ICTACT Journal on Image and Video Processing

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Recognizing Tamil Palm-Leaf Manuscript Characters Using Hybridized Human Perception Based Features


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1 Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
     

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In this paper, we present a zoning strategy for recognizing manuscript character images, based on human structural perception of characters. The deficiencies in a uniform zoning approach are filled by drawing significant information using the proposed method. The obtained feature set when applied on a SVM classifier, substantially improves the recognition rate for character images having structural variation at significant regions of characters. As a initiative, we have formulated the Tamil Palm-Leaf Character dataset. Preliminary results show that the incorporation of this hybridized zoning approach has improved the symbol recognition rate to 9.06% (from 81.07% to 90.13%). The average rejection rate has been nullified using this generic non-symmetrical zoning for the proposed dataset.

Keywords

Manuscript Character Recognition, Visual perception, Triangular Zoning, Shape Based Zoning, Significant Zone Slicing.
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  • Recognizing Tamil Palm-Leaf Manuscript Characters Using Hybridized Human Perception Based Features

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Authors

Paramasivam Muthan Eswaran
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
Dinesh Manib
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
Sabeenian Royappan Savarimuthu
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India

Abstract


In this paper, we present a zoning strategy for recognizing manuscript character images, based on human structural perception of characters. The deficiencies in a uniform zoning approach are filled by drawing significant information using the proposed method. The obtained feature set when applied on a SVM classifier, substantially improves the recognition rate for character images having structural variation at significant regions of characters. As a initiative, we have formulated the Tamil Palm-Leaf Character dataset. Preliminary results show that the incorporation of this hybridized zoning approach has improved the symbol recognition rate to 9.06% (from 81.07% to 90.13%). The average rejection rate has been nullified using this generic non-symmetrical zoning for the proposed dataset.

Keywords


Manuscript Character Recognition, Visual perception, Triangular Zoning, Shape Based Zoning, Significant Zone Slicing.

References