Abstract—On-line handwriting recognition is one of the most
successful applications in the area of pattern recognition.
Though this field is quite matured, yet the research issues are
still challenging, particularly in handwriting character
recognition, where the problems are still wide open. The OCR
system for printed characters is almost done, though it cannot
guarantee for 100% accuracy. However, the research works in
recognition of Arabic handwriting are still at the beginning and
require more attention. This paper presents the novel on-line
Arabic handwriting character recognition. An efficient
approach is introduced here to divide it into some particular
component. A set of features are extracted from these
components, and then encoded for the classification stage. The
system classification is implemented by using two processes, i.e.
weight initialization in back propagation, and with multilayer
perceptron neural network. Finally, the proposed system was
tested on a database of Arabic handwritten samples.
Index Terms—Feature extraction; on-line character
recognition; classification.
Majid Harouni is a Ph.D. candidate at UTMViCube Lab, Faculty of
Computer Science and Information Systems, Universiti Technologi Malaysia,
P. O. Box 81310, Skudai, Johor, Malaysia and with the Department of
Computer Science, Islamic Azad University, Dolatabad branch, Isfahan, Iran
(e-mail: majid.harouni@ gmail.com).
Dzulkifli Mohamad, Mohd Shafry Mohd Rahim, and Mahboubeh Afzali
are with the UTMViCube Lab, Faculty of Computer Science and Information
Systems, Universiti Technologi Malaysia, P. O. Box 81310, Skudai, Johor,
Malaysia (e-mail: dzulkifli@utm.my, shafry@utm.my, and
afzali_mahboobeh@yahoo.com respectively).
Sami M. Halawani is with the Faculty of Computing and Information
Technology, King Abdul Aziz University, Saudi Arabia (e-mail:
halawani@kau.edu.sa).
Cite:Majid Harouni, Dzulkifli Mohamad, Mohd Shafry Mohd Rahim, Sami M. Halawani, and Mahboubeh Afzali, "Handwritten Arabic Character Recognition Based on Minimal Geometric Features," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 578-582, 2012.