Predicting the Original Appearance of Damaged Historical Documents
Authors: name: Zhenhua Yang, name: Dezhi Peng, name: Yongxin Shi, name: Yuyi Zhang, name: Chongyu Liu, name: Lianwen Jin

Historical documents encompass a wealth of cultural treasures but suffer from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this end, we present a new task, termed Historical Document Repair (HDR), which
aims to predict the original appearance of damaged historical documents.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diffusion-based network DiffHDR for historical document repair.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.