SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Al-Qahtani, S.A. and Khorsheed, M.S. (2004) A HTK-Based System to Recognize Arabic Script. Proceedings of the 4th IASTED International Conference on Visualization, Imaging, and Image Processing, Marbella, 6-8 September 2004.

has been cited by the following article:

  • TITLE: A Recognition-Based Approach to Segmenting Arabic Handwritten Text

    AUTHORS: Ashraf Elnagar, Rahima Bentrcia

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

    JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.7 No.4, November 4, 2015

    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%.