Journal of Intelligent Learning Systems and Applications, 2015, 7, 93-103 Published Online November 2015 in SciRes. http://www.scirp.org/journal/jilsa http://dx.doi.org/10.4236/jilsa.2015.74009 How to cite this paper: Elnagar, A. and Bentrcia, R. (2015) A Recognition-Based Approach to Segmenting Arabic Handwrit- ten Text. Journal of Intelligent Learning Systems and Applications, 7, 93-103. http://dx.doi.org/10.4236/jilsa.2015.74009 A Recognition-Based Approach to Segmenting Arabic Handwritten Text Ashraf Elnagar, Rahima Bentrcia Department of Computer Science, College of Sciences, University of Sharjah, Sharjah, UAE Received 22 July 2015; accepted 1 November 2015; published 4 November 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Segmenting Arabic handwritings had been one of the subjects of research in the field of Arabic character recognition for more than 25 years. The majority of reported segmentation techniques share a critical shortcoming, which is over-segmentation. The aim of segmentation is to produce the letters (segments) of a handwritten word. When a resulting letter (segment) is made of more than one piece (stroke) instead of one, this is called over-segmentation. Our objective is to over- come this problem by using an Artificial Neural Networks (ANN) to verify the resulting segment. We propose a set of heuristic-based rules to assemble strokes in order to report the precise seg- mented letters. Preprocessing phases that include normalization and feature extraction are re- quired as a prerequisite step for the ANN system for recognition and verification. In our previous work [1], we did achieve a segmentation success rate of 86% but without recognition. In this work, our experimental results confirmed a segmentation success rate of no less than 95%. Keywords Character Segmentation, Handwritten Recognition Systems, Arabic Handwriting, Neural Networks, Multi-Agents 1. Introduction Automatic recognition of handwritings is developing well as a result of research contributions in this field. There are two main types of character recognition systems, namely, on-line and off-line systems. On-line sys- tems recognize handwritings input on a tablet or any a similar device by a digital pen or stylus. Off-line systems deal with images of handwritings stored. The temporal information in the first type would positively contribute to the recognition process. Such information is absent in the second type, which makes this problem in particular more challenging.