WRITER ADAPTIVE SEGMENTATION OF HANDWRITTEN JAPANESE CHARACTERS

In this paper, we propose a character segmentation method which obtains shape feature vectors from segmentation candidates, evaluates the candidates by linearly transforming of their feature vectors, and searches for the segmentation path which has the best sum of evaluated values. The advantage of the proposed method is that the parameters for linear transformation can be optimized by a steepest gradient method to obtain the best segmentation rate for training samples. Since optimization can be carried out in terms of on-line learning, the segmentation results can be gradually adapted to the writer. Experiments prove the efficiency of the proposed method.