Characters Recognition Method Based on Vector Field and Simple Linear Regression Model

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Tetsuya Izumi
Tetsuo Hattori
Hiroyuki Kitajima
Toshinori Yamasaki

Abstract

In order to obtain a low computational cost method (or rough classification) for automatic handwritten characters 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.

Article Details

How to Cite
[1]
T. Izumi, T. Hattori, H. Kitajima, and T. Yamasaki, “Characters Recognition Method Based on Vector Field and Simple Linear Regression Model”, ECTI-CIT Transactions, vol. 1, no. 2, pp. 118–125, Mar. 2016.
Section
Artificial Intelligence and Machine Learning (AI)