Digital Signal Processing

   

Zernike Moments and Neural Networks for Recognition of Isolated Arabic Characters

Authors: Mustapha Oujaoura, Rachid El Ayachi, Mohamed Fakir, Belaid Bouikhalene, Brahim Minaoui

The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition rate of 98% is achieved by using Zernike moments.

Comments: 9 Pages.

Download: PDF

Submission history

[v1] 2012-08-18 21:57:25

Unique-IP document downloads: 550 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus