Kernel based principal component for recognizing handwritten numbers

We implement kernel-based principal component analysis to recognize handwritten numbers. Then we analyze the relationship of each numeral type's eigenvalue and eigenvector with its recognition rate. We also present a modified algorithm to improve the robustness as well as the efficiency of the recognition method by employing the secondary training and detection methods from the perspective of nature of kernel function. This method can solve the problem of low recognition rate of a small number of scribbled characters at both low time cost and space complexity. Experiments using 1000 to 5000 test samples all show that our method can achieve 97.8% to 99.0% recognition accuracy.