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Reseach Article

Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

by Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir
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

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 = { },
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

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


Devanagari Numeralrecognition Handwrittenrecognition Gradient Gradient Feature Extraction svm