Affine-transformation and 2D-projection invariant k-NN classification of handwritten characters via a new matching measure
We enhance k-NN classification by affine and 2D-projection invariant matchings. We develop acceleration methods for those distortion-tolerant matching techniques. We propose a matching measure using similarity in direction and curvature of edges. Experiments using the MNIST database show a very low error rate of 0.30%. The source code used in the above-mentioned experiments is uploaded.
matching affine transformation two-dimensional projection transformation k-nearest neighbors nearest-neighbor distance of equi-gradient direction