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Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

by Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 8
Year of Publication: 2012
Authors: Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir
10.5120/7371-0151

Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir . Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM. International Journal of Computer Applications. 48, 8 ( June 2012), 39-44. DOI=10.5120/7371-0151

@article{ 10.5120/7371-0151,
author = { Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir },
title = { Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 8 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number8/7371-0151/ },
doi = { 10.5120/7371-0151 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:35.431191+05:30
%A Ashutosh Aggarwal
%A Rajneesh Rani
%A Renu Dhir
%T Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 8
%P 39-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognition of Indian languages is a challenging problem. In Optical Character Recognition (OCR), acharacter or symbol to be recognized can be machine printed or handwritten characters/numerals. Several approaches in the past have been proposed that deal with problem of recognition of numerals/character depending on the type of feature extracted and way of extracting them. In this paper also a recognition system for isolated Handwritten Devanagari Numerals has been proposed. The proposed system is based on the division of sample image into sub-blocks and then in each sub-block Strength of Gradient is accumulated in 8 standard directions in which Gradient Direction is decomposed resulting in a feature vector with dimensionality of 200. Support Vector Machine (SVM) is used for classification. Accuracy of 99. 60% has been obtained by using standard dataset provided by ISI (Indian Statistical Institute) Kolkata.

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Index Terms

Computer Science
Information Sciences

Keywords

Devanagari Numeralrecognition Handwrittenrecognition Gradient Gradient Feature Extraction svm

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