Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine
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Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine

Muhammad Athoillah, Rani Kurnia Putri


 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%


Arabic, Classification, Handwritten, Machine Learning, Pattern Recognition

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