Predicting the Original Appearance of Damaged Historical Documents
Historical documents encompass a wealth of cultural treasures but suffer from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this end, we present a new task, termed Historical Document Repair (HDR), which
aims to predict the original appearance of damaged historical documents.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diffusion-based network DiffHDR for historical document repair.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.
67638043d09954b7a91a16a2
Zhenhua Yang
claimed_verified
2024-12-19T18:01:41.643Z
66a89cddb8c4797944207f20
/avatars/3b062541f5b59f5ef8e700f4e973e2ea.svg
Zhenhua Yang
user
yeungchenwa0106
67638043d09954b7a91a16a3
Dezhi Peng
admin_assigned
2024-12-20T10:11:16.651Z
66beb1c9c55655c715092329
/avatars/b5ff6d5f38e08169144e77d3f5faf043.svg
Dezhi Peng
user
NanShanNan
67638043d09954b7a91a16a4
Yongxin Shi
admin_assigned
2024-12-20T10:11:09.993Z
64d4daf356041a17cc62c9b6
/avatars/aae28bfbda59db4fd7d1c83130adc17f.svg
史永鑫
user
Shiyongxin
67638043d09954b7a91a16a5
Yuyi Zhang
admin_assigned
2024-12-20T10:10:57.156Z
65fba5700b78c48c9e393a3e
/avatars/795cc8167460d8e89ff91d27c5da9fb2.svg
Yuyi Zhang
user
ZZXF
67638043d09954b7a91a16a6
Chongyu Liu
admin_assigned
2024-12-20T10:10:21.390Z
6306e365cb5492c985a47de9
/avatars/15dd02528d15c2a1cbe99e76491d0466.svg
chongyu
user
liuchongyu
67638043d09954b7a91a16a7
Lianwen Jin
admin_assigned
2024-12-20T10:10:11.670Z
66a102960072f5db18e860e3
/avatars/7679eddb31153c6b868cf496833551d6.svg
Lianwen Jin
user
lianwen
Predicting the Original Appearance of Damaged Historical Documents
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.
DiffHDR, a diffusion-based network, repairs historical documents by predicting their original appearance using a large-scale dataset with semantic and spatial enhancements.
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