Feature Extraction and Selection in Batak Toba Handwritten Text Recognition
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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.