A handprinted character recognition system using image transformation based on partial inclination detection

In recognition of handprinted characters, it is important to dissolve distortions of character caused by writer's habits. In order to dissolve distortions and to obtain better features, many image conversion methods have been proposed. But there are distortions that cannot be dissolved by these methods. One example is the case of parallel strokes which are spread out in fan shape. In this paper, in order to dissolve distortions, we propose a new image conversion method, Transformation based on Partial Inclination Detection (TPID), which is employed just before normalization, and is intended to dissolve several kinds of distortions in images of each character. TPID constructs transformation functions from inclination angles which are detected in some subspaces of the character's image, and converts images using the transformation functions. TPID is especially suitable for correcting the inclinations of horizontal and vertical strokes of a character. This has a powerful impact on the quality of the characteristic features. In recognition experiments using ETL9B, the largest database of handprinted characters in Japan, we have obtained a recognition rate of 99. 08%, which is the best to our knowledge.