Discrimination of similar characters with a nonlinear compound discriminant function

In studies of similar character recognition, the quadratic compound discriminant function has been proposed, in which a quadratic nonlinear transformation is applied to the compound discriminant function. It has been shown that the method can achieve high discrimination power. It may be true that a better decision boundary is estimated by raising the order of the nonlinear transformation, but the realization of third- or higher-order nonlinear compound discriminant functions is difficult due to the explosive increase of the amount of computation. On the other hand, the nonlinear transformation using kernel functions has recently been attracting attention. The high-order nonlinear transformation can be realized by this approach with a small amount of computation. It is expected that the above problem will be solved by performing a nonlinear transformation of the compound discriminant function by this method. This paper shows that the nonlinear transformation of the compound discriminant function can be formulated by using a kernel function, and proposes the nonlinear compound discriminant function. A recognition experiment shows that greater discrimination power than the quadratic compound discriminant function can be acquired by the proposed method with almost the same amount of computation as that using the compound discriminant function.