Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition

Dr.T. Sitamahalakshmi; Dr.A.Vinay Babu; M. Jagadeesh

Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition

Dr.T. Sitamahalakshmi * §

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Dr.A.Vinay Babu

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M. Jagadeesh

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kvvcmouli

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Gitam Institute of Technology

Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition

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Abstract

The research on recognition of hand written scanned images of documents has witnessed several problems, some of which include recognition of almost similar characters. Therefore it received attention from the fields of image processing and pattern recognition. The system of pattern recognition comprises a two step process. The first stage is the feature extraction and the second stage is the classification. In this paper, the authors propose two classification methods, both of which are based on artificial neural networks as a means to recognize hand written characters of Telugu, a language spoken by more than 100 million people of south India (Negi et al. ,2001). In this model, the authors used Radial Basis Function (RBF) networks and Probabilistic Neural Networks (PNN) for classification. These classifiers were further evaluated using performance metrics such as accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and F measure. This paper is a comparison of results obtained with both the methods. The values of F measure are quite satisfactory and this is a good indication of the suitability of the methods for classification of characters. The values of F-Measure for both the methods approach the value of 1, which is a good indication and out of the two, RBF is a better method than PNN.

References

16 Cites in Article

Reference Format

Krishna Negi,C Bhagavati (2001). An OCR system for Telugu.
A Khawaja,S Tinghi,Rajpur Menon (2006). A " Recognition of printed ©2011 Global Journals Inc. (US) Chinese characters by using neural network.
S Nawaz,M Sarfraz,A Zidouri,W Al-Khatib (2004). An approach to offline Arabic character recognition using neural networks.
Rajan Ashok (2010). Writer Identification and Recognition Using Radial Basis Function.
K Vijay (2004). Radial Basis Function and Subspace Approach For Printed Kannada Text Recognition.
K V Birijesh (2010). Handwritten Hindi Character Recognition Using Multilayer Perceptron and: Radial Basis Function Neural Networks.
R Kunte,R D Samuel (2007). A simple and efficient optical character recognition system for basic symbols in printed Kannada text.
M Vatkin,M Selinger (2001). The system of Handwritten Characters Recognition on the Basis of Legendre Moments and Neural Network.
Touretzky Romero,Thibadeau (1997). Optical Chinese Character Recognition Using Probabilistic Neural Networks.
Khalaf Khatatneh,Ibrahiem Emary,Basem Al- Rifai (2006). Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters.
K Koche (2010). Comparison of Neural Network and Template Matching Technique for Identification of Characters in License Plate.
Jeatrakul,K Wong (2009). Comparing the Performance of Different Neural Networks for Binary Classification Problems.
L Heutte,T Paquet,J Moreau,Y Lecourtier,C Olivier (1998). A structural/statistical feature based vector for handwritten character recognition.
P K Patra,Nayak Nayak,Gobbak (2002). Probabilistic Neural Network for Pattern Classification.
Han Kamber (2009). Data Mining concepts and Techniques.
Steinback Tan,V Kumar (2007). Introduction to Data Mining.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr.T. Sitamahalakshmi, Dr.A.Vinay Babu, M. Jagadeesh, kvvcmouli. 1970. "Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 4).

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GJCST Volume 11 Issue 4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST Classification I.5.1

I.2.7

Submission ReceivedJanuary 10, 2011
Peer Review Double Blind
Handling Editor
Accepted February 4, 2011
Published February 16, 2011

Version of record

v1.2

Issue date

March 13, 2011

Language

English

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Article Matrices

Total Score: 159

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr.T. Sitamahalakshmi, Dr.A.Vinay Babu, M. Jagadeesh ,Dr.K.V.V.Chandra Mouli (PhD/Dr. count: 3)

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Publish Date: 2011 02, Wed

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