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has been cited by the following article:

• TITLE: A Multi-Agent Approach to Arabic Handwritten Text Segmentation

  AUTHORS: Ashraf Elnagar, Rahima Bentrcia

  KEYWORDS: Character Segmentation; Handwritten Recognition Systems; Multi-Agents; Arabic Handwriting

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.3, August 30, 2012

  ABSTRACT: The segmentation of individual words into characters is a vital process in handwritten character recognition systems. In this paper, a novel approach is proposed to segment handwritten Arabic text (words). We consider the “Naskh” font style. The segmentation algorithm employs seven agents in order to detect regions where segmentation is illegal. Feature points (end points) are extracted from the remaining regions of the word-image. Initially, the middle of every two successive end points is considered as a candidate segmentation point based on a set of rules. The experimental results are very promising as we achieved a success rate of 86%.