Papers › NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
Dongwoo Park, Suk Pil Ko
Scene text image super-resolution (STISR) enhances the resolution and quality of low-resolution images. Unlike previous studies that treated scene text images as natural images, recent methods using a text prior (TP), extracted from a pre-trained text recognizer, have shown strong performance. However, two major issues emerge: (1) Explicit categorical priors, like TP, can negatively impact STISR if incorrect. We reveal that these explicit priors are unstable and propose replacing them with Non-CAtegorical Prior (NCAP) using penultimate layer representations. (2) Pre-trained recognizers used to generate TP struggle with low-resolution images. To address this, most studies jointly train the recognizer with the STISR network to bridge the domain gap between low- and high-resolution images, but this can cause an overconfidence phenomenon in the prior modality. We highlight this issue and propose a method to mitigate it by mixing hard and soft labels. Experiments on the TextZoom dataset demonstrate an improvement by 3.5%, while our method significantly enhances generalization performance by 14.8\% across four text recognition datasets. Our method generalizes to all TP-guided STISR networks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | TextZoom | NCAP | ASTER Overall Accuracy | 68.1 | #1 of 1 | Archive leaderboard | report |
| Image Super-Resolution | TextZoom | NCAP | Average Accuracy | 63.7 | #1 of 1 | Archive leaderboard | report |
| Image Super-Resolution | TextZoom | NCAP | CRNN Overall Accuracy | 58.3 | #1 of 1 | Archive leaderboard | report |
| Image Super-Resolution | TextZoom | NCAP | MORAN Overall Accuracy | 64.6 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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