Multilayer fuzzy HMM for online handwriting shape recognition

Author

Li, Cuiyun ; Ji, Hongbing ; Pei, Jihong

Author_Institution

Sch. of Electr. Eng., Xidian Univ., Xi´´an, China

Volume

2

fYear

2004

fDate

31 Aug.-4 Sept. 2004

Firstpage

1427

Abstract

This paper discusses a novel type of fuzzy hidden Markov model (FHMM) based on a multilayer decision tree and presents its application to online shape recognition. A local feature vector is obtained by calculating a shape´s absolute angle, which is used as the feature for FHMM training and recognition. The global features of the shape are incorporated into the decision tree. The multilayer FHMM can decrease the computation time in training because of the fuzziness of the model. Due to the reduction of the shape searching space by the decision tree, the recognition time is saved and the recognition rate is improved.

Keywords

decision trees; fuzzy set theory; handwritten character recognition; hidden Markov models; learning (artificial intelligence); FHMM; FHMM training; decision tree; fuzzy hidden Markov model; local feature vector; multilayer decision tree; online handwriting shape recognition; shape searching space; Character recognition; Decision trees; Density measurement; Handwriting recognition; Hidden Markov models; Logic; Robustness; Shape; Spatial databases; Speech;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on

Print_ISBN

0-7803-8406-7

Type

conf

DOI

10.1109/ICOSP.2004.1441594

Filename

1441594