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Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

by Deepali R. Birajdar, Manasi M.patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 3
Year of Publication: 2015
Authors: Deepali R. Birajdar, Manasi M.patil
10.5120/21204-3883

Deepali R. Birajdar, Manasi M.patil . Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction. International Journal of Computer Applications. 120, 3 ( June 2015), 1-4. DOI=10.5120/21204-3883

@article{ 10.5120/21204-3883,
author = { Deepali R. Birajdar, Manasi M.patil },
title = { Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 3 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number3/21204-3883/ },
doi = { 10.5120/21204-3883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:14.766293+05:30
%A Deepali R. Birajdar
%A Manasi M.patil
%T Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 3
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten Devanagari off-line characters recognition is a challenging task due to peculiarities involved in the writing styles of different person. We are collected features from 64 dimensional feature extraction techniques and shadow features for the Devanagari character recognition in this proposed scheme. These calculated features are used for further classification. Features which are used here for recognition means histograms of direction chain code of the contour points of the characters [4]. The features are classified using neural network and selected the best result by weighted majority voting technique.

References
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  2. Nei Kato,Shin'ichiro Omachi,Hirotomo Aso, Yoshiaki Nemoto "A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance", IEEE transactions on pattern analysis and machine intelligence, vol. 21, no. 3, march 1999 pp 258-262
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  8. Sandhya Arora1. Debotosh Bhattacharjee2, Mita Nasipuri2, L. Malik4 "Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition", International Journal of Computer Science Issues, Vol. 7, Issue 3, No 6, May 2010.
  9. Vikas J Dongre Vijay H Mankar," A Review of Research on Devnagari Character Recognition", International Journal of Computer Applications (0975 – 8887)Volume 12– No. 2, November 2010.
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Index Terms

Computer Science
Information Sciences

Keywords

64 dimensional features Shadow features neural network and weighted majority voting technique.

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