Invariant pattern recognition using third-order networks and Zernike moments

The classification of two-dimensional binary images by artificial neural networks, irrespective of their position, orientation, and size, is investigated using two complementary methods. Third-order networks were used first. Invariance under all three transformations was achieved by grouping triplets of pixels into appropriate equivalent classes. A suitable reduction in the number of weights then resulted in economical networks which exhibit high recognition rates under all transformations simultaneously, together with robustness against local distortions. The performance of these networks was tested on invariant classification of both typed and handwritten numerals. It was found to be superior to that achieved by the more traditional method of preprocessing that image using moments. Zernike moments were used because they are known to be well suited for pattern classification. In both cases, the network was trained by back-error propagation