提出新方法与数据集，实现全流程历史文献自动修复
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration
- 分三阶段模拟专家流程：定位损伤、预测上下文、修复图像
- 严重破损文档的OCR准确率从46.83%提升至94.25%（人机协作）
- 适用于文化遗产保护、数字典籍重建等场景
历史文献是宝贵的文化遗产，但长期受撕裂、水渍和氧化影响而严重退化。现有历史文献修复（HDR）方法多局限于单模态或小范围修复，难以满足实际需求。为此，我们构建了全页HDR数据集（FPHDR），包含1,633张真实图像和6,543张合成图像，提供字符级与行级位置标注及不同损伤等级的字符注释。同时提出自动化修复方案AutoHDR，采用三阶段流程：基于OCR的损伤定位、视觉-语言上下文文本预测、补丁自回归外观修复。其模块化设计支持灵活的人机协作，在各阶段可介入优化。实验表明，处理严重损坏文档时，该方法将OCR准确率从46.83%提升至84.05%，经人机协同进一步达94.25%。本工作推动了自动化历史文献修复的发展，助力文化遗产保护。模型与数据集已在GitHub开源。
原文摘要 · Abstract (English)
Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historical Document Restoration (HDR) methods primarily focus on single modality or limited-size restoration, failing to meet practical needs. To fill this gap, we present a full-page HDR dataset (FPHDR) and a novel automated HDR solution (AutoHDR). Specifically, FPHDR comprises 1,633 real and 6,543 synthetic images with character-level and line-level locations, as well as character annotations in different damage grades. AutoHDR mimics historians' restoration workflows through a three-stage approach: OCR-assisted damage localization, vision-language context text prediction, and patch autoregressive appearance restoration. The modular architecture of AutoHDR enables seamless human-machine collaboration, allowing for flexible intervention and optimization at each restoration stage. Experiments demonstrate AutoHDR's remarkable performance in HDR. When processing severely damaged documents, our method improves OCR accuracy from 46.83% to 84.05%, with further enhancement to 94.25% through human-machine collaboration. We believe this work represents a significant advancement in automated historical document restoration and contributes substantially to cultural heritage preservation. The model and dataset are available at https://github.com/SCUT-DLVCLab/AutoHDR.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。