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Abstract

A bill-recognition apparatus includes a neural network having a learning capability and performs high-efficiency pattern recognition of seven kinds of U.S. dollar bills. Pattern image data optically inputted through a sensor is compressed using plurality of column masks, and then a plurality of values representative of images (slab values) are determined. The image data is divided into a large number of strip-shaped segments, and some of theses segments are masked with column areas of masks. The values representative of images compressed through column masks are not influenced by a slight inclination of the pattern image during the reading operation. These values representative of images are inputted to a separation processing unit (neural network). From these values, the separation processing unit calculates separation values corresponding to respective decision patterns associated with pattern images, using weights which have been adjusted to optimum values for respective decision patterns. A correct pattern image is determined from the maximum value of the separation values. The above arrangement allows for a reduction in scale of the neural network and control system. Furthermore, bill recognition may also be achieved by separation processing using a plurality of small-scaled neural networks connected in cascade, or replacing weight functions in the same neural network and performing separation processing a plurality of times for the same slab values (cascade processing). In this way, it is possible to reduce the scale of the neural network and the control system.

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