ARABIC HANDWRITTEN CHARACTERS RECOGNITION VIA MULTI-SCALE HOG FEATURES AND MULTI-LAYER DEEP RULE-BASED CLASSIFICATION

Abstract
Optical character recognition systems for handwritten Arabic language still face challenges, owing to high level of ambiguity, complexity and tremendous variations in human writing styles. In this paper, we propose a new and effective Arabic handwritten characters recognition framework using multi-scale histogram oriented gradient (HOG) features and the deep rule-based classifier (DRB). In the feature extraction stage, the proposed framework combines multi-scale HOG features, and then the DRB is applied on comprehensive HOG features to obtain the final classification label/class. This study involves experimental analyses that were conducted on the publicly available cursive Arabic Handwritten Characters Database (AHCD) containing 16800 characters. Experimental results demonstrate the efficacy of the proposed recognition system compared to the existing state-of-the-art systems.

Authors
Soumia Djaghbellou1, Zahid Akhtar2, Abderraouf Bouziane3, Abdelouahab Attia4
Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria1,3,4, University of Memphis, United State of America2

Keywords
Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 10 , Issue: 4 )
Date of Publication :
May 2020
DOI :

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