Two-Stage Stains Reduction with Diffusion Models for Ancient Japanese Manuscripts

Ancient Japanese manuscripts are a valuable cultural heritage that allows us to understand the culture and values of the past. However, due to preservation over hundreds of years, stains result in antique manuscripts, and they cause low readability and loss of contents to the manuscripts. Considering preserving these assets through virtual restoration, we propose a two-stage stain reduction based on Denoising Diffusion Restoration Models (DDRM). The stain reduction proceeds by combining removing background by the binarization and reconstruction of before-processing images by DDRM. In addition, the sequential conducting that contains patches of book pages and entire them leads to preserving local information and ensuring consistency across the pages. Generation results are qualitatively improved and more realistic than traditional binarization. Also, the proposal method is evaluated by two aspects, the stain reduction metrics and preserving characters metrics. The evaluation result shows our proposal is more effective and achieves higher restoration quality than conventional binarization.

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Publication Details

Published
2024-11-08
DOI
https://doi.org/10.1109/iccsi62669.2024.10799366
Citations
1
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.25
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Two-Stage Stains Reduction with Diffusion Models for Ancient Japanese Manuscripts

Hayata Kaneko, Ryuto Ishibashi, Lin Meng, Mingcong Deng et al.
1 citations
Handwritten Text Recognition Techniques
0.25
article

Two-Stage Stains Reduction with Diffusion Models for Ancient Japanese Manuscripts

Hayata Kaneko, Ryuto Ishibashi, Lin Meng, Mingcong Deng, Yuya Yoshizu
article en
1 citations

Abstract

Ancient Japanese manuscripts are a valuable cultural heritage that allows us to understand the culture and values of the past. However, due to preservation over hundreds of years, stains result in antique manuscripts, and they cause low readability and loss of contents to the manuscripts. Considering preserving these assets through virtual restoration, we propose a two-stage stain reduction based on Denoising Diffusion Restoration Models (DDRM). The stain reduction proceeds by combining removing background by the binarization and reconstruction of before-processing images by DDRM. In addition, the sequential conducting that contains patches of book pages and entire them leads to preserving local information and ensuring consistency across the pages. Generation results are qualitatively improved and more realistic than traditional binarization. Also, the proposal method is evaluated by two aspects, the stain reduction metrics and preserving characters metrics. The evaluation result shows our proposal is more effective and achieves higher restoration quality than conventional binarization.

Ritsumeikan University (JP), Tokyo University of Agriculture and Technology (JP)
Openalex Percentile: Top 43%
Handwritten Text Recognition Techniques
0.25
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