The week's pick
Reseach Article
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 |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 116 - Number 21 |
| Year of Publication: 2015 |
| Authors: Shubhra Saxena, V S Dhaka |
10.5120/20459-2817
|
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
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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