Characters recognition method based on vector field and simple linear regression model
T. Izumi, Tetsuo Hattori, H. Kilajima, Toshinori Yamasaki · 2005
In order to obtain a low computational cost method (or rough classification) for automatic handwritten character recognition, the paper proposes a combined system of two feature representation methods based on a vector field: an autocorrelation matrix; 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 the input pattern feature vectors and the reference pattern ones that are normalized eigenvectors of a KL (Karhunen-Loeve) expansion. The 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 experimental results for 3036 categories of handwritten Japanese characters.