Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers

Abstract

Scene text image super-resolution aims to improve readability by recovering text shapes from low-resolution degraded text images. Although recent developments in deep learning have greatly improved super-resolution (SR) techniques, recovering text images with irregular shapes, heavy noise, and blurriness is still challenging. This is because networks with Convolutional Neural Network (CNN)-based backbones cannot sufficiently capture the global long-range correlations of text images or detailed sequential information about the text structure. In order to address this issue, this paper proposes a Multi-task learning-based Text Super-resolution (MTSR) Network to approach this problem. MTSR is a multi-task architecture for image reconstruction and SR. It uses transformer-based modules to transfer complementary features of the reconstruction model, such as noise removal capability and text structure information, to the SR model. In addition, another transformer-based module using 2D positional encoding is used to handle irregular deformations of the text. The feature maps generated from these two transformer-based modules are fused to attempt improvement of the visual quality of images with heavy noise, blurriness, and irregular deformations. Experimental results on the TextZoom dataset and several scene text recognition benchmarks show that our MTSR significantly improves the accuracy of existing text recognizers.