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Reseach Article

Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features

by Pratibha Singh, Ajay Verma, Narendra S. Chaudhari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 1
Year of Publication: 2014
Authors: Pratibha Singh, Ajay Verma, Narendra S. Chaudhari
10.5120/15464-3628

Pratibha Singh, Ajay Verma, Narendra S. Chaudhari . Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features. International Journal of Computer Applications. 89, 1 ( March 2014), 6-12. DOI=10.5120/15464-3628

@article{ 10.5120/15464-3628,
author = { Pratibha Singh, Ajay Verma, Narendra S. Chaudhari },
title = { Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 1 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number1/15464-3628/ },
doi = { 10.5120/15464-3628 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:05.902730+05:30
%A Pratibha Singh
%A Ajay Verma
%A Narendra S. Chaudhari
%T Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 1
%P 6-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We give our formulation for a ten class classification of handwritten Hindi digit recognition. Automatic Recognition of Handwritten Devnagri Numerals is a difficult task, because of the variability in writing style; pen used for writing and the color of handwriting, unlikely the printed character. Furthermore, Hindi Digit can be drawn in different sizes. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. Hence we have chosen a combination of global and local features. The global features are the structural features like endpoint, crosspoint, centroid of the loop, u shaped structure, C shaped structure and inverted C shaped structure. The local set of features combine the distance of thinned image from geometric centroid calculated zone-wise and histogram based features calculated zone-wise. Variability in writing style is taken care by size normalization and normalization to constant thickness as preprocessing a step before feature extraction. We used an Artificial Neural Network as classifier for recognition. Our method results in average correct rate of 95% or better. The combination of local and global features results in reduced confusion value. .

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Index Terms

Computer Science
Information Sciences

Keywords

Features ANN Structural feature neuron Histogram