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.

In recent years, high-fidelity three-dimensional (3D) oracle bone models (3D-OBMs) have received extensive attention from oracle bone experts due to their unparalleled reducibility to real oracle bone. In the research process of 3D-OBMs, the first procedure is to extract oracle bone inscriptions (OBIs) from the model to form individual oracle bone characters (OBCs). However, the manual extraction of OBIs is a time-consuming and labor-intensive task that relies heavily on oracle bone knowledge. To address these problems, we propose a texture-mapping-based OBI extractor (tm-OBIE), which leverages the symmetrical characteristics of the texture mapping process and is able to extract 3D-OBIs from 3D-OBMs saved as a wavefront file. The OBIs in the texture file were first located using a trained 2D object detector. After that, the 3D mesh area, where the OBIs are located, was obtained using an inverse texture mapping method. Thirdly, a specific 2D plane was fitted using the centroid of triangular faces in the flat regions of the mesh via a singular value decomposition (SVD) method. Finally, by measuring the distances between the triangle meshes and the fitted plane, the meshes of the 3D-OBIs were obtained. This paper verifies the feasibility of this method via experiments and analyzes the possibility of using the algorithm framework for extracting other ancient characters from their corresponding 3D models.

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.

Automated bamboo slip character detection supports cultural heritage digitization, but it remains difficult because excavated manuscripts contain dense, slender, fragmented, and degraded characters. Faded ink, wood grain, cracks, and stains often resemble genuine strokes, causing missed detections, false positives, and unstable boundary localization. To address these challenges, we propose a Fine-Grained Bidirectional Multi-Scale YOLO (FBMS-YOLO) for extreme-aspect-ratio bamboo slip character detection. The framework integrates three task-oriented designs: an Attention-enhanced Refinement and Context Network (ARCNet) for structural representation of slender strokes, a Fine-grained Bidirectional Feature Pyramid Network (FB-FPN) for preserving high-resolution spatial details through adaptive cross-scale fusion, and a Dynamic Focusing and Boundary-Penalty Intersection over Union loss (DFB-IoU) for robust localization under ambiguous boundaries. Experiments on the DeepJiandu dataset show that FBMS-YOLO improves the YOLOv11-n baseline from 92.8 to 94.4% in mAP\(_{50}\) and from 55.0 to 56.8% in mAP\(_{50:95}\). Additional component analysis, ablation studies, and cross-dataset evaluation on oracle bone inscription images demonstrate the effectiveness and transferability of the proposed framework. These results indicate that FBMS-YOLO provides an accuracy-oriented solution for dense character localization in degraded historical manuscripts.