Off-line handwritten word recognition using a hidden Markov modeltype stochastic network
Mou-Yen Chen
Amlan Kundu
Jian Zhou
CEDAR, State Univ. of New York, Buffalo, NY;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 1994
Volume: 16,
Issue: 5
On page(s): 481-496
ISSN: 0162-8828
References Cited: 39
CODEN: ITPIDJ
INSPEC Accession Number: 4713245
Digital Object Identifier: 10.1109/34.291449
Current Version Published: 2002-08-06
Abstract
Because of large variations involved in handwritten words, the
recognition problem is very difficult. Hidden Markov models (HMM) have
been widely and successfully used in speech processing and recognition.
Recently HMM has also been used with some success in recognizing
handwritten words with presegmented letters. In this paper, a complete
scheme for totally unconstrained handwritten word recognition based on a
single contextual hidden Markov model type stochastic network is
presented. Our scheme includes a morphology and heuristics based
segmentation algorithm, a training algorithm that can adapt itself with
the changing dictionary, and a modified Viterbi algorithm which searches
for the (l+1)th globally best path based on the previous l best paths.
Detailed experiments are carried out and successful recognition results
are reported
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