Automated recongition of characters using optical filtering with positive and negative functions encoding pattern and relevance information

摘要

A method and apparatus is described for recognition of hand printed characters using pairs of positive and negative correlative functions (PNCFs), the PNCFs including both pattern and relevance information, implemented by optical elements. A set of optical elements having varying optical density corresponding to a set of two-dimensional PNCFs 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 PNCFs. The amount of light transmitted through each of the elements is measured, providing a transmission coefficient. The transmission coefficients are the 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 PNCFs. 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.