Apparatus, and an accompanying method, for use in, e.g., a neural network-based optical character recognition (OCR) system (5) for accurately classifying each individual character extracted from a string of characters, and specifically for generating a highly reliable confidence measure that would be used in deciding whether to accept or reject each classified character. Specifically, a confidence measure, associated with each output of, e.g., a neural classifier (165), is generated through use of all the neural activation output values. Each individual neural activation output provides information for a corresponding atomic hypothesis of an evidence function. This hypothesis is that a pattern belongs to a particular class. Each neural output is transformed (1650) through a pre-defined monotonic function into a degree of support in its associated evidence function. These degrees of support are then combined (1680, 1690) through an orthogonal sum to yield a single confidence measure associated with the specific classification then being produced by the neural classifier. <IMAGE>
An evidential confidence measure and rejection technique for use in a neural network based optical character recognition system