1. A neural network architecture for optical character recognition of an image to be recognized, the neural network architecture comprising: means for providing an array of input signals, respective ones of the input signals corresponding with respective image elements of the image to be recognized, the input signals having respective values which are related to the respective image elements of the image to be recognized, the array of input signals including a set of input signals whose corresponding image elements of the image are at various positions rotationally disposed about the image; a neural network layer including first through k-th groups of weighted inputs, each group including first through n-th weighted inputs, the weighted inputs of each group having first through n-th weight values, such that the first input of each of the groups has the first weight factor, the second input of each of the groups has the second weight factor, and so forth for all n of the inputs of each of the groups; wherein the inputs of the first through k-th groups are coupled to receive input signals of the set of input signals, such that each given one of the input signals of the set is coupled to inputs of a plurality of the first through k-th groups, and, for each of the plurality of the groups, different ones of the first through n-th weighted inputs are coupled to receive the given input signal; the means for providing including a plurality of respective input units having inputs coupled to receive image element information of respective ones of the image elements, and having outputs coupled to the inputs of the first through k-th groups of inputs; means for switchably coupling the inputs of the neural network layer to the outputs of the input units, to receive respective ones of the signals related to the respective image elements of the image to be recognized in accordance with the positions at which the respective image elements are distributed, thereby providing high level rotation of the signals to the at least one inputs; and whereby, an output of the neural network architecture, indicative of recognition of the image, is made based on an output of the neural network layer, the output of the neural network layer being a constant for a plurality of rotational orientations of the input image.