A comprehensive repository of awesome Scene Text Image Super-Resolution (STISR) methods based on deep learning.
- The "@¥" means that the paper has been Accepted by ¥.
- The "*" means that the work involves Vision Transformer for STISR.
Method | Year | Venue | Accuracy of ASTER | |||
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easy | medium | hard | weighted-avg | |||
TSRN | 2020 | ECCV | 75.1% | 56.3% | 40.1% | 58.3% |
TEAN | 2023 | TCSVT | 80.4% | 64.5% | 45.6% | 64.6% |
- Accuracy of different methods using ASTER in the Textzoom dataset.
Year | Venue | Title | Type | Link |
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2020 | ECCV | TextZoom | Real | Dataset |
2016 | CVPR | SynthText | Syntic | Dataset |
2015 | ICDAR | ICDAR2015-TextSR | Syntic | Dataset |
2014 | NIPSW | Synth90k | Syntic | Dataset |
- The use of datasets Synth90K and SynthText for scene text image super-resolution requires manual creation.
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This repository is scheduled to be updated regularyly in accordance with schedules of major AI or CV related Conferences and Journals.
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Welcome to post Pull-requests to update the list above.
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Thanks to all the above researchers' contribution.