Optical heteroassociative memory for character translation
Francis T. S. Yu, Taiwei Lu, Xiang Y. Yang · Optical Society of America Annual Meeting · 1990
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.