The Oracle Bone Characters are the earliest known ancient Chinese characters and are an important record of the civilization of ancient China.The interpretation of the Oracle Bone Characters is challenging and requires professional knowledge from ancient Chinese language experts. Although some works have utilized deep learning to perform image detection and recognition using the Oracle Bone Characters, these methods have proven difficult to use for the interpretation of uninterpreted Oracle Bone Character images. Inspired by the prior knowledge that there exists a relation between glyphs from Oracle Bone Character images and images of modern Chinese characters, we proposed a method of image translation from Oracle Bone Characters to modern Chinese characters based on the use of a generative adversarial network to capture the implicit relationship between glyphs from Oracle Bone Characters and modern Chinese characters. The image translation process between Oracle Bone Characters and the modern Chinese characters forms a symmetrical structure, comprising an encoder and decoder. To our knowledge, our symmetrical image translation method is the first of its kind used for the task of interpreting Oracle Bone Characters. Our experiments indicated that our image translation method can provide glyph information to aid in the interpretation of Oracle Bone Characters.

Oracle bone inscriptions are China’s earliest writing system, dating back 3000 years. Usually carved on bones and tortoise shells, they serve as invaluable records of early Chinese civilisation. Due to erosion, a large number of oracle bones have been fragmented into small pieces. This fragmentation often results in incomplete sentences and the loss of contextual information, which poses significant challenges for accurate interpretation and digital reconstruction. Previous rejoining methods mainly rely on edge patterns, while determining bone-level associations through contextual information remains largely unsolved. To address this issue, we present the first public benchmark for predicting bone-level associations of oracle bone inscription sentences. We also propose a novel multi-modal deep learning method to predict associations between sentence pairs that achieves competitive performance. The proposed dataset and method aim to assist researchers in identifying likely associations among fragments, thereby facilitating the reconstruction and understanding of damaged oracle bone inscription texts.

Oracle bones are artifacts of great significance and value in the study of Chinese history and culture. Because of soil and bacterial erosion, bones become fragile, and the inscriptions on the surface become blurred, resulting in the loss of historical information on the bones. In early times, scholars often used adhesives for bonding and reinforcement, whereas in modern times, organic and inorganic materials have been used as reinforcement for oracle bones. In this study, the surface of oracle bone was protected and reinforced by a new silicone coupling reagent that could self-polymerize in the format of colorless solution with good antimicrobial properties. The DESPMA was applied to the surface of oracle bone by dropwise addition and curing, effectively protecting it from bacteria and slowing down the yellowing process. The results showed that the reagent could significantly improve the antimicrobial properties of bone samples and reduce the yellowing and discoloration caused by bacterial attack. The reagent barely affected the appearance of the bone samples. These findings are promising and valuable for effective application in bone protection and utilization.