Reviving Cultural Heritage: A Novel Approach for Comprehensive
  Historical Document Restoration
Authors: name: Yuyi Zhang, name: Peirong Zhang, name: Zhenhua Yang, name: Pengyu Yan, name: Yongxin Shi, name: Pengwei Liu, name: Fengjun Guo, name: Lianwen Jin

Historical documents represent an invaluable cultural heritage, yet have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 Existing Historical Document Restoration (HDR) methods primarily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a significant advancement in automated historical document restoration and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.