Coarse-to-Fine Generative Model for Oracle Bone Inscriptions Inpainting (2024)

Abstract: Due to ancient origin, there are many incomplete characters in the unearthed Oracle Bone Inscriptions(OBI), which brings the great challenges to recognition and research. In recent years, image inpainting techniques have made remarkable progress. However, these models are unable to adapt to the unique font shape and complex text background of OBI. To meet these aforementioned challenges, we propose a two-stage method for restoring damaged OBI using Generative Adversarial Networks (GAN), which incorporates a dual discriminator structure to capture both global and local image information. In order to accurately restore the image structure and details, the spatial attention mechanism and a novel loss function are proposed. By feeding clear copies of existing OBI and various types of masks into the network, it learns to generate content for the missing regions. Experimental results demonstrate the effectiveness of our proposed method in completing OBI compared to several state-of-the-art techniques.

## introduction since the earliest discovery of oracle bone inscriptions(obi), over 5,000 distinct character forms have been identified, which have significantly advanced our comprehension of many characters' meanings. these deciphered obis provide invaluable historical information crucial for understanding various aspects of ancient chinese politics, society, religion, and more. recognizing and interpreting are important topics in the field of obi research. due to the lack of physical objects, the images of rubbings in the recorded books are the main carriers of research. however, some obis have suffered varying degrees of residual erosion and damage on their surface, resulting in a large number of incomplete fonts in * corresponding author: wangshibin@htu.edu.cn the inscriptions and rubbings seen today. with the rapid development of image generation technology, many image restoration problems difficult to solve in traditional methods have found new research avenues. the comprehensive application of artificial intelligence and other technologies has become a new research direction in the restoration of obis. @xcite proposed the pyramid context encoder network @xcite . it is based on the u-net structure and encodes and decodes contextual semantics to ensure visual and semantic consistency. @xcite developed the recurrent feature reasoning (rfr) network, featuring a plug-andplay rfr module and a knowledge consistent attention (kca) module. they infer the hole boundaries and capture the distant feature information. @xcite introduced a two-stage (coarse-tofine) model. it combines a local binary pattern (lbp) @xcite network and incorporates a new spatial attention mechanism. these methods have enhanced image processing. however, they only grasp limited connections between textures and edges. they fail to fully comprehend image semantics and complex structures. additionally, they overlook the interplay between global and local features. given the complexities behind incomplete fonts and unique font features, existing image restoration models struggle to effectively complete obi image inpainting tasks. to meet these challenges, we propose a twostage (coarse-to-fine) font inpainting network. our network incorporates a dual discriminator structure to capture both global and local image information. specifically, we employ a global discriminator to focus on the spatial correlation between damaged and undamaged regions. the local discriminator concentrates on the local patch information. to effectively understand the intrinsic features of the image, we introduce a novel loss function to accurately restore the structure and details. through extensive comparisons, our framework demonstrates state-of-the-art performance in obi image inpainting tasks. ## conclusion we propose the two-stage (coarse-to-fine) network for efficient obi image inpainting. this new framework consists of an enhanced lbp network and integrated dp and mlflf components. specifically, we design a novel dual discriminator network. the first stage lbp learning network adopts a u-net architecture, aimed at accurately predicting structural information in missing regions. this guides the second image inpainting network in better filling missing pixels. in the second stage image generation network, we employ dual discriminators to complete the masked regions. compared to several state-of-the-art methods, experimental results demonstrate the effectiveness of dp and mlflf components in the proposed method in completing obi image inpainting tasks. in the future, we plan to further develop our network to achieve more powerful functions, such as increasing the speed, realizing editing functions, and improving the efficiency of paleographers. our goal is to solve the problem of more complex noise or higher mask coverage. we believe that our two-stage (coarse-to-fine) generation model can be extended to very high-resolution coloring applications by improving the first-stage generation results.