Multi-channel recognition discriminator

Abstract

A multi-channel multi-genre character recognition discriminator is disclosed which performs the decision making process between strings of characters coming from a multi-channel (i.e., three or more channels) alpha-numeric output optical character reader (OCR) system for use in such applications as, for example, text processing and mail processing. The multi-channel output OCR uses separate recognition processes for each genre or character set indicative of a distinct group with respect to style (i.e., font) or form, and attempts to recognize each character independently as belonging to each respective genre. For example, in a three channel output OCR for reading mixed numeric, English and Russian Cyrillic character sets, the English alphabetic interpretation of a scanned word is outputted as an English alphabetic subfield on a first OCR output line, the Cyrillic interpretation of the scanned word is outputted as a Cyrillic subfield on a second OCR output line, and numeric interpretation of the scanned word is outputted as a numeric subfield on a third OCR output line. A multi-channel multi-genre character recognition discriminator analyzes these three subfield character streams by calculating a first conditional probability that given the OCR has scanned and recognized an English alphabetic character E.sub.i, the probability that numeric N.sub.K and Cyrillic C.sub.J characters were respectively misrecognized by their recognition channels; a second conditional probability that given the OCR has scanned and recogized a Cyrillic character C.sub.J the probability that numeric N.sub.K and English E.sub.i characters were respectively misrecognized by their recognition channels; and a third conditional probability that given the OCR scanned and recognized a numeric character N.sub.K, the probability that English E.sub.i and Cyrillic C.sub.J characters were respectively misrecognized by their recognition channels. These conditional probabilities are developed character by character for each character within a string thereof or a word. A first product of all the first type conditional probabilities is calculated for all of the characters in a word (which may, of course, contain only a single character); similarly second and third products are calculated for the second and third conditional probabilities, respectively. The magnitudes of the products of these conditional probabilities are then compared in an N-channel comparator, and the highest probability subfield is selected as the most probable interpretation of the word scanned by the OCR.