Abstract In this paper, an online isolated Arabic handwritten character recognition system is introduced. The system can be adapted to achieve the demands of hand$held and dig ital tablet applications. To achieve this goal, despite of single neural networks, four neural networks are used, one for ea ch cluster of characters. Feed forward back propaga tion neural networks are used in classification process. This approach i s employed as classifiers due to the low computatio n overhead during training and recall process. The system recognizes on$line isolated Arabic character and achieves an a ccuracy rate 9 ٥.7% from untrained writers and 99.1% for trained writer s.

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[13] Zafar M., Dzulkifli M., and Razid M., Write Independent Online Handwritten Character Recognition using A Simple Approach, The International Arab Journal of Information Technology , vol. 5, no. 3, pp. 476'484, 2006. Basem Alijla a Lecturer in the Department of Information Technology Systems, Faculty of Information Technology, Islamic university of Gaza since March, 2005 till now. Earned my BSc of computer science from Islamic University of Gaza in 2002, and hold a MS of computer science from Yarmouk University Jordan in 2005, Irbid. My Interested area is Pattern Recognit ions and Natural Language Understanding. OIAHCR: Online Isolated Arabic Handwritten Character Recognition Using Neural Network 351 Kathrein Kwaik is a Database Programmer in Information Technology and Telecommunication Department at General Personal Council in Gaza, Palestine. She Holds a BSc degree in Information Technology Systems from Islamic University of Gaza, 2008. Kwaik has more than 10 works in analyzing, designing and implementing system for various companies and with different languages. She worked as teacher assistance in Information technology faculty in Islamic Universit y of Gaza from 2008 to 2010. Nowadays she is preparin g for master thesis in the same University in Palesti ne.