ICTACT Journal on Image and Video Processing

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Nandinagari Palm Leaf Word Image Retrieval System


Affiliations
1 Department of Computer Science Engineering, Dayananda Sagar University, India
2 Department of Information Technology, University of Mysore, India
     

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This paper provides the first attempt for the recognition of Nandinagari handwritten word in a handwritten Palm Leaf manuscript. We take a set of very aged Palm Leaf and take a representative sample containing over 1000 characters and perform a set of preprocessing steps including background subtraction, de noising using Gaussian filter, contrast enhancement using histogram equalization and binarization using adaptive thresholding technique to obtain high quality readable manuscript. The words are subsequently extracted using annotation method to get a set of 100 meaningful Handwritten Nandinagari words of different sizes. A dictionary of these vocabulary words is formed and stored. Effective corner based feature extraction technique is applied to these images and the corresponding scale and rotation invariant features are extracted and stored in the database. The query word image is then compared with the dictionary words and the matched similar word images are retrieved.

Keywords

Invariant Feature Extraction, Scale Invariant Feature Transform, Background Subtraction, Denoising, Contrast Enhancement, Auto Thresholding.
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  • Nandinagari Palm Leaf Word Image Retrieval System

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Authors

Prathima Guruprasad
Department of Computer Science Engineering, Dayananda Sagar University, India
Guruprasad
Department of Information Technology, University of Mysore, India

Abstract


This paper provides the first attempt for the recognition of Nandinagari handwritten word in a handwritten Palm Leaf manuscript. We take a set of very aged Palm Leaf and take a representative sample containing over 1000 characters and perform a set of preprocessing steps including background subtraction, de noising using Gaussian filter, contrast enhancement using histogram equalization and binarization using adaptive thresholding technique to obtain high quality readable manuscript. The words are subsequently extracted using annotation method to get a set of 100 meaningful Handwritten Nandinagari words of different sizes. A dictionary of these vocabulary words is formed and stored. Effective corner based feature extraction technique is applied to these images and the corresponding scale and rotation invariant features are extracted and stored in the database. The query word image is then compared with the dictionary words and the matched similar word images are retrieved.

Keywords


Invariant Feature Extraction, Scale Invariant Feature Transform, Background Subtraction, Denoising, Contrast Enhancement, Auto Thresholding.

References