An optimum character recognition system using decision functions

The character recognition problem, usually resulting from characters being corrupted by printing deterioration and/or inherent noise of the devices, is considered from the viewpoint of statistical decision theory. The optimization consists of minimizing the expected risk for a weight function which is preassigned to measure the consequences of system decisions As an alternative minimization of the error rate for a given rejection rate is used as the critenon. The optimum recogition is thus obtained. The optimum system consists of a conditional-probability densisities computer; character channels, one for each character; a rejection channel; and a comparison network. Its precise structure and and ultimate performance depend essentially upon the signals and noise structure. Explicit examples for an additive Gaussian noise and a ``cosine´´ noise are presented. Finally, an error-free recognition system and a possible criterion to measure the character style and deteriortation are presented.