Authors:
Tetsuya Izumi
;
Tetsuo Hattori
;
Hiroyuki Kitajima
and
Toshinori Yamasaki
Affiliation:
Graduate School of Engineering, Kagawa University, Japan
Keyword(s):
Handwritten Characters Recognition, Feature Extraction, Vector Field and Fourier Transform
Related
Ontology
Subjects/Areas/Topics:
Control and Supervision Systems
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
In order to obtain a low computational cost method for automatic handwritten characters recognition, this paper proposes a combined system of two rough classification methods based on features of a vector field: one is autocorrelation matrix method, and another is a low frequency Fourier expansion method. In each method, the representation is expressed as vectors, and the similarity is defined as a weighted sum of the squared values of the inner product between input pattern and the reference patterns that are normalized eigenvectors of KL (
Karhunen-Loeve) expansion. This paper also describes a way of deciding the weight coefficients based on linear regression, and shows the effectiveness of the proposed method by illustrating some experimentation results for 3036 categories of handwritten Japanese characters.