Combining HMM classifiers in a handwritten text recognition system

Author

Procter, S. ; Illingworth, J.

Author_Institution

Centre for Vision, Speech & Signal Process., Surrey Univ., Guildford, UK

Volume

2

fYear

1998

fDate

4-7 Oct 1998

Firstpage

934

Abstract

A study of several methods for combining information from two classifiers in a system for the recognition of handwritten text is presented. The system uses two hidden Markov models (HMMs) per character to model columns and rows of pixels in the character image. We show that the best method of combining the results from the vertical and horizontal classifiers is simply to multiply the probabilities produced by the two methods. This approach outperforms more complicated classifier combination strategies such as the behaviour-knowledge space (BKS) method

Keywords

handwritten character recognition; hidden Markov models; image classification; probability; HMM classifiers; behaviour-knowledge space method; character image; handwritten text recognition system; hidden Markov models; horizontal classifiers; pixels; probabilities; vertical classifiers; Electronic mail; Hidden Markov models; Information technology; Mathematics; Pattern recognition; Pixel; Signal processing; Speech processing; Speech recognition; Text recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on

Conference_Location

Chicago, IL

Print_ISBN

0-8186-8821-1

Type

conf

DOI

10.1109/ICIP.1998.723708

Filename

723708