LIRA neural classifier for handwritten digit recognition and visual controlled microassembly

Abstract

In this paper, limited receptive area neural classifiers are described which are based upon Rosenblatt's perceptron. These networks can be used for both binary and gray-level images. A method is reviewed for greatly expanding the amount of available training data. A training algorithm, based upon that of Rosenblatt, is given. The networks are applied to the handwritten numeral recognition problem, and to task in microassembly image recognition.