An Auto-Encoder of Inscription Character Inpainting based on Branch Convolutional Channel Attention Module

Abstract

Many inscriptions suffer severe damage as a result of artificial or natural causes, necessitating computer repair and protection. There isn’t a publicly accessible dataset of inscriptions, and the majority of character inpainting algorithms that are currently in use are directly derived from common image inpainting algorithms , which have insufficient feature extraction capabilities for the character of inscriptions and don’t have metrics for the weights of obscured and unobscured regions. We developed a Character Auto-Encoder (CAE) to improve the inpaint-ing capabilities of inscription characters and solve the aforementioned issues. The down-sampling module is replaced by the Branch Convolutional Channel Attention Module (BCCAM), which employs a branching structure to enhance the model’s capacity for representation and weights various areas of the character. First, we compared other inpainting models using the inscription dataset, and the CAE’s peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increased by 0.82 and 0.016, respectively, compared to the baseline model. Next, we compared the Handwritten Chinese Character Inpainting Generative Adver-sarial Network (HCCI-GAN) with the handwritten Chinese character dataset, and the CAE’s design was relatively more ingenious and simpler.