K-NN Classification of Handwritten Characters Using a New Distortion-Tolerant Matching Measure

Abstract

The global affine transformation (GAT) correlation method proposed by Wakahara et al. is a pattern matching method that can compensate for affine transformation embedded in an input pattern. The GAT correlation method demonstrated a high performance in character recognition and object matching. For example, it outperformed the well-known tangent distance (TD) method in character recognition experiments made on IPTP handwritten numeral database. The purpose of this paper is threefold. First, we introduce a new matching measure called the nearest neighbor distance of equi-gradient direction (NNDEGD) in cooperation with the GAT correlation method. The NNDEGD is just the window parameter of the Gaussian function used in the GAT correlation method, which is equal to the average minimum distance between a point in one image and another point in the other image with the same gradient direction. We propose to use this value as a new matching measure. Secondly, we extend the GAT correlation method so as to handle the change of stroke width besides the affine transformation. Finally, we apply the original and extended versions of the GAT correlation method to fc-NN classification experiments using the MNIST database. These experiments are carried out efficiently for the first time because we have substantially reduced the computational complexity and memory load involved in the original GAT correlation method. We successfully show that the enhanced GAT correlation method has achieved the lowest error rate of 0.49% compared with competing distortion-tolerant template matching techniques.