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Associative memories for handwritten pattern recognition

We describe the construction of a complete system of handwritten classification on the basis of bidirectional associative memories (BAM). The BAM is synthesized with a two-layer neural network in which the neuron thresholds are adjusted at the moment of learning, and the ideal prototypes for each class are selected using an automatic method. The system is completed with a topological preprocessor that eliminates distortion and noise components. It was tested on the popular NIST#19 database, attaining 100% success rates for all the characters, even under conditions of high levels of contamination due to noise or distortion of the input image.