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Preferred Computational Approaches for the Recognition of different Classes of Printed Malayalam Characters using Hierarchical SVM Classifiers

by Bindu Philip, R. D. Sudhaker Samuel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 16
Year of Publication: 2010
Authors: Bindu Philip, R. D. Sudhaker Samuel
10.5120/350-530

Bindu Philip, R. D. Sudhaker Samuel . Preferred Computational Approaches for the Recognition of different Classes of Printed Malayalam Characters using Hierarchical SVM Classifiers. International Journal of Computer Applications. 1, 16 ( February 2010), 5-10. DOI=10.5120/350-530

@article{ 10.5120/350-530,
author = { Bindu Philip, R. D. Sudhaker Samuel },
title = { Preferred Computational Approaches for the Recognition of different Classes of Printed Malayalam Characters using Hierarchical SVM Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 16 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number16/350-530/ },
doi = { 10.5120/350-530 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:36.855145+05:30
%A Bindu Philip
%A R. D. Sudhaker Samuel
%T Preferred Computational Approaches for the Recognition of different Classes of Printed Malayalam Characters using Hierarchical SVM Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 16
%P 5-10
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Characterization of matrices for efficient classification has several options. There are various alternatives depending on the structure of the matrix. Different features can be adapted in different situations. Image recognition and in particular character recognition is an excellent example where large number of image matrices need to be stored and retrieved often at high speed, at the same time performing computational tasks, resulting in requirements of huge memory and computation time. Near 100% character segmentation accuracy is achieved based on a novel segmentation technique. Here feature extraction is based on the distinctive structural features of machine-printed text lines in these scripts. The final recognition is achieved through Support Vector Machine (SVM) classifiers. The proposed algorithms have been tested on a variety of printed Malayalam documents. Recognition rates between 97.72% and 98.78% have resulted.

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Index Terms

Computer Science
Information Sciences

Keywords

Pattern recognition character classification segmentation Optical character recognition Singular value decomposition marginal frequency Support vector machine classifier Malayalam OCR

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