Abstract This paper proposed a new architecture for Offline Isolated Arabic Handwriting Character Recognition System Based on SVM (OIAHCR). An Arabic handwriting dataset also proposed for training and testing the proposed system. Although half of the dataset used for training the Support Vector Machine (SVM) and the second half used for testing, the system achieved high performance with less training data. Besides, the system achieved best recognition accuracy 99.64% based on several feature extraction methods and SVM classifier. Experimental results show that the linear kernel of SVM is convergent and more accurate for recognition than other SVM kernels.

References

[1] Abdullah M., Al-Harigy L., and Al-Fraidi H., “Off-Line Arabic Handwriting Character Recognition Using Word Segmentation,” Journal of Computing, vol. 4, no. 3, pp. 40-44, 2012.

[2] Abed M. and Alasad H., “High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 2, pp. 145-152, 2015.

[3] Abdurazzag A. and Rehie S., “Off-Line Omni- style Handwriting Arabic Character Recognition System Based on Wavelet Compression,” ARISER, vol. 3, no. 4, pp. 123-135, 2007.

[4] Abuhaiba I. and Ahmed P., “Restoration of Temporal Information in Off-Line Arabic Handwriting,” Pattern Recognition, vol. 26, no. 7, pp. 1009-1017, 1993.

[5] Abuhaiba I., Mahmoud S., and Green R., “Recognition of Handwritten Cursive Arabic Characters,” IEEE Transactionon Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 664-672, 1994.

[6] Abdul Hassan A. and Kadhm M., “An Efficient Preprocessing Framework for Arabic Handwriting Recognition System,” Diyala Journal For Pure Sciences, vol. 12, no. 3, pp. 147-163, 2016.

[7] Abdul Hassan A. and Kadhm M., “Arabic Handwriting Text Recognition Based on Efficient Segmentation, DCT and HOG Features,” International Journal of Multimedia and Ubiquitous Engineering, vol. 11, no. 10, pp. 83-92, 2016.

[8] Abdul Hassan A. and Kadhm M., “An Efficient Image Thresholding Method for Arabic Handwriting Recognition System,” Engineering and Technology Journal, vol. 34, no. 1, pp. 26- 34, 2016.

[9] AL-Shareefi F., “A Haar Wavelet-Based Zoning for Offline Arabic Handwritten Character Recognition,” Journal of Babylon University/Pure and Applied Sciences, vol. 23, no. 2, pp. 575-585, 2015.

[10] Al-Yousefi H. and Udpa S., “Recognition of Arabic Characters,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 14, no. 8, pp. 853-857, 1992.

[11] Ben Amor N. and Essoukri B., “Hidden Markov Models and Wavelet Transform in Multifont Arabic Characters Recognition,” in Proceedings of International Conference on Computing, Communications and Control Technologies, United State, pp. 50-54, 2005.

[12] Chang C. and Lin C., “Libsvm: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-27, 2011.

[13] Clocksin W. and Fernando P., “Towards Automatic Transcription of Syriac Handwriting,” in Proceedings of the 12th 472 The International Arab Journal of Information Technology, Vol. 16, No. 3, May 2019 International Conference on Image Analysis and Processing, Mantova, pp. 664-669, 2003.

[14] Cortes C., “Support-Vector Networks,” Machine Learning, vol. 20, no 3, pp 273-297, 1995.

[15] Dalal N. and Triggs B., “Histograms of Oriented Gradients for Human Detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 886-893, 2005.

[16] Govindan V. and Shevaprasad A., “Character Recognition-A Review,” Pattern Recognition, vol. 23, no.7, pp. 671-683, 1990.

[17] Haraty R. and Ghaddar C., “Arabic Text Recognition,” The International Arab Journal of Information Technology, vo. 1, no. 2, pp. 156-163, 2004.

[18] Haraty R. and Hamid A., “Segmenting Handwritten Arabic Text,” in Proceedings of the International Conference on Computer Science, Software Engineering, Information Technology, E-Business, and Applications, Foz do Iguazu, pp. 95-101, 2002.

[19] Jiang J., Weng Y., and LI P., “Dominant Color Extraction in DCT Domain,” Image and Vision Computing, vol. 24, no. 12, pp. 1269-1277, 2006.

[20] Kadhm M. and Abdul Hassan A., “ACRS: Arabic Character Recognition System Based on Multi Features Extraction Methods,” International Journal of Scientific and Engineering Research vol. 6, no.10, pp. 665-661, 2015.

[21] Khorsheed M., “Recognizing Handwritten Arabic Manuscripts Using A Single Hidden Markov Model,” Pattern Recognition Letters, vol. 24, no. 14, pp. 2235-2242, 2003.

[22] Mori S., Nishida H., and Yamada H., Optical Character Recognition, John Wiley, 1999.

[23] Sahlol A. and Suen C., “A Novel Method for the Recognition of Isolated Handwritten Arabic Characters,” Technical Report, Cornell University, 2013.

[24] Vapnik V., the Nature of Statistical Learning Theory, Springer, 1999.

[25] Zaki F., Elkonyaly S., Elfattah A., and Enab Y., “A New Technique for Arabic Handwriting Recognition,” in Proceedings of the 11th International Conference for Statistics and Computer Science, Cairo, pp. 171-180, 1986. Mustafa Kadhm is a lecturer at the Department of Computer Engineering Techniques, Imam Ja'afar Al-Sadiq University. He received his B.S. degrees in Software Engineering from Al- Mansour University College, Baghdad, Iraq in 2009 and M.S. in Information Technology from University of Tun Abdulrazak, Malaysia in 2012. His research interests include Artificial Intelligence, Image Processing, Computer Vision, Pattern Recognition, and Data Mining. Alia Abdul Hassan, Date of Birth: 28-3- 1971. Computer Science Dep. /University of Technology. B.Sc. Computer Science/ University of Technology/1993, MSc. Computer Science/ University of Technology/ 1999, Ph.D. Computer Science/ University of Technology /2004. Assistant professor since 20/3/2008. Position Deputy Dean of Computer Science Department since Feb 2015 till now. Supervised on 22 MSc. &PhD thesis in Computer Science since 2007. Publications Published more than (45) papers in International Conferences and Journals. Current Research Interests Soft computing, Green computing, AI, Data Mining, Software engineering, Document Recognition, Electronic Management, Computer security.