Reviving Cultural Heritage: A Novel Approach for Comprehensive
  Historical Document Restoration
Historical documents represent an invaluable cultural heritage, yet have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 Existing Historical Document Restoration (HDR) methods primarily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a significant advancement in automated historical document restoration and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.
686cc8bc364e2ad167eb54e3
Yuyi Zhang
686cc8bc364e2ad167eb54e4
Peirong Zhang
686cc8bc364e2ad167eb54e5
Zhenhua Yang
686cc8bc364e2ad167eb54e6
Pengyu Yan
686cc8bc364e2ad167eb54e7
Yongxin Shi
686cc8bc364e2ad167eb54e8
Pengwei Liu
686cc8bc364e2ad167eb54e9
Fengjun Guo
686cc8bc364e2ad167eb54ea
Lianwen Jin
Reviving Cultural Heritage: A Novel Approach for Comprehensive
  Historical Document Restoration
Historical documents represent an invaluable cultural heritage, yet have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 Existing Historical Document Restoration (HDR) methods primarily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.
AutoHDR, a three-stage automated solution, significantly enhances historical document restoration through OCR-assisted damage localization, vision-language context prediction, and patch autoregressive appearance restoration, improving OCR accuracy and enabling human-machine collaboration.
OCR-assisted damage localization vision-language context text prediction patch autoregressive appearance restoration modular architecture human-machine collaboration