Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine
Corresponding Author(s) : Muhammad Athoillah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 4, No 2, May 2019
Abstract
Handwritten recognition is how computer can identify a handwritten character or letter from a document, an image or another source. Recently, many devices provide a feature using handwritten as an input such as laptops, smartphones, and others, affecting handwritten recognition abilities become important thing. As the mother tongue of Muslims, and the only language used in holy book Al Qur’an, therefore recognizing in arabic character is a challenging task. The outcome of that recognizing system has to be quite accurate, the results of the process will impact on the entire process of understanding the Qur’an lesson. Basically handwritten recognition problem is part of classification problem and one of the best algorithm to solve it is Support Vector Machine (SVM). By finding a best separate line and two other support lines between input space data in process of training, SVM can provide the better result than other classify algorithm. Although SVM can solve the classify problem well, SVM must be modified with kernel learning method to be able to classify nonlinear data. However, determining the best kernel for every classification problem is quite difficult. Therefore, some technique have been developed, one of them is Multi Kernel Learning (MKL). This technique works by combining some kernel function to be one kernel with an equation. This framework built an application to recognize handwritten arabic numeral character using SVM algorithm that modified with Kernel Learning Method. The result shows that the application can recognize data well with average value of Accuracy is 84,37%
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- Q, ye and Doerman D, “Tect Detection and Recognition in Imagery : A Survey”. IEEE Transactions on Pattern Analysisi and Machine Intelligence., vol. 37, no. 7, pp. 1480–1500, 2015.
- Sivani and Dipti BS, “Techniques of Text Detection and Recognition: A Survey,” International Journal of Emerging Researach in Management & Technology., Vol. 6, no. 6, pp. 83-87, 2017.
- T. L. Dimond, “Devices for reading handwritten characters,” in IRE-ACM-AIEE ’57 (Eastern) Papers and discussions – Proceeding, 1957. pp. 232–237.
- Shatnawi M, “Off-line Handwritten Arabic Character Recognition : A Survey,” in Int’l Conf IP, Comp Vision, Pattern Recognit | IPCV’15 | - Proceeding, 2015. pp. 52–58.
- Anderson RH. “Syntax-directed recognition of hand-printed two-dimensional mathematics,” in Association for Computing Machinery Inc Symposium – Proceeding, 1967. pp. 436–459.
- Athoillah M, Irawan, M. I, and Imah, Elly M., “Study Comparison of SVM-, K-NN- and Backpropagation-Based Classifier for Image Retrieval,” Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)., vol. 8, no. 1, pp. 11–19, 2015.
- B. Scholkopf and A. J. Smola, Learning with Kernels. MIT Press, 2001.
- Rakotomamonjy, A ,Francis R. B, Canu, S., and Yves, G., “SimpleMKL,” Journal of Machine Learning Research.., vol. 9, pp. 2491–2521, 2008.
- Athoillah M, Irawan MI, and Imah EM., “Support vector machine with multiple kernel learning for image retrieval,” in 2015 International Conference on Information & Communication Technology and Systems (ICTS) – Proceeding, 2015. pp. 17–22.
- C. Campbell and Y. Ying, Learning with Support Vector Machines. Morgan & Claypool Publishers, 2011.
- Kumar AR, and Saravanan D, “Content Based Image Retrieval Using Color Histogram,” International Journal of Computer Science and Information Technologies., vol. 4, no. 2, pp. 242–245, 2013.
- S. Abe. Intoduction in Support Vector Machines for Pattern Classification. Springer, London, 2010 .
References
Q, ye and Doerman D, “Tect Detection and Recognition in Imagery : A Survey”. IEEE Transactions on Pattern Analysisi and Machine Intelligence., vol. 37, no. 7, pp. 1480–1500, 2015.
Sivani and Dipti BS, “Techniques of Text Detection and Recognition: A Survey,” International Journal of Emerging Researach in Management & Technology., Vol. 6, no. 6, pp. 83-87, 2017.
T. L. Dimond, “Devices for reading handwritten characters,” in IRE-ACM-AIEE ’57 (Eastern) Papers and discussions – Proceeding, 1957. pp. 232–237.
Shatnawi M, “Off-line Handwritten Arabic Character Recognition : A Survey,” in Int’l Conf IP, Comp Vision, Pattern Recognit | IPCV’15 | - Proceeding, 2015. pp. 52–58.
Anderson RH. “Syntax-directed recognition of hand-printed two-dimensional mathematics,” in Association for Computing Machinery Inc Symposium – Proceeding, 1967. pp. 436–459.
Athoillah M, Irawan, M. I, and Imah, Elly M., “Study Comparison of SVM-, K-NN- and Backpropagation-Based Classifier for Image Retrieval,” Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)., vol. 8, no. 1, pp. 11–19, 2015.
B. Scholkopf and A. J. Smola, Learning with Kernels. MIT Press, 2001.
Rakotomamonjy, A ,Francis R. B, Canu, S., and Yves, G., “SimpleMKL,” Journal of Machine Learning Research.., vol. 9, pp. 2491–2521, 2008.
Athoillah M, Irawan MI, and Imah EM., “Support vector machine with multiple kernel learning for image retrieval,” in 2015 International Conference on Information & Communication Technology and Systems (ICTS) – Proceeding, 2015. pp. 17–22.
C. Campbell and Y. Ying, Learning with Support Vector Machines. Morgan & Claypool Publishers, 2011.
Kumar AR, and Saravanan D, “Content Based Image Retrieval Using Color Histogram,” International Journal of Computer Science and Information Technologies., vol. 4, no. 2, pp. 242–245, 2013.
S. Abe. Intoduction in Support Vector Machines for Pattern Classification. Springer, London, 2010 .