Abstract
Optical Character Recognition Systems (OCR) provide human-machine interaction and are widely used in many applications. Classification is the most important step in an OCR system. Support Vectors Machines (SVM) is among the tool of classification that appears these days. This tool proves its ability to discriminate between the forms and gives encouraging result. In this paper, we present an overview of the Arabic optical character recognition (AOCR) work done using SVM classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amara, M., Zidi, K.: Feature Selection Using a Neuro-Genetic Approach For Arabic Text Recognition. In: Proceedings of the International Conference on Metaheuristics and Nature Inspired Computing, Tunisia (2012)
Vladimir Vapnik, N.: Statistical Learning Theory. John Wiley & Sons Inc., New York (1998)
Zhou, W., Zhang, L., Jiao, L.: Linear programming support vector machines. Pattern Recognition Society (2002)
Gay Thomé, A.C.: SVM Classifiers – Concepts and Applications to Character Recognition, book edited by Xiaoqing Ding (2012)
Ghosh, D., Dube, T., Shivaprasad, A.P.: Script Recognition – A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
Borovikov, E.A.: An Evaluation of Support Vector Machines as a Pattern Recognition Tool. University of Maryland at College Park (1999)
Abdulla, M., Paschos, G.: Effective Arabic Character Recognition using Support Vector Machines. In: Innovations and Advanced Techniques in Computer and Information Sciences and Engineering, pp. 7–11 (2007)
Chang, C.-C., Lin, C.-J.: LIBSVM – A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Boucharebet, F., Hamdi, R., Bedda, M.: Handwritten Arabic character recognition based on SVM Classifier. In: Information and Communication Technologies: From Theory to Applications, Algeria (2008)
Faouzi, Z., Abdelhamid, D., Mohamed, B.: An Approach Based on Structural Segmentation for the Recognition of Arabic Handwriting. In: Advances in Information Sciences and Service Sciences, vol. 2 (2010)
Al-Hamadi, A., Saeed, A., Dings, L., Elzobi, M.: Arabic handwriting recognition using Gabor wavelet transform and SVM. In: Signal Processing (ICSP), Germany (2012)
Guericke, O.V.: IESK-arDB-A database for off-line handwritten Arabic words, http://www.iesk-ardb.ovgu.de/
Alalshekmubarak, A., Hussain, A., Wang, Q.-F.: Off-line handwritten arabic word recognition using SVMs with normalized poly kernel. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 85–91. Springer, Heidelberg (2012)
Zanchettin, C., Azevedo, W.W., Bezerra, B.L.D.: A KNN-SVM hybrid model for cursive handwriting recognition. In: Proceedings of the International Conference on Neural Networks (IJCNN), Brazil, pp. 1–8 (2012)
Hassanien, A.E., Suraj, Z., Slezak, D., Lingras, P.: Rough computing: Theories, technologies and applications. IGI Publishing Hershey, PA (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Amara, M., Zidi, K., Zidi, S., Ghedira, K. (2014). Arabic Character Recognition Based M-SVM: Review. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-13461-1_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13460-4
Online ISBN: 978-3-319-13461-1
eBook Packages: Computer ScienceComputer Science (R0)