Variable duration hidden markov model and morphological segmentation for handwritten word recognition

This 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 a 1-D sequence o...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 4; no. 12; pp. 1675 - 1688
Main Authors Chen, Mou-Yen, Kundu, Amlan, Srihari, Sargur N.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.1995
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Constraint modelling
Continuity (mathematics)
Gaussian distribution
Handwriting recognition
Hidden Markov models
Image recognition
Image segmentation
Information sources
Markov analysis
Markov chains
Markov processes
Mathematical morphology
Morphology
Normal distribution
Symbols
Training
Viterbi algorithm
Viterbi algorithm detectors
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Summary:This 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 a 1-D sequence of subcharacter 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. A modified Viterbi algorithm, which provides 2 globally best paths, is adapted to VDHMM by incorporating the duration probabilities for the variable duration state sequence. The general string editing method is used at the postprocessing stage. The detailed experiments are carried out for two postal applications; and successful recognition results are reported.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.1995.8875996