发表机构
Wuhan University of Technology（武汉理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对甲骨文破译辅助，提出FROD方法，结合特征匹配门控分割、残差去噪扩散模型及字体风格细化，在字形不相交数据集上Top-1识别率较OBSD绝对提升3.8%。
AI 中文摘要
甲骨文（OBS）作为最早的中国文字体系之一，在中国词源学研究中占有重要地位。传统破译工作高度依赖领域专家，他们通过语义语境和结构演变来分析文字。为辅助这一劳动密集型过程，我们将甲骨文破译辅助任务构建为跨时代图像翻译任务，并提出FROD（特征匹配残差去噪甲骨文破译）方法。尽管许多甲骨文字与现代对应字存在显著差异，但它们通常在部首层面保留局部拓扑不变量。在训练过程中，FROD利用快速特征匹配提供门控分割监督：具有足够匹配度的配对样本按块处理以对齐细粒度部首，而低相似度配对则整体训练以避免错配伪影。此外，残差去噪扩散模型（RDDM）联合估计噪声和残差信号，从而减少标准扩散模型中常见的位偏移和笔画紊乱问题。最后，多阶段字体风格细化网络通过消除边缘噪声和稳定笔画结构来精炼生成图像。在我们增强的字形不相交数据集上，FROD取得了比评估基线更高的Top-1识别准确率，相较于OBSD绝对提升了3.8%。
英文摘要
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.
Comments15 pages, 5 figures, 3 tables. Accepted at ICONIP 2026
