EA-GAN: restoration of text in ancient Chinese books based on an example attention generative adversarial network

Ancient Chinese books are of great significance to historical research and cultural inheritance. Unfortunately, many of these books have been damaged and corroded in the process of long-term transmission. The restoration by digital preservation of ancient books is a new method of conservation. Traditional character restoration methods ensure the visual consistency of character images through character features and the pixels around the damaged area. However, reconstructing characters often causes errors, especially when there is large damage in critical locations. Inspired by human’s imitation writing behavior, a two-branch structure character restoration network EA-GAN (Example Attention Generative Adversarial Network) is proposed, which is based on a generative adversarial network and fuses reference examples. By referring to the features of the example character, the damaged character can be restored accurately even when the damaged area is large. EA-GAN first uses two branches to extract the features of the damaged and example characters. Then, the damaged character is restored according to neighborhood information and features of the example character in different scales during the up-sampling stage. To solve problems when the example and damaged character features are not aligned and the convolution receptive field is too small, an Example Attention block is proposed to assist in restoration. Qualitative and quantitative analysis experiments are carried out on a self-built dataset MSACCSD and real scene pictures. Compared with current inpainting networks, EA-GAN can get the correct text structure through the guidance of the additional example in the Example Attention block. The peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) value increased by 9.82% and 1.82% respectively. The learned perceptual image patch similarity (LPIPS) value calculated by Visual Geometry Group (VGG) network and AlexNet decreased by 35.04% and 16.36% respectively. Our method obtained better results than the current inpainting methods. It also has a good restoration effect in the face of untrained characters, which is helpful for the digital preservation of ancient Chinese books.