Design and Application of Clerical Style Recognition System Based on Data Mining Algorithm

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.