RESEARCH ARTICLE


Hardware and Software Co-Design of Arabic Alphabets Recognition Platform for Blind and Visually Impaired Persons



Brahim Sabir1, *, Yassine Khazri1, Mohamed Moussetad1, Bouzekri Touri2
1 LIMAT Lab, Physics Department, Faculty of Science Ben M'Sik - Physics Casablanca, Casablanca, Morocco
2 LAPSTICE Lab, Language and Communication Department, Faculty of Science Ben M'Sik - Physics Casablanca, Casablanca, Morocco


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© 2017 Sabir et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author LAPSTICE Lab, Language and Communication Department Faculty of Science Ben M’Sik, Casablanca, Morocco; Tel: 00212-650352972; E-mail: Sabir.brahim@hotmail.com


Abstract

Background:

Optical character Recognition (OCR) is a technic that converts scanned or printed text images into editable text. Many OCR solutions have been proposed and used for Latin and Chinese alphabets.

However not much can be found about OCRs for the handwriting scripts Arabic Alphabets, and especially to be used for blind and visually impaired persons.

This paper has been an attempt towards the development of an OCR for Arabic Alphabets dedicated to blind and visually impaired persons.

Method:

The proposed Optical Arabic Alphabets Recognition algorithm includes binarization of the inputted image, segmentation, feature extraction and a classification based on neural networks to match read Arabic alphabets with trained pattern.

The proposed algorithm has been developed using Matlab, and the solution was designed to be implemented on hardware platform and can be customized for mobile phones.

Conclusion:

The presented method has the benefit that the accuracy of recognition is comparable to other OCR algorithms.

Keywords: Artificial Neural Network, OCR, Arabic Alphabets, HDL coder, Blind.