高技术通讯（英文版）2003，Vol.9Issue(4)：83-87,5.
Boosting the Expense and Performance of Ann/Hmm Approch for on-line Handwriting Recognition
Boosting the Expense and Performance of Ann/Hmm Approch for on-line Handwriting Recognition
Li Haifeng(李海峰) 1Han Jiqing 1Zheng Tieran 1Ma Lin 1Gallinari P2
作者信息
- 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001,P.R.China
- 2. Computer Science Laboratory, University Paris 6, Paris 75015,France
- 折叠
摘要
Abstract
This paper focuses on a state sharing method for an artificial neural network (ANN) and hidden Markov model (HMM) hybrid on-line handwriting recognition system. A modeling precision-based distance measure is proposed to describe similarity between two ANNs, which are used as HMM state-models. Limiting maximum system performance loss, a minimum quantification error aimed hierarchical clustering algorithm is designed to choose the most representative models. The system performance is improved by about 1.5% while saving 40% of the system expense. About 92% of the performance may also be maintained while reducing 70% of system parameters. The suggested method is quite useful for designing pen-based interface for various handheld devices.关键词
boosting/state sharing/hierarchical clustering/on-line handwriting recognitionKey words
boosting/state sharing/hierarchical clustering/on-line handwriting recognition分类
信息技术与安全科学引用本文复制引用
Li Haifeng(李海峰),Han Jiqing,Zheng Tieran,Ma Lin,Gallinari P..Boosting the Expense and Performance of Ann/Hmm Approch for on-line Handwriting Recognition[J].高技术通讯（英文版）,2003,9(4):83-87,5.
