Rosenblatt perceptrons for handwritten digit recognition

Abstract

The Rosenblatt perceptron was used for handwritten digit recognition. For testing its performance the MNIST database was used. 60,000 samples of handwritten digits were used for perceptron training, and 10,000 samples for testing. A recognition rate of 99.2% was obtained. The critical parameter of Rosenblatt perceptrons is the number of neurons N in the associative neuron layer. We changed the parameter N from 1,000 to 512,000. We investigated the influence of this parameter on the performance of the Rosenblatt perceptron. Increasing N from 1,000 to 512,000 involves decreasing of test errors from 5 to 8 times. It was shown that a large scale Rosenblatt perceptron is comparable with the best classifiers checked on MNIST database (98.9%-99.3%).