Text Image Inpainting Based on Diffusion Model

Text image inpainting aims to inpaint the structural images of corrupted or damaged text caused by environmental or anthropogenic factors, thereby enhancing the accuracy of scene text recognition. Currently, the primary approach involves the utilization of discriminative Convolutional Neural Network (CNN) supplemented by various forms of text-guided methodologies for inpainting. However, when confronted with severely damaged images, the generative capability of these methods often falls short of achieving ideal inpainting results due to the limited semantic information extracted from the original images. Consequently, we propose a Text Image Inpainting Method based on Diffusion Models (TIDM). This method leverages concise semantic guidance to direct the generative neural network of the diffusion model in inpainting defective text images. To validate the effectiveness of TIDM, we conducted experimental verification on the TII-ST dataset. The experimental results demonstrate that TIDM outperforms the current state-of-the-art methods in terms of text recognition accuracy and image fidelity.