Characters recognition using vector field and linear regression model

In order to obtain a low computational cost method (or rough classification) for automatic handwritten character recognition, this paper proposes a combined system of two feature representation methods based on a vector field: one is autocorrelation matrix, and another is a low frequency Fourier expansion. In each method, the similarity is defined as a weighted sum of the squared values of the inner product between input pattern feature vector and the reference pattern ones that are normalized eigenvectors of KL (Karhunen-Loeve) expansion. This paper also describes a way of deciding the weight coefficients using a simple linear regression model, and shows the effectiveness of the proposed method by illustrating some experimentation results for 3036 categories of handwritten Japanese characters.