Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models

doi:10.1016/0031-3203(95)00124-7

Pattern Recognition

Volume 29, Issue 5, May 1996, Pages 845-858

https://doi.org/10.1016/0031-3203(95)00124-7 Get rights and content

Abstract

This paper proposes a recognition system of constrained Handwritten Hangul (Korean character) and alphanumeric characters using discrete hidden Markov models (HMMs). The HMM process encodes the distortion and similarity among patterns of a class through a doubly stochastic approach. Characterizing the statistical properties of characters using selected features, a recognition system can be implemented by absorbing possible variations in the form. Hangul shapes are classified into six types by fuzzy according to their effectiveness in each class. The constrained alphanumerics recognition is also performed using the same features employed in Hangul recogndition. The forward-backward, Viterbi and Baum-Welch reestimation algorithms are used for training and recognition of handwritten Hangul and alphanumeric characters. The simulation result shows that the proposed method recognizes effectively handwritten Korean charaters and alphanumerics

References (17)

ChoS.-B. et al.
Recognition of large-set printed Hangul (Korean script) by two-stage backpropagation neural classifier
Pattern Recognition
(1992)
SimD.-G. et al.
On-line recognition of cursive Korean characters using DP matching and fuzzy concepts
Pattern Recognition
(1994)
KunduA. et al.
Recognition of handwritten word: First and second order hidden Markov model based approach
Pattern Recognition
(1989)
PavlidisT.
A thinning algorithm for discrete binary images
Comput. Graphics Image Process
(1980)
HamY.K. et al.
Automated analysis of mixed documents consisting of printed Korean/alphanumeric texts and graphic images
Opt. Eng.
(1994)
HamY.K. et al.
Recognition of raised characters for automatic classification of rubber tires
Opt. Eng.
(1995)
RabinerL.R.
A tutorial on hidden Markov models and selected applications in speech recognition
AnigboguJ.C. et al.
Performance evaluation of an HMM based OCR system

There are more references available in the full text version of this article.

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Citation Excerpt :
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Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models

Abstract

Pattern Recognition

Pattern Recognition

Pattern Recognition

Comput. Graphics Image Process

Automated analysis of mixed documents consisting of printed Korean/alphanumeric texts and graphic images

Opt. Eng.

Recognition of raised characters for automatic classification of rubber tires

Opt. Eng.

A tutorial on hidden Markov models and selected applications in speech recognition

Performance evaluation of an HMM based OCR system