Improving Text Image Resolution using a Deep Generative Adversarial Network for Optical Character Recognition

Abstract

Optical character recognition (OCR) has been widely studied in previous work. Except for the models used, the recognition accuracy depends most on the resolution of the image to be recognized. To enhance OCR performance, this paper proposes an approach based on a generative adversarial network to improve text image resolution. Our approach uses a perceptual loss function that consists of an adversarial loss, a content loss and an L1 loss. The adversarial loss and the L1 loss are used to ensure the generated super-resolved images are closer to the ground truth high-resolution images. Meanwhile, the content loss is used to ensure the generated super-resolved images and the input low-resolution images have similar features on the basis of perceptual instead of pixel similarity. To evaluate the proposed approach, we compare the recognition accuracies before and after improving the resolution of both English and Chinese text images. The results show that the recognition accuracies on the super-resolved text images obtained with our approach are significantly higher than those on the low-resolution images without processing.