Character recognition using novel optoelectronic neural network
William Robinson, John M. Taboada, Harold G. Longbotham · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
We apply a novel optoelectronic neural network to recognize a set of characters from the alphabet. The network consists of a 15 X 1 binary input vector, two optoelectronic vector matrix multiplication layers, and a 15 X 1 binary output layer. The network utilizes a pair of custom fabricated spatial light modulators (SLMs) with 90 levels of gray scale per pixel. The SLMs realize the matrix weights. Previous networks of this type were hampered by limited levels of gray scale and the need to use two separate weight masks (matrices) per layer. We operate the weight masks in unipolar mode which allows for both positive and negative weights from the same masks. We use a hard limiting function for the network's nonlinearity. A modification of Widrow's seldom known MR2 training algorithm is used to train the network. Furthermore, the network introduces a novel lens-free crossbar matrix- vector multiplier. We also show proposed networks of higher capacity which could be implemented for image processing.