Combining Discrete Orthogonal Moments and DHMMS for Off-Line Handwritten Chinese Character Recognition

摘要：

Discrete orthogonal moment set is one of the novel feature moment-based descriptors for image analysis. The Tchebichef moments and Krawtchouk moments are the two representatives in this class. This paper studies the performance of the two discrete orthogonal moments in the recognition of off-line handwritten Chinese amount in words under Discrete-time Hidden Markov Models (DHMMs) framework. The lower order moments are employed as features. A serial of experiments are carried out to compare their performance with that of the continuous orthogonal movements such as Zernike and Legendre. Experimental results suggest that the recognition performance of two discrete orthogonal moments is higher than that of the continuous discrete moments. In additional, different values of the number of zones, observation symbols and states are also used to find the better model structure for the new approach.

关键词： Discrete orthogonal moments DHMMs Offline handwritten character recognition.

作者: Xianmei Wang Yang Yang Kang Huang

作者单位: School of Information and Engineering University of Science and Technology Beijing No. 30,Xueyuan Road, Beijing, China, 100083

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 788-793

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）