Invariant digit recognition by Zernike moments and third-order neural networks

The classification of hand-written digits with invariance under translations, rotations and scaling using neural networks is discussed. Two approaches are considered. First, Zernike moment expansions are used to produce invariant representations of the image. Secondly, the image is coded using triplets of pixels grouped into similarity classes of triangles. Both types of coding form the input into a multi-layered perceptron classifier. Methods of reducing the dimensionality of the ensuing image representations are discussed, and the performances of both coding methods are assessed and compared. Third-order networks result in a generalisation success rate of 79% under all transformations combined.<>