Feature Extraction Comparison in Handwriting Recognition of Batak Toba Alphabet

https://doi.org/10.22146/ijitee.31969

Novie Theresia Br Pasaribu(1*), M. Jimmy Hasugian(2)

(1) Program Studi Teknik Elektro, Universitas Kristen Maranatha
(2) Program Studi Teknik Elektro, Universitas Kristen Maranatha
(*) Corresponding Author

Abstract


Offline handwriting recognition is one of the most prominent research topics due to its tremendous application and high variability as well. This paper covers the offline Batak Toba handwritten text recognition, from the noise removal, the process of feature extraction until the recognition by using several classifiers. Experiments show that elliptic fourier descriptor (EFD) is the most discriminative feature and Mahalanobis distance (MD) outperforms the two others classifier.

Keywords


Batak Toba Alphabet, handwriting recognition, feture extraction, classification.

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References

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DOI: https://doi.org/10.22146/ijitee.31969

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