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[bibtex]@InProceedings{Park_2025_WACV, author = {Park, Dongwoo and Ko, Suk Pil}, title = {NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {2432-2441} }
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
Abstract
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.
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