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Vellasques, A.E. Oliveira, L.S., Britto Jr., A.S., Koerich, A.L. and Sabourin, R. (2008) Filtering Segmentation Cuts for Digit String Recognition. Pattern Recognition, 41, 3044-3052.
http://dx.doi.org/10.1016/j.patcog.2008.03.019

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%.