Handwritten numeral recognition using autoassociative neural networks

This paper describes a result of fundamental study an pattern recognition using autoassociative neural networks, and experimental comparison on handwritten numeral recognition by conventional multi-layered neural network and statistical classification techniques. As the statistical classification techniques, the projection distance method and the nearest neighbor method are employed. The relationship between the projection distance method which is based on the K-L expansion and three layer-ed autoassociative networks is discussed and it is shown that the three and five layered autoassociative networks are superior to the projection distance method. In the handwritten numeral recognition experiment, a total of 44,862 numeral samples collected by IPTP are used to evaluate and compare the recognition rates of the autoassociative networks, the mutual associative network, the nearest neighbor method, and the projection distance method The five layered autoassociative networks achieved the highest recognition rate in the handwritten numeral recognition experiment. The result of experiment together with the fundamental study show that the autoassociative net works have such characteristics that: (1) class independent training makes the possibility of local convergence less than that of the mutual associative network, (2) the networks possess the higher ability of dimension reduction and interpolation than the nearest neighbor method, (3) they yield less misclassification due to subspace sharing than the projection method, (4) the five layered autoassociative network can fit a curved hypersurface to a distribution of patterns.