Handwritten Digit Recognition Based on Support Vector Machine

In this paper, we propose a handwritten digit recognition method based on Support Vector Machined (SVM). Firstly, some main features are extracted from the handwritten digital images (Euler Number, roundness, moment feature, crossing density, pixel density). Secondly, adopt the method of SVM for classification. This paper adds the feature values normalized, using radial basis function and Cross-validation important parameters. Our approach has been implemented with MNIST database and we have achieved an average recognition rate of 96.3%, the lowest single digit recognition rate of 93.5% when the training data are 100x10.