Problem
Research questions and friction points this paper is trying to address.
Manchu historical documents
document restoration
glyph-level fidelity
low-resource
pixel reconstruction
Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents
🤖 AI Summary
This study addresses the challenges of severe degradation and training data scarcity in historical Manchu documents, where conventional pixel-level restoration often compromises glyph structure. To overcome these limitations, this work proposes a retrieval-guided, glyph-aware image inpainting framework that transcends traditional pixel reconstruction paradigms. By retrieving relevant glyph exemplars to explicitly introduce structural priors, the method integrates retrieval-augmented generation with glyph-aware modeling, providing precise guidance for character recovery in low-resource scenarios. Experimental results demonstrate that the proposed framework significantly improves both restoration quality and glyph fidelity on historical Manchu documents, effectively resolving the persistent issue of structural identity loss during the restoration process.
📝 Abstract
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 Extensive experiments on Manchu historical documents demonstrate that the proposed approach improves both image restoration quality and glyph-level fidelity compared with existing restoration methods.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highlight the importance of incorporating character-aware structural priors for reliable restoration of low-resource historical documents.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Retrieval-Guided Restoration
Glyph-Aware
Low-Resource Documents
Historical Document Restoration
Structural Prior
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