Large scale pattern recognition system using hierarchical neural network and false-alarming nodes

A hierarchical neural network was studied for large scale pattern recognition problems such as multilingual character recognition. The hierarchical neural network is made up of preclassification and fine recognition modes. The authors constructed clusters for the preclassification mode using self-organizing feature map (SOFM) learning and performed modifying steps for reducing the number of clusters. The clusters contain patterns that have similar characteristics. They adopted multilayer perceptron (MLP) networks to the corresponding clusters for the fine recognition mode. They also proposed the use of false-alarming nodes in the output layer of the MLP network, which could be constructed on error-prone negative examples quite similar to the patterns of the selected cluster but actually belonging to different nearby clusters through SOFM´s topology-preserving mapping. The proposed system could be successfully adopted for recognizing the large number of printed Korean/Chinese character databases as well as the IRIS database