Handwritten Digit Recognition Method Based on Combination Features
基于组合特征的手写体数字识别方法

A new method is proposed for handwritten digit recognition.Firstly,we extract global features using Kernel Principal Component Analysis(KPCA) technique and extract local features using Independent Component Analysis(ICA)technique.We select some of the local features and the global features and combine them.Then we perform classification using the combination features.For validation of the method,we tested our method on the USPS database by using linear Support Vector Machine.Meanwhile,we compared performance of our method with that of PCA-based,KPCA-based and ICA-based methods.The experiment results indicate the performance of our method is superior to those of other methods.