A novel metric for nearest-neighbor classification of hand-written digits

Classifiers based on the k-nearest neighbors (k-NN) approach have recently received an increasing attention because of their simple implementation and absence of training. In this technique, the similarity measure used to compute the distance between the stored patterns and the test element is the most crucial part of the method. The paper addresses this issue within the context of recognition of hand-written digits. A novel similarity measure is proposed and used to associate a number to each pair of samples in a suitable N-dimensional space in order to define the distance between two handwritten characters. The proposed similarity measure has been parameterized and the best values of these parameters have been evaluated using suitable statistical approaches. Finally, some results obtained from the classification of digits extracted from a ZIP code database are provided