A study of the classification, protection, and inheritance of woodblock New Year painting using a deep learning image recognition model

Woodblock prints represent a significant form of traditional Chinese folk art.Woodblock prints are an important form of traditional Chinese folk art, carrying intensive information, containing profound humanistic spirit, and expressing distinctive national psychology during thousands of years of inheritance. They also sketch the three-dimensional image of folk life in the agricultural era with their unique art form, which profoundly reveals the people's deepest yearning and pursuit of the spiritual level. However, with the accelerating pace of modern life and the march of industrialisation and digitalisation, the allure of traditional New Year's Paintings has gradually diminished among younger generations. In response, the Rescue Office, under the leadership of Feng Jicai (Chairman of the Chinese Folk Literary and Artistic Artists Association), conducted a comprehensive census of the nation's wooden New Year's Paintings, with the aim of rescuing them. However, the results of this census revealed that the majority of the collected information was difficult to verify due to various issues related to preservation conditions. Consequently, the Rescue Office was only able to restore the existing information and conduct a subsequent investigation. The subsequent step in this process is to restore the existing information. The primary objective of this paper is to address the missing information in the woodblock prints by employing a deep learning image recognition model, which primarily utilises a convolutional neural network for image repair and classification feature extraction. The paper proposes a novel saturation distribution coefficient element in the paintings for woodblock print classification by saturation. It is proposed to use the potential diffusion model to generate videos corresponding to the elements of the New Year paintings, in order to improve the attractiveness of the woodblock prints to the younger generation as well as the method of preserving the inheritance of the woodblock prints, and it can be extended to the whole field of cultural relics.

Authors

Institutions

Publication Details

Published
2025-07-24
DOI
https://doi.org/10.1117/12.3068814
Primary Topic
Cultural and Historical Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A study of the classification, protection, and inheritance of woodblock New Year painting using a deep learning image recognition model

Xiaozhu Lin, Yixuan Wang
Cultural and Historical Studies
article

A study of the classification, protection, and inheritance of woodblock New Year painting using a deep learning image recognition model

Xiaozhu Lin, Yixuan Wang
article en

Abstract

Woodblock prints represent a significant form of traditional Chinese folk art.Woodblock prints are an important form of traditional Chinese folk art, carrying intensive information, containing profound humanistic spirit, and expressing distinctive national psychology during thousands of years of inheritance. They also sketch the three-dimensional image of folk life in the agricultural era with their unique art form, which profoundly reveals the people's deepest yearning and pursuit of the spiritual level. However, with the accelerating pace of modern life and the march of industrialisation and digitalisation, the allure of traditional New Year's Paintings has gradually diminished among younger generations. In response, the Rescue Office, under the leadership of Feng Jicai (Chairman of the Chinese Folk Literary and Artistic Artists Association), conducted a comprehensive census of the nation's wooden New Year's Paintings, with the aim of rescuing them. However, the results of this census revealed that the majority of the collected information was difficult to verify due to various issues related to preservation conditions. Consequently, the Rescue Office was only able to restore the existing information and conduct a subsequent investigation. The subsequent step in this process is to restore the existing information. The primary objective of this paper is to address the missing information in the woodblock prints by employing a deep learning image recognition model, which primarily utilises a convolutional neural network for image repair and classification feature extraction. The paper proposes a novel saturation distribution coefficient element in the paintings for woodblock print classification by saturation. It is proposed to use the potential diffusion model to generate videos corresponding to the elements of the New Year paintings, in order to improve the attractiveness of the woodblock prints to the younger generation as well as the method of preserving the inheritance of the woodblock prints, and it can be extended to the whole field of cultural relics.

Jiangsu Normal University (CN), Dalian University (CN)
Openalex Percentile: Top 82%
Cultural and Historical Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.