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Akhtar, Zahid
- Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2195-2200Abstract
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
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- A Survey on Machine And Deep Learning for Detection of Diabetic Retinopathy
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Authors
Affiliations
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ