Chinese inscription restoration based on artificial intelligent models

Chinese ancient inscriptions have a long history, while natural erosion and human destruction have led to many incomplete inscriptions with low-quality textual data and blurry images. With deep learning technologies, it is expected to use relevant image and language processing tasks to restore inscriptions. To improve the efficiency of restoration tasks and promote the digital protection of cultural heritage, this study used deep learning technology to restore ancient Chinese inscriptions. We combined natural language processing and computer vision technologies to train models for restoring inscriptions. The results indicated that the joint solution had advantages over every single model for incomplete character restoration.

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Publication Details

Journal
npj Heritage Science
Published
2025-07-05
DOI
https://doi.org/10.1038/s40494-025-01900-x
Citations
2
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
2.55

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article

Chinese inscription restoration based on artificial intelligent models

Honglei Li, Yujun Li, Zhen Wang
2 citations
npj Heritage Science
Image Processing and 3D Reconstruction
2.55
article

Chinese inscription restoration based on artificial intelligent models

Honglei Li, Yujun Li, Zhen Wang
article en
2 citations

Abstract

Chinese ancient inscriptions have a long history, while natural erosion and human destruction have led to many incomplete inscriptions with low-quality textual data and blurry images. With deep learning technologies, it is expected to use relevant image and language processing tasks to restore inscriptions. To improve the efficiency of restoration tasks and promote the digital protection of cultural heritage, this study used deep learning technology to restore ancient Chinese inscriptions. We combined natural language processing and computer vision technologies to train models for restoring inscriptions. The results indicated that the joint solution had advantages over every single model for incomplete character restoration.

npj Heritage ScienceVol. 13(1)
Liaoning Normal University (CN)
Social Science Foundation of Liaoning Province
Sustainable cities and communities
Openalex Percentile: Top 10%
Image Processing and 3D Reconstruction
2.55
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