Author:
Sabir Brahim,Khazri Yassine,Moussetad Mohamed,Touri Bouzekri
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Electrical and Electronic Engineering
Reference29 articles.
1. Djeddi C, Souici-Meslati L. A texture based approach for arabic writer identification and verification In: 2010 International Conference on Machine and Web Intelligence (ICMWI). Algiers, Algeria: IEEE 2010; pp. 115-20.
2. Khader A, Saudagar J, Mohammed HV. Concatenation Technique for Extracted Arabic Characters for,Efficient Content-based Indexing and Searching In: Proceedings of the Second International Conference on,Computer and Communication Technologies. Hyderabad, India: Advances in Intelligent Systems and Computing 2015; pp. 24-6.
3. Nidhal Abdi M, Khemakhem M. A model-based approach to offline text independent Arabic writer identification and Verification. Pattern Recognit 2015; 48 : 1890-903.
4. Malik H, Fahiem MA. Segmentation of printed Urdu scripts using structural features Proc 2nd International Conference in Visualisation (VIZ’09). 191-5.
5. Gazzah S, Ben Amara N. Arabic handwriting texture analysis for writer identification using the DWT-lifting scheme In: The Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). Curitiba, Parana, Brazil: IEEE 2007; 2: pp. 1133-7.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献