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Segregated Handwritten Character Recognition using GLCM features

by V. C. Bharathi, M. Kalaiselvi Geetha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 2
Year of Publication: 2013
Authors: V. C. Bharathi, M. Kalaiselvi Geetha
10.5120/14545-2644

V. C. Bharathi, M. Kalaiselvi Geetha . Segregated Handwritten Character Recognition using GLCM features. International Journal of Computer Applications. 84, 2 ( December 2013), 1-7. DOI=10.5120/14545-2644

@article{ 10.5120/14545-2644,
author = { V. C. Bharathi, M. Kalaiselvi Geetha },
title = { Segregated Handwritten Character Recognition using GLCM features },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 2 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number2/14545-2644/ },
doi = { 10.5120/14545-2644 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:51.944597+05:30
%A V. C. Bharathi
%A M. Kalaiselvi Geetha
%T Segregated Handwritten Character Recognition using GLCM features
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 2
%P 1-7
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten document recognition is an area of pattern recognition that has been showing impressive performance in the machine printed text. Handwritten document recognition is an intricate task to various writing styles of individual person. The system first identifies the contour in a handwritten document for segmentation and features are extracted from the segmented character. This paper uses GLCM(Gray Level Co-occurrence Matrix) for character recognition. Features of a character has been computed based on calculating the pairs of pixel with specific values and specified spatial relationship occurrence in an image. First order and second order textures are used to measure the intensity of the original pixels. Data were collected from different persons, and the system is trained using SVM with various writing styles. The proposed system achieves a maximum recognition accuracy of 95. 2% with training and testing data using GLCM as features and SVM with RBF kernel function.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Handwritten Character Recognition Segmentation Gray Level Co-occurrence Matrix (GLCM) Support Vector Machine.

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