Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

Sundaram, Suresh and Ramakrishnan, AG (2008) Two Dimensional Principal Component Analysis for Online Tamil Character Recognition. In: XI Int. Conf Frontiers in Handwriting Recognition (ICFHR 2008).

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Abstract

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

Item Type:	Conference Paper
Keywords:	Principal Component Analysis(PCA);2DPCA;Mahalanobis Distance.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	20 Sep 2011 07:26
Last Modified:	20 Sep 2011 07:26
URI:	http://eprints.iisc.ac.in/id/eprint/40565

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