="path to the config file" --ckpt="path to your ckpt"

Evalute on Total-Text:

python scripts/test_model.py --config-file="./configs/evaluation/v1/tt.yaml" --ckpt="./save_models/tt_best.pth" --lexicon_type=0 # 0 refers none, 1 refers to full

Evalute on ICDAR 2015:

python scripts/test_model.py --config-file="./configs/evaluation/v1/ic15.yaml" --ckpt="./save_models/ic15_best.pth"  --lexicon_type=0 #0 refers none, 1 refers generic

Evalute on CTW 1500:

python scripts/test_model.py --config-file="./configs/evaluation/v1/ctw.yaml" --ckpt="./save_models/ctw_best.pth"  --lexicon_type=0 #0 refers none, 1 refers full

SRSTS v2

To be released.

Citation

Please cite the related works in your publications if it helps your research:

@inproceedings{wu2022decoupling,
  title={Decoupling recognition from detection: Single shot self-reliant scene text spotter},
  author={Wu, Jingjing and Lyu, Pengyuan and Lu, Guangming and Zhang, Chengquan and Yao, Kun and Pei, Wenjie},
  booktitle={ACM MM},
  pages={1319--1328},
  year={2022}
}

@misc{wu2023single,
      title={Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition}, 
      author={Jingjing Wu and Pengyuan Lyu and Guangming Lu and Chengquan Zhang and Wenjie Pei},
      year={2023},
      eprint={2207.07253},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}