Research Article
Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 116 - Issue 21 |
| Published: April 2015 |
| Authors: Shubhra Saxena, V S Dhaka |
10.5120/20459-2817
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Shubhra Saxena, V S Dhaka . Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model. International Journal of Computer Applications. 116, 21 (April 2015), 12-17. DOI=10.5120/20459-2817
@article{ 10.5120/20459-2817,
author = { Shubhra Saxena,V S Dhaka },
title = { Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 116 },
number = { 21 },
pages = { 12-17 },
doi = { 10.5120/20459-2817 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Shubhra Saxena
%A V S Dhaka
%T Exploration of Improved Methodology for Character Image Recognition of Two Popular Indian Scripts using Gabor Feature with Hidden Markov Model%T
%J International Journal of Computer Applications
%V 116
%N 21
%P 12-17
%R 10.5120/20459-2817
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Handwritten character recognition plays an important role in the modern world. It can solve more complex problems and make the human's job easier. The present work portrays a novel approach in recognizing handwritten cursive character using Hidden Markov Model (HMM) . The method exploits the HMM formalism to capture the dynamics of input patterns, by applying a Gabor filter to a character image, observation feature vector is obtained, and used to form feature vectors for recognition. The HMM model is proposed to recognize a character image. All the experiments are conducted by using the Matlab tool kit.
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