Abstract
The capabilities of present commercial machines for producing correct text by recognizing words in print, handwriting and speech are very limited. For example, most optical character recognition [OCR] machines are limited to a few fonts of machine print, or text that is handprinted under certain constraints; any deviation from these constraints will produce highly garbled text. This paper describes an algorithm for text recognition/correction that effectively merges a bottom-up refinement process that is based on the utilization of transitional probabilities and letter confusion probabilities, known as the Viterbi algorithm [VA], together with a top-down process based on searching a trie structure representation of a lexicon. The algorithm is applicable to text containing an arbitrary number of character substitution errors such as that produced by OCR machines.
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