The field of STISR has marked a significant advancement in text recognition and image enhancement. Traditional approaches often struggle to improve text legibility in low-resolution images, constraining their efficacy in applications such as document scanning and autonomous driving. The innovative STISR methodology expounded upon in this article leverages deep learning techniques to achieve substantial improvements. Rigorous testing has revealed marked improvements in text legibility and image clarity. These advances hold great potential to increase the accuracy of text recognition systems and generate high-quality text images, providing enhanced support for subsequent text recognition tasks.

Scene text image superresolution (STISR) aims to enhance the resolution of images containing text within a scene, making the text more readable and easier to recognize. This technique has broad applications in numerous fields such as autonomous driving, document scanning, image retrieval, and so on. However, most existing STISR methods have not fully exploited the multiscale structural and semantic information within scene text images. As a result, the restored text image quality is not sufficient, significantly impacting subsequent tasks such as text detection and recognition. Hence, this article proposes a novel scheme that leverages multiscale structural and semantic priors to efficiently guide text semantic restoration, ultimately yielding high-quality text images. First, a multiscale interaction attention (MSIA) module is designed to capture location-specific details of various-scale structural features and facilitate the recovery of semantic information. Second, a multiscale prio...