Comparative Study of KNN, SVM and SR Classifiers in Recognizing Arabic Handwritten Characters Employing Feature Fusion

Abu Sayeed Ahsanul Huque, Mainul Haque, Haidar A. Khan, Abdullah Al Helal, Khawza I. Ahmed


This paper evaluates and compares the performance of K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Sparse Representation Classifier (SRC) for recognition of isolated Arabic handwritten characters. The proposed framework converts the gray-scale character image to a binary image through Otsu thresholding, and size-normalizes the binary image for feature extraction. Next, we exploit image down-sampling and the histogram of image gradients as features for image classification and apply fusion (combination) of these features to improve the recognition accuracy. The performance of the proposed system is evaluated on Isolated Farsi/Arabic Handwritten Character Database (IFHCDB) – a large dataset containing gray scale character images. Experimental results reveal that the histogram of gradient consistently outperforms down-sampling based features, and the fusion of these two feature sets achieves the best performance. Likewise, SRC and SVM both outperform KNN, with the latter performing the best among the three. Finally, we achieved a commanding accuracy of 93.71% in character recognition with fusion of features classified by SVM, where 92.06% and 91.10% is achieved by SRC and KNN respectively.


Arabic OCR; Support Vector Machine; Sparse Representation Classifier; Fusion

Full Text:



A. A. A. Ali and M. Suresha, “Arabic handwritten character recognition using machine learning approaches,” in 2019 Fifth International Conference on Image Information Processing (ICIIP). IEEE, 2019, pp. 187–192.

S.N. Srihari and G. Ball, Guide to OCR for Arabic Scripts, Springer London, 2012.

B. E. Sawe, “What is the Most Spoken Language in the World?,” WorldAtlas, Jun. 7, 2019. [Online]. Available: [Accessed: Mar. 08, 2020].

A. Lawgali, A. Bouridane, M. Angelova and Z. Ghassemlooy, “Handwritten Arabic character recognition: Which feature extraction method?,” International Journal of Advanced Science and Technology, vol. 34, Sep. 2011, pp. 1-8.

K. Addakiri and M. Bahaj. “On-line Handwritten Arabic Character Recognition using Artificial Neural Network,” International Journal of Computer Applications, vol. 55, no. 13, Oct. 2012, pp. 42-46.

B. Alijla and K. Kwaik, “Oiahcr: online isolated arabic handwritten character recognition using neural network,” Int. Arab J. Inf. Technol., vol. 9, no. 4, July 2012, pp. 343-351.

F.H. Zawaideh, “Arabic handwritten character recognition using modified multi-neural network,” Journal of Emerging Trends in Computing and Information Sciences, vol. 3, no. 7, July 2012, pp. 1021-1026.

M.T. Parvez and S. Mahmoud, “Arabic Handwritten Alphanumeric Character Recognition using Fuzzy Attributed Turning Functions”, Proc. Workshop in Frontiers in Arabic Handwriting Recognition, in conjunction with 20th ICPR, Istanbul, Turkey, Aug. 2010, pp. 9-14.

A. Saidani, A. Kacem Echi, and A. Belaid, “Arabic/latin and machineprinted/handwritten word discrimination using hog-based shape descriptor,” ELCVIA: electronic letters on computer vision and image analysis, pp. 0001–23, 2015.

M. Biglari, F. Mirzaei, and J. G. Neycharan, “Persian/arabic handwritten digit recognition using local binary pattern,” International Journal of Digital Information and Wireless Communications (IJDIWC), vol. 4, no. 4, pp. 486–492, 2014.

K. S. Younis, “Arabic handwritten character recognition based on deep convolutional neural networks,” Jordanian Journal of Computers and Information Technology (JJCIT), vol. 3, no. 3, pp. 186–200, 2017.

A. Lawgali, M. Angelova, and A. Bouridane, "HACDB: Handwritten Arabic characters database for automatic character recognition," Proc. IEEE EUVIP, Paris, France, June 2013, PP. 255-259.

S. Mozaffari, K. Faez, F. Faradji, M. Ziaratban and S.M. Golzan, “A comprehensive isolated Farsi/Arabic character database for handwritten OCR research,” Proc. IWFHR, La Baule, France, Oct. 2006, pp 385-389.

C.-L. Liu and C.Y. Suen, “A new benchmark on the recognition of handwritten bangla and farsi numeral characters,” Pattern Recognition, vol. 42, no. 12, Dec. 2009, pp. 3287–3295.

D. Sudha and M. Ramakrishna, “Comparative study of features fusion techniques,” in 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). IEEE, 2017, pp. 235–239.

B. Fernando, E. Fromont, D. Muselet, and M. Sebban, “Discriminative feature fusion for image classification.” Proc. IEEE CVPR, Rhode Island, USA, June 2012, pp. 3434-3441.

Y. Alginahi, Preprocessing techniques in character recognition, INTECH Open Access Publisher, 2010.

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, Jan. 1979, pp. 62-66.

M. G. Sarowar, A. A. Jamal, A. Saha, and A. Saha, “Performance evaluation of feature extraction and dimensionality reduction techniques on various machine learning classifiers,” in 2019 IEEE 9th International Conference on Advanced Computing (IACC). IEEE, 2019, pp. 19–24.

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Comparison of feature extraction techniques for handwritten digit recognition with a photonic reservoir computer,” in International Conference on Artificial Neural Networks. Springer, 2019, pp. 175–179.

J.A. Rodrıguez, and F. Perronnin, “Local gradient histogram features for word spotting in unconstrained handwritten documents,” Proc. 1st ICFHR, 2008, pp. 7-12.

N. Suguna, and K. Thanushkodi, “An improved K-nearest neighbor classification using Genetic Algorithm,” International Journal of Computer Science Issues, vol. 7, no. 2, 18-21.

J. Gou, W. Qiu, Z. Yi, X. Shen, Y. Zhan, and W. Ou, “Locality constrained representation-based k-nearest neighbor classification,” Knowledge-Based Systems, vol. 167, pp. 38–52, 2019.

C.J.C. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, June 1998, pp. 121-167.

J. S. Paiva, J. Cardoso, and T. Pereira, “Supervised learning methods for pathological arterial pulse wave differentiation: a svm and neural networks approach,” International journal of medical informatics, vol. 109, pp. 30–38, 2018.

V. Passricha, and R. K. Aggarwal, “Convolutional support vector machines for speech recognition,” International Journal of Speech Technology, 2018, pp.1-9.

P. Wang, and C. Xu, “Robust Face Recognition via Sparse Representation”.

H.A. Khan, A.A. Helal, K.I. Ahmed, “Handwritten bangla digit recognition using sparse representation classifier,” Proc. IEEE ICIEV, Dhaka, Bangladesh, May 2014, pp. 1–6.

D.L. Donoho, “For most large underdetermined systems of linear equations the minimal l1norm solution is also the sparsest solution,” Communications on Pure and Applied Mathematics, vol. 59, no. 6, June 2006, pp. 797–829.

Al-Ohali, Yousef, M. Cheriet, and C. Suen, “Databases for recognition of handwritten Arabic cheques,” Pattern Recognition, vol. 36, no. 1, Jan. 2003, pp. 111-121.

L. Souici, N. Farah, T. Sari, and M. Sellami, “Rule based neural networks construction for handwritten arabic city-names recognition,” AIMSA, Aug. 2004, pp. 331-340.

C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no.3, Apr. 2011, p. 27.

G. Madzarov, D. Gjorgjevikj, and I. Chorbev, “A multi class SVM classifier utilizing binary decision tree,” Informatica, vol. 33, no. 2, May 2009, pp. 233-241.



  • There are currently no refbacks.

Copyright (c) 2019 Abu Sayeed Ahsanul Huque, Mainul Haque, Haidar A. Khan, Abdullah Al Helal, Khawza I. Ahmed

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Signal and Image Processing Letters
ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website :
Email 1 :
Email 2 :


Creative Commons License

View My Stats