Integrating diverse knowledge sources in text recognition

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.