Text-Attentional Conditional Generative Adversarial Network For Super-Resolution Of Text Images

Text in natural scene images are often faced with low-resolution problem, which brings significant difficulties to many text-related tasks such as text detection and recognition. In this paper, we propose a novel text-attentional Conditional Generative Adversarial Network (cGAN) model for text image super-resolution (SR). The model enhances the original cGAN by introducing effective channel and spatial attention mechanisms based on the proposed Residual Dense Channel Attention Block and text/non-text segmentation information, which focus the model on the text regions instead of the background of the image to learn more effective representations of text and achieve better text super-resolution result. The proposed model achieves state-of-the-art performances on public text image super-resolution dataset.