HMM-BASED SYSTEM FOR TRANSCRIBING CHINESE HANDWRITING

摘要：

A novel recognition strategy is proposed for the transcription of Chinese handwritten documents.The recognizer adapts continuous density Hidden Markov Model (HMM) as the recognition engine.It incorporates character segmentation and recognition in one step avoiding character segmentation phase.Textline is extracted and converted to observation sequence by sliding windows first Then Baum-Welch algorithm is used to train character HMMs.Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output Experiments are conducted on a writer-dependent Chinese handwriting database with a 1,695 lexicon.The results show that our baseline recognizer outperforms much one popular commercial handwritten character recognition product and the strategy presented in this paper is a promising research direction.

关键词： Hidden Markov models Chinese characters Optical character recognition Handwriting recognition Sliding window

作者: TONG-HUA SU TIAN-WEN ZHANG ZHAO-WEN QIU

作者单位: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China Institute of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040, Ch

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3412-3417

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）