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Off-line handwritten word recognition (HWR) using a singlecontextual hidden Markov model
Chen, M.-Y. Kundu, A. Zhou, J.
Center of Excellence for Document Anal. & Recognition, State Univ. of New York, Buffalo, NY;

This paper appears in: Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on
Publication Date: 15-18 Jun 1992
On page(s): 669-672
Meeting Date: 06/15/1992 - 06/18/1992
Location: Champaign, IL, USA
ISBN: 0-8186-2855-3
References Cited: 3
INSPEC Accession Number: 4391266
Digital Object Identifier: 10.1109/CVPR.1992.223205
Current Version Published: 2002-08-06

Abstract
A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported

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