Combination of the assembly neural network with a perceptron for recognition of handwritten digits arranged in numeral strings

The purpose of the paper is to design and test neural network structures and mechanisms for making use of the information that is contained in the character strings for more correct recognition of the characters constituting these strings. Two neural networks are considered in the paper; both networks are combined into a joint recognition system. The first is the assembly neural network and the second is the neural network of a perceptron type. A computer simulation of the system is performed. The combined system solves the task of recognition of handwritten digits of the MNIST test set provided that the digits have been arranged in the numeral strings memorized in the system. During a recognition process of an input numeral string, the assembly neural network executes intermediate recognition of the digits basing on which a perceptron type network accomplishes the final choice among the limited combinations of strings memorized in the network. The experiments have demonstrated that the combined system is able to make use of the information that is contained in the strings for more correct recognition of digits of the MNIST test set. In particular, the experiments have shown that the combined system commits no errors in the recognition of MNIST test set on the condition that the digits of this set had been organized in the strings of more than 5 digits each. 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.