Multi-resolution character recognition by adaptive classification

Abstract

The quality of character image plays an important role for the performance of character recognition system. However there is no good way to measure the recognition difficulty of a given character image. For the given character image with unknown quality, it is improper that apply the single character database to recognize it by the same feature and the same classifier. This paper proposed a novel approach for multi-resolution character recognition whose feature is extracted directly from gray-scale image and classification is adaptive classification which adaptively selects the appropriate character database and classifiers by evaluating the image quality of the input character. A resolution evaluation algorithm based on gray distribution feature was proposed to decide the adaptive classification weights for the classifiers, which make the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of character recognition system. © Springer-Verlag Berlin Heidelberg 2007.