Scene Text Image Super-Resolution with Visual Text Cues Transfer and Enhancement

Scene text image super-resolution (STISR) aims to improve the visual clarity of the text in low-resolution scene images. Due to the intrinsic lack of detailed text appearance information in the low-resolution input image, the text images generated by most STISR methods often contain varying degrees of distortion or loss of text details. To improve the quality of the super-resolved text image, we propose a novel Visual Text Cues Transfer-based scene text image Super-Resolution Network (VTCTSRN). The network introduces synthetic text prototypes to provide high-resolution, supplementary visual cues of the text to be reconstructed, and leverages a novel visual text cues transfer mechanism to adaptively fuse complementary visual characteristics of the prototype and the input text to help recover clear and accurate text details. Additionally, we propose dynamic attentional sequential recurrent blocks for the text reconstruction pipeline and introduce effective loss function term to enhance text representation, leading to a marked improvement in the quality of generated text images. Experimental results demonstrate that the proposed network significantly enhances the readability of low-resolution text images and establishes new state-of-the-art performance on the mainstream STISR benchmark.