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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.