Transform-Invariance in Local Averaging Classifier for Handwritten Digit Pattern Recognition

Abstract

In this paper, a classification method designed by combining a local averaging classifier and a tangent distance is proposed for handwritten digit pattern recognition. In practice, first the k-nearest neighbors of an input sample are selected in each class by using a two-sided tangent distance. Next, the mean vectors of the selected transformed-neighbor samples are computed in individual classes. Finally, the input sample is classified to the class that minimizes the one sided tangent distance between the input sample and the mean one. The superior performance of the proposed method is verified with the experiments on benchmark datasets MNIST and USPS.