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Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach

by B.V.Dhandra, R.G.Benne, Mallikarjun Hangarge
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 9
Year of Publication: 2011
Authors: B.V.Dhandra, R.G.Benne, Mallikarjun Hangarge
10.5120/3134-4319

B.V.Dhandra, R.G.Benne, Mallikarjun Hangarge . Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach. International Journal of Computer Applications. 26, 9 ( July 2011), 11-16. DOI=10.5120/3134-4319

@article{ 10.5120/3134-4319,
author = { B.V.Dhandra, R.G.Benne, Mallikarjun Hangarge },
title = { Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 9 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number9/3134-4319/ },
doi = { 10.5120/3134-4319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:20.154593+05:30
%A B.V.Dhandra
%A R.G.Benne
%A Mallikarjun Hangarge
%T Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 9
%P 11-16
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a script independent automatic numeral recognition system is proposed. A single algorithm is proposed for recognition of Kannada, Telugu and Devanagari handwritten numerals. In general the number of classes for numeral recognition system for a scripts/language is 10. Here, three scripts are considered for numeral recognition forming 30 classes. In the proposed method 30 classes have been reduced to 18 classes. The global and local structural features like directional density estimation, water reservoirs, maximum profile distances and fill hole density are extracted. A Probabilistic neural network (PNN) classifier is used in the recognition system. The algorithms efficiency is for various radial values of PNN classifiers, with different experimental setup and obtained encouraging results are compared to other methods proposed in the literature survey. A total of 2550 numeral images of Kannada, Telugu and Devanagari scripts are considered for experimentation. The overall accuracy of the system is 97.20%. The novelty of the proposed method is that, it is script independent, thinning free, fast, and without size normalization.

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Index Terms

Computer Science
Information Sciences

Keywords

OCR Handwritten Numeral Indian scripts Structural feature Probabilistic Neural Net (PNN)

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