Scene Text Image Super-Resolution Reconstruction Based on Perceiving Multi-Domain Character Distance

Scene text image super-resolution (STISR) aims to enhance the resolution and legibility of text in low-resolution images. In cases of spatial deformation or low-resolution text images, the lack of details in text regions and the difficulty in aligning semantic cues and visual features with character position make it difficult to recognize text effectively. In order to address these challenges, this paper proposes a perceiving multi-domain character distance for scene text image super-resolution method (PMDC), which improves the image text region and edge texture details. Firsly, the visual and semantic features are extracted by using the asymmetric convolution module along with the semantic prior module. Then the enhanced position coding is obtained by the character distance perception module to perceive the distance change and semantic similarity between characters. Finally, the guiding cues and visual features are combined to restructure the pixels and generate a super-resolution text image. In comparison to TATT, experimental results from the public dataset TextZoom showed an increase of 0.11 dB in the fidelity of the peak signal-to-noise ratio index. This improvement effectively enhances the clarity of the text area and the detailed edge texture. Additionally, the recognition accuracy was improved by 1.4%, which effectively enhances the readability of the text image. © 2024 Chinese Institute of Electronics. All rights reserved.