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 JILSA  Vol.7 No.4 , November 2015
A Recognition-Based Approach to Segmenting Arabic Handwritten Text
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 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%.
Cite this paper: 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.
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