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Feature Extraction and Selection in Batak Toba Handwritten Text Recognition

Novie Theresia Br Pasaribu, M. Jimmy Hasugian

Abstract



Batak Toba is one of the tribes in Indonesia that has its own language and alphabet. However, not many Batak Toba people are familiar with the alphabet. In this paper, we use several feature extraction methods in the recognition process of Batak Toba handwritten text. For some features, between or within-class scatter matrix criterion is used to select the significant features. The k-NN classifier is used in the recognition step. The results show that elliptic Fourier descriptor is the most superior features that has recognition percentage greater than the other categories.

Keywords


Batak Toba text, Feature extraction, Elliptic Fourier descriptor, Feature selection, k-NN classifier.

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