Three Dimensional Rotation-Free Recognition of Characters

In this paper, we propose a new method for three dimensional rotation-free recognition of characters in scene. In the proposed method, we employ the Modified Quadratic Discriminant Function (MQDF) classifier trained with samples generated by three-dimensional rotation process in a computer. We assume that when recognizing individual characters, considering three-dimensional rotation can approximately handle the recognition of perspectively distorted characters. The results of the evaluation experiments using printed alphanumeric characters as an evaluation data set, consisting of approximately 600 samples/class for 62 character classes, show that the recognition rate is 99.34% for rotated characters while it is 99.59% for non rotated characters. We have empirically confirmed that the rotated characters given as the training data set do not negatively affect significantly to recognition of non rotated characters. Moreover, 437 characters extracted from 50 camera-captured scenes were correctly recognized and the feasibility of real world application of our method has been confirmed. Finally we describe on three dimensional rotation angle estimation of characters for detecting local normal of the surface on which the characters are printed aiming to scene analysis by shape from characters.