

Historical Document Repair
Overview :
HDR is a new technology focused on restoring damaged historical documents, aimed at predicting their original appearance. This technology utilizes the HD28K large-scale dataset and the diffusion-based network, DiffHDR, to address various damages including character loss, paper deterioration, and ink erosion. The primary advantage of HDR is its ability to accurately capture character content and style while ensuring coherence with the background of the restoration area. This technology not only restores damaged documents but also extends to document editing and text block generation, showcasing high flexibility and generalization capabilities. HDR holds significant importance for preserving invaluable culture and civilization.
Target Users :
The target audience includes historical document restoration specialists, cultural heritage preservers, archivists, and scholars interested in the study of historical documents. HDR technology is suitable for them as it offers an efficient and precise method for restoring and preserving damaged historical documents, contributing to cultural heritage and historical research.
Use Cases
Restoring historical texts with blurred ink due to age.
Recovering important historical documents damaged by war or natural disasters.
Digitally restoring ancient manuscripts for better preservation and study.
Features
- Restore damaged historical documents: predict the original appearance of damaged documents.
- Large-scale dataset HDR28K: comprises 28,552 pairs of damaged and restored image pairs, featuring character-level annotations and diverse style degradations.
- Diffusion-based network DiffHDR: combines semantic and spatial information, along with carefully designed character-aware loss, to enhance contextual and visual consistency.
- Experimental results: After training on HDR28K, DiffHDR significantly outperforms existing methods, demonstrating exceptional performance on real damaged documents.
- Expandable application: DiffHDR can be extended to document editing and text block generation, exhibiting high flexibility and adaptive capabilities.
- Open-source code and dataset: Code and dataset are available on GitHub.
- High-precision restoration: Capable of accurately capturing character content and style in harmony with the surrounding background.
How to Use
1. Visit the HDR project GitHub page to download the code and dataset.
2. Install the necessary software and dependencies as per the documentation.
3. Train the DiffHDR model using the HDR28K dataset.
4. Input damaged historical document images into the HDR model for restoration.
5. Evaluate the restoration results by reviewing the model's output images.
6. If needed, utilize DiffHDR for further document editing and text block generation.
7. Fine-tune and optimize the restoration outcomes based on project requirements.
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