“An Optimum Character Recognition System Using Decision Functions”, 1957-12-01 (; backlinks):
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 criterion. The optimum recognition is thus obtained.
The optimum system consists of a conditional-probability densities computer; character channels, one for each character; a rejection channel; and a comparison network. Its precise structure 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 deterioration are presented.