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Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques

by Riya Jain, Gunjan Singh, Pankaj Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 26
Year of Publication: 2018
Authors: Riya Jain, Gunjan Singh, Pankaj Sharma
10.5120/ijca2018916599

Riya Jain, Gunjan Singh, Pankaj Sharma . Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques. International Journal of Computer Applications. 180, 26 ( Mar 2018), 29-34. DOI=10.5120/ijca2018916599

@article{ 10.5120/ijca2018916599,
author = { Riya Jain, Gunjan Singh, Pankaj Sharma },
title = { Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 180 },
number = { 26 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number26/29122-2018916599/ },
doi = { 10.5120/ijca2018916599 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:28.206340+05:30
%A Riya Jain
%A Gunjan Singh
%A Pankaj Sharma
%T Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 26
%P 29-34
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic character recognition is one of the most discovered and crucial application areas of pattern recognition field. Due to the increasing demand of machine processing of handwritten characters, this area of pattern recognition is gaining a lot of attention of researchers. Despite of a great deal of efforts done so far in this direction, still a lot is required to get done. In this work, we are focusing our attention on handwritten Hindi characters. Automatic recognition of Handwritten Hindi characters is complex due to the cursive nature and high level of similarity (such as presence of header line, vertical bar, etc.) in the structure of characters. Hybrid feature extraction approach is followed to extract meaningful features from the collected handwritten characters data. A comparative study of performance analysis of selected neural networks models is performed. Results indicate that the Radial basis function network model perform with 99.09% recognition accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Hindi Character Recognition Backpropagation Algorithm Radon Transform Gabor Filter Feed-forward Neural Network and Radial Basis Function Network.

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