Abstract

Character translation can be accomplished by a heteroassociative memory in an artificial neural network. Because of the similarity among the characters, the special features of the patterns are important in pattern recognition. In this paper, a neural-network model based on the interpattem association (IPA) concept is presented.¹ Generalized logical rules are developed to construct the excitatory and inhibitory interconnections in the heteroassociative memory. An adaptive optical neural network using high-resolution liquid-crystal televisions² is used to translate between English letters and Chinese characters. Experimental and computer-simulated results have revealed that the IPA model is more effective in recognizing input among similar characters and has a larger storage capacity than the Hopfield model. Furthermore, the IPA model has shown two major advantages: fewer interconnections and fewer gray levels.

© 1990 Optical Society of America