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Automated recognition of characters using optical filtering with maximum uncertainty - minimum variance (MUMV) functions

Image Number 3 for United States Patent #5371808.

A method and apparatus is described for recognition of hand printed characters using maximum uncertainty--minimum variance (MUMV) functions, such as Gabor functions, implemented by optical elements. A set of optical elements having varying optical density corresponding to a set of two-dimensional MUMV functions is generated. A pattern of illumination responsive to the image of the character to be identified is simultaneously transmitted through each of the optical elements implementing the MUMV functions. The amount of light transmitted through each of the elements is measured, providing a transmission coefficient. Such transmission coefficients are used as a set of inputs to a neural network, such that the inputs to the neural network are a set of transmission coefficients resulting from transmission of light corresponding to a character to be identified through a complete set of optical elements implementing a set of two-dimensional MUMV functions. The neural network calculates weighted sums of the transmission coefficients. The neural network may be implemented as a network of resistors connected between input nodes, intermediate nodes, and output nodes. The output node having the highest voltage identifies the character to be identified.

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