ICTACT Journal on Image and Video Processing

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Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification


Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
2 Department of Computer Science, University of Memphis, United States
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, Algeria
     

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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.

Keywords

Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD.
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  • Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification

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Authors

Soumia Djaghbellou
Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria
Zahid Akhtar
Department of Computer Science, University of Memphis, United States
Abderraouf Bouziane
LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, Algeria
Abdelouahab Attia
Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria

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.

Keywords


Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD.

References