Handwritten Character Recognition Based on the Nonlinear PCA Neural Network

Principal component analysis(PCA)has been applied widely in pattern recognition.Based on the nonlinear PCA algorithm and subspace pattern recognition method,a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper.The method has been used in handwritten digits and characters recognition,and a comparison with BP neural network based classifiers has been made.Some satis- factory results have been obtained.The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits,and 91.03% for the handwritten characters.