Memory-based character recognition using a transformation invariant metric

Memory-based classification algorithms such as radial basis functions or K-nearest neighbors often rely on simple distances (Euclidean distance, Hamming distance, etc.), which are rarely meaningful on pattern vectors. More complex better suited distance measures are often expensive and rather ad-hoc. We propose a new distance measure which: 1) can be made locally invariant to any set of transformations of the input; and 2) can be computed efficiently. We tested the method on large handwritten character databases provided by the US Post Office and NIST. Using invariances with respect to translation, rotation, scaling, skewing and line thickness, the method outperformed all other systems on a small (less than 10,000 patterns) database and was competitive on our largest (60,000 patterns) database.