Axially symmetric neural network architecture for rotation-invariant pattern recognition

We propose an axially symmetric neural network architecture capable of detecting orientations for rotation-invariant pattern recognition. This is a class of multilayer perceptrons, where the synaptic weights “in parallel” between the lower layer and the successive upper layer all have the same values (i.e., symmetric with respect to the principal axis of the network architecture). This network can be trained by the backpropagation procedure under the weight constraints using any patterns (e.g. 10-digit or 26 alphabet characters) in a standard position, which automatically makes it possible to recognize the test patterns with different orientations (i.e., rotated patterns), simultaneously detect the orientation