HiGAS: Weakly-Supervised Oracle Bone Inscription Component Segmentation via Hierarchical Graph Alignment

Oracle Bone Inscription (OBI) recognition is of great scientific importance for deciphering ancient Chinese characters, a large portion of which still remain undeciphered. Since complex OBI characters are usually composed of components with independent semantics, accurately uncovering the topological relationships between characters and components provides crucial support for character decipherment. However, existing methods suffer from high annotation costs, while significant structural variants of the same component make it challenging to match against standard forms. To address these issues, we propose HiGAS, a weakly supervised method for OBI component segmentation via hierarchical graph alignment. Using standard components as topological references, HiGAS constructs bottom-up hierarchical undirected graphs across segment, sub-stroke, and stroke levels for both input characters and reference components. Through within-level edge matching and cross-level topological priors, HiGAS effectively alleviates topological discontinuity and under-segmentation caused by local feature overfitting under weak supervision. Combined with a dynamic edge selection strategy and joint optimization of classification and segmentation completeness losses, the model predicts component presence probabilities while performing fine-grained edge-level segmentation. Extensive experiments on the public Component-$20$ and our extended Component-$50$ datasets demonstrate that HiGAS achieves a top-$5$ hit rate of $96.77\%$ in multi-component recognition while delivering segmentation results closest to expert annotations.