超越像素重建：面向低资源满文历史文档的检索引导字形感知修复
Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents
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中文总结 AI 辅助
针对满文历史文档修复中像素级重建忽略字形结构的问题，提出检索引导的字形感知修复框架，通过引入字形级结构知识提升低资源条件下的修复质量与字形保真度。
中文摘要 AI 辅助
满文历史文档保存了宝贵的语言和文化遗产，然而其数字化受到严重退化以及配对训练数据稀缺的阻碍。现有的文档修复方法主要优化像素级重建，这虽然能产生视觉上看似合理的结果，却未能保留满文字形的结构同一性。为解决这一局限，我们提出了一种检索引导的字形感知修复框架，该框架通过显式融入字形级结构知识，超越了像素重建。我们的方法检索相关的字形示例以在修复过程中提供结构引导，并将这些信息整合到重建流程中，从而在低资源条件下改善退化字符结构的恢复。在满文历史文档上的大量实验表明，与现有修复方法相比，所提出的方法同时提升了图像修复质量和字形级保真度。这些结果凸显了在低资源历史文档的可靠修复中融入字符感知结构先验的重要性。
英文摘要
Historical Manchu documents preserve invaluable linguistic and cultural heritage, yet their digitization is hindered by severe degradations and the scarcity of paired training data. Existing document restoration methods primarily optimize pixel-level reconstruction, which can produce visually plausible results while failing to preserve the structural identity of Manchu glyphs. To address this limitation, we propose a retrieval-guided glyph-aware restoration framework that goes beyond pixel reconstruction by explicitly incorporating glyph-level structural knowledge. Our method retrieves relevant glyph exemplars to provide structural guidance during restoration and integrates this information into the reconstruction process, improving the recovery of degraded character structures under low-resource conditions. Extensive experiments on Manchu historical documents demonstrate that the proposed approach improves both image restoration quality and glyph-level fidelity compared with existing restoration methods. These results highlight the importance of incorporating character-aware structural priors for reliable restoration of low-resource historical documents.
发表机构
- Beijing Jiaotong University（北京交通大学）
- University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。
