This analysis demonstrates improved peak signal-to-noise ratio in text recognition using image super-resolution and optical character recognition methods.
Text recognition in real-life images poses a challenging task due to blur, distortion, and low resolution. This work presents an innovative method integrating image super-resolution, image restoration, and optical character recognition techniques to enhance text recognition in real-life photographs. We specifically reviewed the processing of the TextZoom dataset and utilized transfer learning on an improved version of the image super-resolution model, SwinIR. The findings of our experiment show that our text recognition scores are better than the current best scores, and there is a significant rise in the peak signal-to-noise ratio while dealing with deformed low-resolution images from the TextZoom dataset. This approach outperforms earlier research in the domain of scene text image super-resolution and offers a promising resolution for text recognition in real-life images. The code can be accessed at this location: https://github.com/Phimanu/TextSR
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Hildebrandt et al. (2025) studied this question.
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