Recognition with supplementary information how many bits are lacking for 100% recognition?

In this paper, we propose a new model in which the classifier receives not only a pattern itself but also supplementary information that assists recognition. This model enables us to achieve a 100% recognition rate with a 0% rejection rate with certain bits of supplementary information required. For printed characters, experiments show that 4 bits of supplementary information were required in the leave-one-out method and 1 bit was in the resubstitution method. In addition, we generalize the discussion into the relationship among a quantity of supplementary information, a recognition rate and a rejection rate. The theory presented in this paper is applied to the data embedding of a font set for camera-based character recognition [9].