Variable duration hidden Markov model and morphologicalsegmentation for handwritten word recognition
Chen, M.-Y.
Kundu, A.
Srihari, S.N.
Center of Excellence for Document Analysis & Recognition, State Univ. of New York, NY;
Abstract
A complete system for the recognition of unconstrained handwritten
words using a continuous density variable duration hidden Markov model
(CDVDHMM) is described. A new segmentation algorithm based on
mathematical morphology is used to translate the 2-D image into a 1-D
sequence of sub-character symbols. This sequence of symbols is modeled
by the CDVDHMM. Generally, there are two information sources associated
with the written text. 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. In this
context, the variable duration state is used to take care of the
segmentation ambiguity among the consecutive characters. Some
experimental results are described to demonstrate the success of the
proposed scheme
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
