Research Article Open Access

A Hybrid Feature Extraction Method for Accuracy Improvement in “Aksara Lontara” Translation

Intan Sari Areni1, Asyraful Insan Asry1 and Indrabayu1
  • 1 Universitas Hasanuddin, Indonesia

Abstract

An Optical Character Recognition (OCR) of “Aksara Lontara” has been constructed using a novel combination of feature extraction methods in this study. The ancient font of “Lontara” is then translated into Bahasa Indonesia to help non-native language to learn this language. Two powerful extraction feature methods, i.e., Modified Direction Feature (MDF) and Fourier Descriptor (FD) are stages combined to deal with two dominant phases of the Lontara font. The classification process is conducted using Support Vector Machine (SVM) as a fast and straightforward learning method deal with 23 fonts in image containing of 150×120 pixels. In this research, 50 verbs were used for training and 30 verbs for validating the system. The results show that system can reach 96% accuracy using this hybrid in extraction feature with kernel variable of C = 3 and σ = 8.

Journal of Computer Science
Volume 13 No. 9, 2017, 393-399

DOI: https://doi.org/10.3844/jcssp.2017.393.399

Submitted On: 19 April 2017 Published On: 27 August 2017

How to Cite: Areni, I. S., Asry, A. I. & Indrabayu, (2017). A Hybrid Feature Extraction Method for Accuracy Improvement in “Aksara Lontara” Translation. Journal of Computer Science, 13(9), 393-399. https://doi.org/10.3844/jcssp.2017.393.399

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Keywords

  • Lontara script
  • Modified Direction Feature
  • Fourier Descriptor
  • Support Vector