FROD: Feature Matching Residual Denoising Oracle Bone Decipher

2026-09-15

AI Summary

为辅助甲骨文释读，提出FROD方法，通过跨时代图像转换、快速特征匹配及残差去噪扩散模型减少位置漂移和笔画混乱问题。

Abstract

Oracle bone script (OBS), one of the earliest Chinese writing systems, plays an important role in the study of Chinese etymology. Traditional decipherment relies heavily on domain experts who analyze characters through semantic context and structural evolution. To assist this labor-intensive process, we formulate OBS decipherment assistance as a cross-era image translation task and propose FROD (Feature Matching Residual Denoising Oracle Bone Decipher). Although many OBS characters differ substantially from their modern counterparts, they often preserve local topological invariants at the radical level. During training, FROD leverages fast feature matching to provide gated segmentation supervision: paired samples with sufficient matches are processed patch-wise to align fine-grained radicals, whereas low-similarity pairs are trained holistically to avoid mismatched artifacts. In addition, a Residual Denoising Diffusion Model (RDDM) jointly estimates noise and residual signals, thereby reducing the positional drift and stroke disorder commonly observed in standard diffusion models. Finally, a multi-stage font stylization refinement network refines the generated images by eliminating edge noise and stabilizing stroke structures. On our augmented character-disjoint dataset, FROD achieves higher Top-1 recognition accuracy than the evaluated baselines, with a 3.8% absolute gain over OBSD.

Problem

• Oracle Bone Script
• Decipherment
• Feature Matching
• Residual Denoising
• Image Translation

Innovation

• Feature Matching
• Residual Denoising Diffusion Model
• Gated Segmentation Supervision