Printed character recognition using Kernel CCA with LS-SVM method

As an improved unsupervised learning algorithm of CCA, Kernel Canonical Correlation Analysis (Kernel CCA) can extend CCA to the nonlinear case by applying the kernel trick. In this paper, an optical character recognition system based on image preprocessing technologies combined with Kernel CCA has been developed. Moreover, due to the duality between Kernel CCA and LS-SVM, the optimization problem of Kernel CCA is transformed into the solving of quadratic equations by means of LS-SVM method. The proposed method has been evaluated by carrying out recognition experiments on the optical printed characters of electronic components. The results show that the proposed method has a better recognition performance, and the computational complexity can be simplified largely by introducing LS-SVM method.