Abstract: Current image inpainting methods are often perform poorly on ancient text images, producing results with blurred textures or incomplete structural content. To address this problem, we propose an inpainting algorithm for ancient text images via edge guide and laplacian pyramid decomposition. We first use an edge restoration module to restore the edge structure for the damaged regions and construct an edge-guided map. Then, we employ the pre-trained text learning module to restore the local damaged regions and obtain a local inpainting image, which is decomposed into a content image and a detail map through laplacian pyramid transform. At last, in the Laplace pyramid restoration module, the content restoration module is used to progressively repair the image according to the low-level and high-level features of the image. The content restoration module introduces a dual cross-encoder and multi-scale fusion blocks to prompt the module to obtain more effective feature information and generate desirable image inpainting results. The superiority quantitative results on the benchmark dataset demonstrate the effectiveness and feasibility of the proposed method, that peak signal to noise ratio (PSNR) is 34.322 dB, structural similarity (SSIM) is 0.970 and root mean square error (RMSE) is 5.203.