An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals

Authors

  • Jaafar M. Alghazo College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
  • Ghazanfar Latif College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
  • Ammar Elhassan College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
  • Loay Alzubaidi College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
  • Ahmad Al-Hmouz Department of Electrical and Computer Engineering, King Saud University, Riyadh Saudi Arabia.
  • Rami Al-Hmouz Faculty Of Information Technology, Middle East University, Amman, Jordan.

Keywords:

Arabic Numerals, Persian Numerals, Structural Features, Random Forest, Numerals Recognition, Digit Recognition, Arabic Digits, Persian Digits,

Abstract

With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques.

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Published

2017-09-15

How to Cite

M. Alghazo, J., Latif, G., Elhassan, A., Alzubaidi, L., Al-Hmouz, A., & Al-Hmouz, R. (2017). An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-10), 33–40. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2703

Issue

Vol. 9 No. 2-10: Technology Transforming Lives II

Section

Articles

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