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A Neural Nework Approach to Printed Devanagari Character Recognition

by Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 22
Year of Publication: 2013
Authors: Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil
10.5120/10230-4917

Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil . A Neural Nework Approach to Printed Devanagari Character Recognition. International Journal of Computer Applications. 61, 22 ( January 2013), 33-37. DOI=10.5120/10230-4917

@article{ 10.5120/10230-4917,
author = { Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil },
title = { A Neural Nework Approach to Printed Devanagari Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 22 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number22/10230-4917/ },
doi = { 10.5120/10230-4917 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:19.943016+05:30
%A Surendra P. Ramteke
%A Ramesh D. Shelke
%A Nilima P. Patil
%T A Neural Nework Approach to Printed Devanagari Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 22
%P 33-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we deals with the recognition of printed Devanagari Characters with neural network approach. The paper shows measurement of the effectiveness classifier in terms of precision in recognition. It is also a benchmark for testing and verifying new pattern recognition theories and algorithms. 10 samples of each devanagari vowel and consonant from 10 different printed kruti dev font have been sampled and database was prepared. After segmentation, an individual image is normalized to 100X100 pixel size. Seven moment invariants (MIs) are evaluated for each character along with GLCM properties like Contrast, Homogeneity, Entropy, Correlation , color domain and histogram. The Neural network function has been adopted for classification. The main objective of the paper is to test the possibility of using the MI for recognition of printed character independent of its Size, slant and other variations.

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Index Terms

Computer Science
Information Sciences

Keywords

Histogram Moment Invariant GLCM color domain ANN

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