Author(s)

Ashraf Elnagar, Rahima Bentrcia

Department of Computer Science, College of Sciences, University of Sharjah, Sharjah, UAE.

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 overcome 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 segmented letters. Preprocessing phases that include normalization and feature extraction are required 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%.

Character Segmentation, Handwritten Recognition Systems, Arabic Handwriting, Neural Networks, Multi-Agents

Elnagar, A. and Bentrcia, R. (2015) A Recognition-Based Approach to Segmenting Arabic Handwritten Text. Journal of Intelligent Learning Systems and Applications, 7, 93-103. doi: 10.4236/jilsa.2015.74009.