Building multiple prototype classifiers for handwritten character recognition using automatic statistical clustering techniques

Automatic statistical clustering techniques have been applied to implement different multiple prototype classifiers. Multiple prototyping offers an optimised solution to cases where there is significant variability in the training data. A typical application area is the recognition of handwritten characters. Once a set of features has been extracted, different statistical clustering techniques can be implemented to achieve multi-dimensional clustering in the feature space. Building of prototypes from these clusters is straight-forward, The success of the multi-prototyping depends on the efficiency of the statistical clustering techniques. In this paper, different clustering techniques have been used in conjunction with the use of different approaches to the formation of prototypes and the relative performance enhancements are reported.