EpiSAM通过上下文感知提升石刻文字分割精度，解决模糊边界难题。
EpiSAM: Character Segmentation in Challenging Stone Inscriptions
- 利用邻近字符上下文信息，联合预测目标字符及其相邻字符
- 在扩展的石刻数据集上实现优于基线的分割性能
- 适用于低对比度、表面磨损等复杂石刻分析场景
石刻是历史与语言学的重要资料，但因表面不规则、风化和视觉对比度低，自动化分析仍面临巨大挑战。传统文档与手写体分析方法在此类场景下表现不佳。本文提出将字符检测作为核心策略，构建EpiSAM——一种提示引导的Transformer框架，用于石刻中的字符分割。不同于孤立处理字符，EpiSAM采用新颖的邻域感知策略，显式预测目标字符及其相邻字符，利用上下文信息解决边界模糊问题，提升掩码生成质量，实现更精确的字符分割。此外，我们扩充了现有石刻数据集，添加密集多边形标注，支持东南亚铭文研究。实验表明，EpiSAM在多个基准上持续优于现有方法，并在复杂铭文场景中展现出强大的零样本泛化能力。
原文摘要 · Abstract (English)
Stone inscriptions are invaluable sources of historical and linguistic knowledge, yet their automated analysis remains a major challenge due to surface irregularities, erosion, and low visual contrast. Conventional document and handwriting analysis techniques fail to perform well in these scenarios. In this work, we propose character detection as a core strategy for robust inscription analysis. We introduce EpiSAM, a prompt-guided transformer framework for character segmentation in stone inscriptions. Rather than treating characters in isolation, EpiSAM employs a novel neighbor-aware strategy, explicitly predicting adjacent characters alongside the target. These contextual cues resolve boundary ambiguities, improving mask generation and enabling more accurate character segmentation. Furthermore, we expand an existing stone inscription dataset by adding dense polygonal annotations for characters, thereby enabling comprehensive research on Southeast Asian epigraphy. Experimental results show that EpiSAM achieves consistent improvements over existing baselines, while also exhibiting strong zero-shot generalization in challenging epigraphic scenarios.
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