Abstract
With the advancements in high-definition imaging and parallel computing hardware, the analysis of massive visual data has become a key focus in pattern recognition and artificial intelligence. Chinese calligraphy, an integral part of traditional culture, has seen digitization of numerous works stored in digital libraries. However, current automatic calligraphy character recognition technology is limited, necessitating the development of efficient computer vision methods for recognizing calligraphy styles. Data mining, crucial in artificial intelligence, involves extracting valuable knowledge from vast and noisy datasets. Recent simulation results show promising recognition rates for Chinese text images, with an average recognition time of 5 seconds per 100 words. This system significantly improves handwriting recognition accuracy compared to existing algorithms, though further refinement and expansion are needed for optimal functionality. © 2025 IGI Global. All rights reserved.
| Original language | English |
|---|---|
| Journal | International Journal of Information System Modeling and Design |
| Volume | 16 |
| Issue number | 1 |
| Online published | 13 Dec 2024 |
| DOIs | |
| Publication status | Published - 2025 |
| Externally published | Yes |
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Funding for this research was covered by the authors of the article.
Research Keywords
- Calligraphy Recognition
- Clerical Script
- Clustering
- Data Mining
- Neural Network-Based ML Algorithms for Visual Recognition
- Official Script
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
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