Modified Kohonen learning network and application in Chinese character recognition

Normal multilayer neural network is rarely used to solve pattern match problem of large scale without grouping classes and creating subnetworks. In this paper, a modified single-layer Kohonen learning network structure based on generalized learning vector quantization (GLVQ) theory is proposed. By cascading two of the proposed learning networks in handwritten Chinese character recognition, training, preclassification and final recognition processes are easily integrated. Experiments conducted with off-line handwritten samples show the efficiency of the network.