Recognition-based Segmentation of Arabic Handwriting

Ashraf Elnagar, Rahima Bentrcia



Several segmentation approaches proposed in the past decades for Arabic handwritings suffer from over-segmentation. This problem decomposes a single letter into small strokes. The aim of this work is to handle this problem using Artificial Neural Networks with a set of combination rules to keep the correct strokes (letters) and combine the over-segmented ones to intact letters in a correct way. After word segmentation, the resulting segments are normalized. Then, a set of features was extracted from each segment and passed to Artificial Neural Network to be recognized. Finally, proposed combination rules were applied to unrecognized strokes and to specific recognized letters. The success rate of the experimental results exceeds 95%.


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Paper Citation

in Harvard Style

Elnagar A. and Bentrcia R. (2009). Recognition-based Segmentation of Arabic Handwriting . In Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009) ISBN 978-989-8111-89-0, pages 83-92. DOI: 10.5220/0002179400830092

in Bibtex Style

author={Ashraf Elnagar and Rahima Bentrcia},
title={Recognition-based Segmentation of Arabic Handwriting},
booktitle={Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009)},

in EndNote Style

JO - Proceedings of the 9th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2009)
TI - Recognition-based Segmentation of Arabic Handwriting
SN - 978-989-8111-89-0
AU - Elnagar A.
AU - Bentrcia R.
PY - 2009
SP - 83
EP - 92
DO - 10.5220/0002179400830092