Recognition of handwritten word: first and second order hiddenMarkov model based approach
Kundu, A.
He, Y.
Bahl, P.
Dept. of Electr. Eng., State Univ., of New York, Buffalo, NY;
This paper appears in: Computer Vision and Pattern Recognition, 1988. Proceedings CVPR '88., Computer Society Conference on
Publication Date: 5-9 Jun 1988
On page(s): 457-462
Meeting Date: 06/05/1988 - 06/09/1988
Location: Ann Arbor, MI, USA
ISBN: 0-8186-0862-5
References Cited: 21
INSPEC Accession Number: 3248013
Digital Object Identifier: 10.1109/CVPR.1988.196275
Current Version Published: 2002-08-06
Abstract
The handwritten word recognition problem is modeled in the
framework of the hidden Markov model (HMM). The states of HMM are
identified with the letters of the alphabet. The optimum symbols are
then generated experimentally using 15 different features. Both the
first- and second-order HMMs are proposed for the recognition tasks.
Using the existing statistical knowledge of English, the calculation
scheme of the model parameters are immensely simplified. Once the model
is established, the Viterbi algorithm is used to recognize the sequence
of letters consisting the word. Some experimental results are also
provided indicating the success of the scheme
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