k-NN Classification of Handwritten Characters via Accelerated GAT Correlation

This paper addresses the problem of reinforcing the ability of k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We make a comparison of three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the GAT (Global Affine Transformation) correlation. The k-NN classification method is straightforward and powerful, however, is very time-consuming. Hence, to reduce the computational cost of matching in k-NN classification we propose to accelerate the GAT correlation technique by reformulating its computational model and adopting efficient lookup tables. Recognition experiments made on the handwritten numerical database IPTP CDROM1B show that matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieve recognition rates of 97.07%, 97.50%, and 98.70%, respectively. Also, the computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.