Chinese Text Image Super-Resolution Network Using SwinIR and Text-Aware Loss

Scene Text Image Super-Resolution (STISR) is a challenging task in computer vision. Existing STISR methods primarily focus on English characters, with insufficient attention given to the more complex Chinese characters. To address this, we propose SwinTISR, an STISR method based on SwinIR, specifically designed for Chinese text images. To better reconstruct the intricate stroke structures of Chinese characters, we introduce a text-aware loss [Formula: see text] that combines stroke-focused features and text location guidance. Additionally, Canny edge maps are utilized as auxiliary supervision to further enhance the visual quality and readability. Extensive experiments on the STISR benchmarks TextZoom and Real-CE demonstrate the effectiveness and superiority of the proposed SwinTISR. Furthermore, we enhance the Chinese STISR dataset Real-CE by incorporating the Multi-scale Homography Network.