Predicting the Original Appearance
of Damaged Historical Documents
Zhenhua Yang1*, Dezhi Peng1*, Yongxin Shi1, Yuyi Zhang1, Chongyu Liu1, Lianwen Jin12(2025)
Introduction
• Historical documents carry cultural, linguistic, and intellectual heritage, and are important to
the transmission of civilization.
• Over long-term preservation, these historical documents often suffer severe damages (character
missing, paper damage, and ink erosion, etc.).
• However, existing document processing methods almost neglect the repair of these damages,
and fall short of understanding the semantics and stylistic elements within document images,
thereby limiting their capability to repair the damaged documents.
Objective: Historical Document Repair (HDR), predicting the
original appearance of damaged historical document images.
✔ Restore character content
✔ Reconstruct font style and stroke details
✔ Recover paper texture and background integrity
Method
HDR28K Dataset
• Designed exactly for HDR, and based on MTHv2 and M5HisDoc historical document datasets.
• Cropped original images into 512×512 patches focusing on text regions.
• Contains images with character-level annotations + 3 types of realistic degradations.
25%
50%
25%
Training: 22848 patches
Testing: 5704 patches
Method
Diffusion-based HDR Network (DiffHDR)
Condition Parsing
Diffusion Pipeline
Training Strategy
Ⅰ. Condition Parsing
• Input: damaged image Xd
• Generate two priors：
Content image Xc: character semantics (what to restore)
Mask image Xm​: damage location (where to restore)
✔ Produce a combined condition for the diffusion model
Method
Diffusion-based HDR Network (DiffHDR)
Condition Parsing
Ⅱ. Diffusion Pipeline
• Add noise to the damaged image: Xt
• Denoiser F gradually removes noise to
recover
character
strokes
and
background textures.
• Output: repaired-image Xr
✔ Repairs both text and paper fibers
✔ Maintains visual and contextual consistency
Diffusion Pipeline
Training Strategy
Method
Diffusion-based HDR Network (DiffHDR)
Condition Parsing
Diffusion Pipeline
Ⅲ. Training Strategy
• Diffusion Loss Ldiff:
Ensuring pixel-level reconstruction
• Character Perceptual Loss Lcp:
Ensuring sharp and consistent character strokes
✔ The model not only learns to denoise but also to write correctly.
Training Strategy
Experiment
Evaluation Metrics
Comparision with Existing Methods
Qualitative Comparison
Experiment
Quantitative Comparison
↑ DiffHDR has the best performance in 3
metrics.
→DiffHDR can reconstruct the original
appearance of damaged images with both
realism and high quality.
Experiment
Real Damaged Document Image Repair
Predict the damaged historical document from the Internet.
✔ Demonstrates the adaptability of our method in real-world scenarios.
✔ Validates the appropriateness of our HDR28K dataset.
Experiment
Effectiveness of CPLoss (Lcp)
Investigate the advantage of the proposed CPLoss.
✔ CPLoss improves the repair performance in terms of
FID, LPIPS, and Rec-ACC.
Editing and Text Block Font Generation
Explore the capabilities of DiffHDR in historical document
editing and text block font generation.
✔ Demonstrates the extended capabilities and practical
value of DiffHDR
Conclusion
Key Contributions
1.Introduce the Historical Document Repair (HDR) task, which aims to reconstruct both missing
characters and corrupted background textures.
2.Construct HDR28K, a large-scale dataset containing 28,552 damaged–repaired image pairs, with
character-level annotations and diverse, realistic degradation types.
3.Propose DiffHDR, a diffusion-based restoration method that incorporates semantic and spatial
priors, along with a character perceptual loss, to ensure contextual correctness and visual
coherence.
4.DiffHDR reaches state-of-the-art performance and verifies its robustness and scalability to
different real-world scenarios.
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