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An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM


Affiliations
1 Department of Computer Science, Mother Teresa Women’s University, Kodaikanal-624102, TN, India
2 School of Physics, Madurai Kamaraj University, Madurai-62502, TN, India
 

This paper presents a new approach of Kohonen neural network based Self Organizing Map (SOM) algorithm for Tamil Character Recognition. Which provides much higher performance than the traditional neural network. Approaches: Step 1:It describes how a system is used to recognize a hand written Tamil characters using a classification approach. The aim of the pre-classification is to reduce the number of possible candidates of unknown character, to a subset of the total character set. This is otherwise known as cluster, so the algorithm will try to group similar characters together. Step 2:Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.

Keywords

Handwritten Character, SOM, Baseline, Statistical, Structural, Crux, Meticulous and Sobel Edge Detection.
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  • An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM

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Authors

R. Indra Gandhi
Department of Computer Science, Mother Teresa Women’s University, Kodaikanal-624102, TN, India
K. Iyakutti
School of Physics, Madurai Kamaraj University, Madurai-62502, TN, India

Abstract


This paper presents a new approach of Kohonen neural network based Self Organizing Map (SOM) algorithm for Tamil Character Recognition. Which provides much higher performance than the traditional neural network. Approaches: Step 1:It describes how a system is used to recognize a hand written Tamil characters using a classification approach. The aim of the pre-classification is to reduce the number of possible candidates of unknown character, to a subset of the total character set. This is otherwise known as cluster, so the algorithm will try to group similar characters together. Step 2:Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.

Keywords


Handwritten Character, SOM, Baseline, Statistical, Structural, Crux, Meticulous and Sobel Edge Detection.