Handwritten word recognition using continuous density variable duration hidden Markov model

Authors:
Mou-Yen Chen;Amlan Kundu;Sargur N. Srihari
Affiliations:
CEDAR, State University of New York at Buffalo, Amherst, NY;Naval Command, Control and Ocean Surveillance Center, San Diego, CA;CEDAR, State University of New York at Buffalo, Amherst, NY
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Year:
1993

Citing 2
Cited 1

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Pattern Recognition

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Abstract

The present paper describes a complete system for the recognition of unconstrained handwritten words using a Continuous Density Variable Duration Hidden Markov Model ( CDVDHMM ). First, a new segmentation algorithm based on mathematical morphology is developed to translate the 2-D image into 1- D sequence of sub-character symbols. This sequence of symbols is modeled by the CDVDHMM. Thirty-five features are selected to represent the character symbols in the feature space. Generally. there are two information sources associated with written text: the shape information and the linguistic knowledge. While the shape information of each character symbol is modeled as a mixture Gaussian distribution. the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. The variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. The detailed experiments are carried out using handwritten citynames: and successful recognition results are reported.