title: Predicting the Original Appearance of Damaged Historical Documents
summary: Historical documents encompass a wealth of cultural treasures but suffer from
  severe damages including character missing, paper damage, and ink erosion over
  time. However, existing document processing methods primarily focus on
  binarization, enhancement, etc., neglecting the repair of these damages. To
  this end, we present a new task, termed Historical Document Repair (HDR), which
  aims to predict the original appearance of damaged historical documents. To
  fill the gap in this field, we propose a large-scale dataset HDR28K and a
  diffusion-based network DiffHDR for historical document repair. Specifically,
  HDR28K contains 28,552 damaged-repaired image pairs with character-level
  annotations and multi-style degradations. Moreover, DiffHDR augments the
  vanilla diffusion framework with semantic and spatial information and a
  meticulously designed character perceptual loss for contextual and visual
  coherence. Experimental results demonstrate that the proposed DiffHDR trained
  using HDR28K significantly surpasses existing approaches and exhibits
  remarkable performance in handling real damaged documents. Notably, DiffHDR can
  also be extended to document editing and text block generation, showcasing its
  high flexibility and generalization capacity. We believe this study could
  pioneer a new direction of document processing and contribute to the
  inheritance of invaluable cultures and civilizations. The dataset and code is
  available at https://github.com/yeungchenwa/HDR.
paper_authors._id: 67638043d09954b7a91a16a2
paper_authors.name: Zhenhua Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-19T18:01:41.643Z
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paper_authors.fullname: Zhenhua Yang
paper_authors.type: user
paper_authors.user: yeungchenwa0106
paper_authors._id: 67638043d09954b7a91a16a3
paper_authors.name: Dezhi Peng
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/b5ff6d5f38e08169144e77d3f5faf043.svg
paper_authors.fullname: Dezhi Peng
paper_authors.type: user
paper_authors.user: NanShanNan
paper_authors._id: 67638043d09954b7a91a16a4
paper_authors.name: Yongxin Shi
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/aae28bfbda59db4fd7d1c83130adc17f.svg
paper_authors.fullname: 史永鑫
paper_authors.type: user
paper_authors.user: Shiyongxin
paper_authors._id: 67638043d09954b7a91a16a5
paper_authors.name: Yuyi Zhang
paper_authors.status: admin_assigned
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paper_authors._id: 65fba5700b78c48c9e393a3e
paper_authors.avatarUrl: /avatars/795cc8167460d8e89ff91d27c5da9fb2.svg
paper_authors.fullname: Yuyi Zhang
paper_authors.type: user
paper_authors.user: ZZXF
paper_authors._id: 67638043d09954b7a91a16a6
paper_authors.name: Chongyu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T10:10:21.390Z
paper_authors._id: 6306e365cb5492c985a47de9
paper_authors.avatarUrl: /avatars/15dd02528d15c2a1cbe99e76491d0466.svg
paper_authors.fullname: chongyu
paper_authors.type: user
paper_authors.user: liuchongyu
paper_authors._id: 67638043d09954b7a91a16a7
paper_authors.name: Lianwen Jin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T10:10:11.670Z
paper_authors._id: 66a102960072f5db18e860e3
paper_authors.avatarUrl: /avatars/7679eddb31153c6b868cf496833551d6.svg
paper_authors.fullname: Lianwen Jin
paper_authors.type: user
paper_authors.user: lianwen
paper_title: Predicting the Original Appearance of Damaged Historical Documents
paper_summary: Historical documents encompass a wealth of cultural treasures but suffer from
  severe damages including character missing, paper damage, and ink erosion over
  time. However, existing document processing methods primarily focus on
  binarization, enhancement, etc., neglecting the repair of these damages. To
  this end, we present a new task, termed Historical Document Repair (HDR), which
  aims to predict the original appearance of damaged historical documents. To
  fill the gap in this field, we propose a large-scale dataset HDR28K and a
  diffusion-based network DiffHDR for historical document repair. Specifically,
  HDR28K contains 28,552 damaged-repaired image pairs with character-level
  annotations and multi-style degradations. Moreover, DiffHDR augments the
  vanilla diffusion framework with semantic and spatial information and a
  meticulously designed character perceptual loss for contextual and visual
  coherence. Experimental results demonstrate that the proposed DiffHDR trained
  using HDR28K significantly surpasses existing approaches and exhibits
  remarkable performance in handling real damaged documents. Notably, DiffHDR can
  also be extended to document editing and text block generation, showcasing its
  high flexibility and generalization capacity. We believe this study could
  pioneer a new direction of document processing and contribute to the
  inheritance of invaluable cultures and civilizations. The dataset and code is
  available at https://github.com/yeungchenwa/HDR.
paper_ai_summary: DiffHDR, a diffusion-based network, repairs historical documents by predicting their original appearance using a large-scale dataset with semantic and spatial enhancements.
paper_ai_keywords: Historical Document Repair HDR28K diffusion-based network DiffHDR character-level annotations multi-style degradations vanilla diffusion framework semantic information spatial information character perceptual loss contextual and visual coherence document editing text block generation