Abstract :
The effectiveness of various forms of contextual information in a postprocessing system for detection and correction of errors in words is examined. Various algorithms utilizing context are considered, from a dictionary algorithm which has available the maximum amount of information, to a set of contextual algorithms utilizing positional binary n-gram statistics. The latter information differs from the usual n-gram letter statistics in that the probabilities are position-dependent and each is quantized to 1 or 0, depending upon whether or not it is nonzero. This type of information is extremely compact and the computation for error correction is orders of magnitude less than that required by the dictionary algorithm.
Keywords :
Character recognition, context, contextual post-processor, dictionary, error correction, error detection, pattern classification, pattern recognition, positional binary n-grams.; Character recognition; Dictionaries; Error analysis; Error correction; Humans; Information science; Natural languages; Pattern classification; Pattern recognition; Statistics; Character recognition, context, contextual post-processor, dictionary, error correction, error detection, pattern classification, pattern recognition, positional binary n-grams.;
