@inproceedings{zhang-etal-2025-reviving,
    title = "Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration",
    author = "Zhang, Yuyi  and
      Zhang, Peirong  and
      Yang, Zhenhua  and
      Yan, Pengyu  and
      Shi, Yongxin  and
      Liu, Pengwei  and
      Guo, Fengjun  and
      Jin, Lianwen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1402/",
    pages = "28876--28892",
    ISBN = "979-8-89176-251-0",
    abstract = "Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historical Document Restoration (HDR) methods primarily focus on single modality or limited-size restoration, failing to meet practical needs.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 system 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 We believe this work represents a significant advancement in automated historical document restoration and contributes substantially to cultural heritage preservation.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."
}