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%.