Building super-resolution image generator for OCR accuracy improvement

Abstract

Super-resolving a low resolution (LR) document image can not only enhance the visual quality and readability of the text, but improve the optical character recognition (OCR) accuracy. However, even despite the ill-posed nature of image super-resolution (SR) problem, how do we treat the finer details of text with large upscale factors and suppress noises and artifacts at the same time, especially for low quality document images is still a challenging task. Thus, in order to boost the OCR accuracy, we propose a generative adversarial network (GAN) based framework in this paper, where a SR image generator and a document image quality discriminator are constructed. To obtain high quality SR document image, multiple losses are designed to encourage the generator to learn the structural properties of texts. Meanwhile, the quality discriminator is trained based on a relativistic loss function. Based on the proposed framework, the obtained SR document images not only maintain the details of textures but remove the background noises, which achieve better OCR performance on the public databases. The source codes and pre-trained models are available at https://gitlab.com/xujun.peng/doc-super-resolution.