Huang, Q.; Li, M.; Agustin, D.; Li, L.; Jha, M. A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font. Sensors2024, 24, 197.
Huang, Q.; Li, M.; Agustin, D.; Li, L.; Jha, M. A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font. Sensors 2024, 24, 197.
Huang, Q.; Li, M.; Agustin, D.; Li, L.; Jha, M. A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font. Sensors2024, 24, 197.
Huang, Q.; Li, M.; Agustin, D.; Li, L.; Jha, M. A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font. Sensors 2024, 24, 197.
Abstract
Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as Regular (Kai Shu), Running (Xing Shu), Official (Li Shu), and Cursive (Cao Shu) scripts. Beginners often start with Regular script, advancing to more intricate styles like Cursive. Each style, marked by unique historical calligraphers' contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification are pivotal. This study introduces an innovative Convolutional Neural Network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphers' styles from the Tang dynasty (690-907 A.D), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5-96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in both font and artistic style categorization. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.
Keywords
deep learning; convolutional neural network (CNN); chinese calligraphy; styles classification; handwriting recognition
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
