Abstract. Optical Character Recognition Systems (OCR) provide humanmachine 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.
The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives during the development of an optical character recognition system. Primitives are defined by experience or by intuition. Several primitives can be extracted while some are irrelevant or redundant. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition and has low computational overhead. We use feature selection techniques to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLPNNs).
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