ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Ahmed El Sawy, M. Loey and Hazem E.L. Bakry, “Arabic Handwritten Characters Recognition using Convolutional Neural Network”, WSEAS Transactions on Computer Research, Vol. 5, pp. 11-19, 2017.

  • A. Lawgali, “Arabic Character Recognition: A Survey”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 8, No 2, pp. 401-426, 2015.

  • A. Cheung, M. Bennamoun and N.W. Bergmann, “An Arabic Optical Character Recognition System using Recognition-Based Segmentation”, Pattern Recognition, Vol. 34, No. 2, pp. 215-233, 2001.

  • A. Goyal, K. Khandelwal and P. Keshri, “Optical Character Recognition for Handwritten Hindi”, CS229 Notes-Machine Learning, Stanford University, pp. 1-5, 2010.

  • P.P. Angelov and G.U. Xiaowei, “Deep Rule-Based Classifier with Human-Level Performance and Characteristics”, Information Sciences, Vol. 463, pp. 196-213, 2018.

  • M. Shatnawi and S. Abdallah, “Improving Handwritten Arabic Character Recognition by Modeling Human Handwriting Distortions”, ACM Transactions on Asian and Low-Resource Language Information Processing, Vol. 15, No. 1, pp. 1-12, 2015.

  • M. Elzobi, A. Al Hamadi, Z. Al Aghbari, L. Dings and A. Saeed, “Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic using Explicit Segmentation”, Image Processing and Communications Challenges, Springer, pp. 245-254, 2014.

  • M. Elzobi, Moftah, A. Al Hamadi and Z. Al Aghbari, “A Database for Handwritten Arabic and An Optimized Topological Segmentation Approach”, International Journal on Document Analysis and Recognition, Vol. 16, No. 3, pp. 295-308, 2013.

  • M. Pechwitz, S.S. Maddouri, V. Margner, N. Ellouze and H. Amiri, “IFN/ENIT-Database of Handwritten Arabic 575 Words”, Proceedings of International Symposium on Writing and Documents, pp. 127-136, 2002.

  • A. Sahlol and C. Suen, “A Novel Method for the Recognition of Isolated Handwritten Arabic Characters”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-13, 2014.

  • A. Maqqor, A. Halli, K. Satori and H. Tairi, “Off-Line Recognition Handwriting Combination of Mutiple Classifiers”, Proceedings of International Conference on Information Science and Technology, pp. 1-12, 2014.

  • Yasser M. Alginahi, “Arabic Character Segmentation: A Survey”, International Journal on Document Analysis and Recognition, Vol. 16, No. 2, pp. 105-126, 2013.

  • K. Jumari and M.A. Ali, “A Comparative Evaluation of Selected Off-Line Arabic Handwritten Character Recognition Systems: A Survey”, Jurnal Teknologi, Vol. 36, No. 1, pp. 1-18, 2012.

  • Ramzi A. Haraty, “Arabic Text Recognition”, International Arab Journal of Information Technology, Vol. 1, No. 2, pp. 156-163, 2004.

  • S. Khorashadizadeh and A. Latif, “Arabic/Farsi Handwritten Digit Recognition using Histogram of Oriented Gradient and Chain Code Histogram”, International Arab Journal of Information Technology, Vol. 13, No. 4, pp. 1-13, 2016.

  • Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.


Abstract Views: 159

PDF Views: 0




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

Abstract Views: 159  |  PDF Views: 0

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