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IJMLC 2012 Vol.2(5): 573-577 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.192

Finding Critical Points of Handwritten Persian/Arabic Character

Majid Harouni, Dzulkifli Mohamad, Mohd Shafry Mohd Rahim, and Sami M. Halawani

Abstract—On-line handwritten character recognition system is a special line of research in image processing and pattern recognition field, it can also considered a special process of in academic researches and production fields in the past decade. The general steps in a recognition system are pre-processing, segmentation, feature extraction and classifications. The techniques of pre-processing stage play an excessive role in the system and directly affect the system performance. The focus of this article is on doing pre-processing stage and providing a most desirable data set form the raw data using in feature extraction and then to increase the rate of recognition system. For this reason, the novel algorithm is presented for finding a critical points set of hand-drawn letters. These critical points help extracting the correct structural and statistical features of on-line character handwriting in any given style writing.

Index Terms—Persian and Arabic script, critical points, on-line character recognition, pre-processing.

Majid Harouni is with UTMViCube Lab, Faculty of Computer Science and Information Systems, Universiti Technologi Malaysia, Johor, Malaysia and with the Department of Computer Science, Islamic Azad University, Dolatabad branch, Isfahan, Iran (e-mail: majid.harouni@ gmail.com).
Dzulkifli Mohamad and Mohd Shafry Mohd Rahim are with the UTMViCube Lab, Faculty of Computer Science and Information Systems, Universiti Technologi Malaysia, Johor, Malaysia (e-mail: dzulkifli@utm.my, and shafry@utm.my).
Sami M. Halawani is with the Faculty of Computing and Information Technology, King Abdul Aziz University, Saudi Arabia (e-mail: halawani@kau.edu.sa).

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Cite:Majid Harouni, Dzulkifli Mohamad, Mohd Shafry Mohd Rahim, and Sami M. Halawani, "Finding Critical Points of Handwritten Persian/Arabic Character," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 573-577, 2012.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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