Alternatives to variable duration HMM in handwriting recognition

被引：20

作者：

Kundu, A ^{[1
]}

He, Y

Chen, MY

机构：

[1] US W Adv Technol, Boulder, CO 80303 USA

[2] Xerox Imaging Syst, Peabody, MA 01960 USA

[3] ITRI, Hsinchu, Taiwan

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1998年 / 20卷 / 11期

关键词：

variable duration HMM (VDHMM); path discriminant HMM (PD-HMM); model discriminant HMM (MD-HMM); nonergodic HMM (NEHMM); variable sequence length HMM (VSLHMM); adaptive length Viterbi algorithm (ALVA);

D O I：

10.1109/34.730561

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A successful handwritten word recognition (HWR) system using Variable Duration Hidden Markov Model (VDHMM) and the PD-HMM strategy is easy to implement. The central theme of this paper is to show that if the duration statistics are computed, it could be utilized to implement an MD-HMM approach for better experimental results. This paper also describes a PD-HMM based HWR system where the duration statistics are not explicitly computed, but results are still comparable to VDHMM based HWR scheme [1].

引用

页码：1275 / 1280

页数：6

共 5 条

[1] OFF-LINE CURSIVE HANDWRITING RECOGNITION USING HIDDEN MARKOV-MODELS [J].