Feature fusion method based on canonical correlation analysis and handwritten character recognition

A new feature extraction method, based on feature fusion, according to the idea of canonical correlation analysis (CCA), is proposed in this paper. A framework of CCA used in pattern recognition is described. The overall process comprises: extracting two groups of feature vectors with the same pattern; establishing the correlation criterion function between the two groups of feature vectors, and extract their canonical correlation features in order to form effective discriminant vectors for recognition. The inherent essence of this method used in recognition is theoretically analyzed. This method uses correlation features between two groups of feature vectors as effective discriminant information, so it not only is suitable for information fusion, but also eliminates redundant information within features, a new way for classification is proposed. Experimental results of our method applying on Concordia University CENPARMI handwritten numeral database has shown that our recognition rate is higher than that of the algorithm adopting single feature or the existing fusion algorithm.