Characters Recognition Method Based on Vector Field and Simple Linear Regression Model
| Paper File | Download Paper File | | Appear In | ECTI Transaction CIT (ECTI Transaction CIT) | | Publication Date | 01/11/2005 - 30/11/2005 | | Volume | 1 | | Pages | 118 - 125 | | No | 2 | | Author 1 | Tetsuya Izumi | | Author 2 | Tetsuo Hattori | | Author 3 | Hiroyuki Kitajima | | Author 4 | 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. |
