Abstract: Fixed-pitch, fixed-font characters embedded in a noisy gray-scale image of picture elements (pels) within a complex background can be extracted prior to execution of any recognition operations by first deriving a normalized Boolean-coded image from the gray-scale image. Then, a subset of at least three uncontaminated character triples is formed by filtering the Boolean-coded image. Next, an affine transform is approximated from locations in the Boolean-coded image of at least three noncollinear ones of the uncontaminated character triples. Lastly, the locations in a logical matrix array of all possible character triples are estimated according to the affine transform.

Abstract: A multi-stage multi-network character recognition system decomposes the estimation of a posteriori probabilities into coarse-to-fine stages. Classification is then based on the estimated a posteriori probabilities. This classification process is especially suitable for the tasks that involve a large number of categories. The multi-network system is implemented in two stages: a soft pre-classifier and a bank of multiple specialized networks. The pre-classifier performs coarse evaluation of the input character, developing different probabilities that the input character falls into different predefined character groups. The bank of specialized networks, each corresponding to a single group of characters, performs fine evaluation of the input character, where each develops different probabilities that the input character represents each character in that specialized network's respective predefined character group.