Triangle Geometry Method Based Dominant Distribution Foreground for Digit Recognition

Authors

  • N. A. Arbain Computational Intelligence and Technologies Lab (CIT LAB), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka MALAYSIA.
  • M. S. Azmi Computational Intelligence and Technologies Lab (CIT LAB), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka MALAYSIA.
  • A. K. Muda Computational Intelligence and Technologies Lab (CIT LAB), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka MALAYSIA.
  • A. R. Radzid Computational Intelligence and Technologies Lab (CIT LAB), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka MALAYSIA.
  • S. A. Miswan Computational Intelligence and Technologies Lab (CIT LAB), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka MALAYSIA.

Keywords:

Digit Recognition, Feature Extraction, Support Vector Machine, Triangle Geometry,

Abstract

Digit recognition has been studied for four decades ago. Many approaches and techniques such as Hidden Markov Model, Neural Network, back-propagation and k-nearest neighbor have been applied to recognize the digit images. Recently, the triangle geometry method has been applied to extract features from triangle properties such as ratio, angle and gradient. However, a problem in determining points of a triangle was triggered due to the points’ position in straight line. Thus, a method of extracting triangle features using triangle geometry based on the dominant of distribution foreground for digit recognition has been proposed. The dominant of distribution foreground is referred to the digit of ‘0’ which is represented as a foreground image during the binarization process. The process to determine the triangle points are based on the dominant of distribution foreground. The classifiers of Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) are used to measure the classification accuracies for four types of digit datasets which are HODA, IFCHDB, MNIST, and BANGLA. The comparison results classification of accuracies demonstrated the effectiveness of our proposed method.

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Published

2018-07-04

How to Cite

Arbain, N. A., Azmi, M. S., Muda, A. K., Radzid, A. R., & Miswan, S. A. (2018). Triangle Geometry Method Based Dominant Distribution Foreground for Digit Recognition. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-7), 63–67. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4421

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