Classification of Handwritten Characters by their Symmetry Features

We propose a technique to classify characters by two different forms of their symmetry features. The generalized symmetry transform is applied to digits from the USPS data set. These features are then used to train probabilistic neural networks and their performances are compared to the traditional method.