Can A Priori Probabilities Help in Character Recognition?
Journal of the ACM (JACM)
The use of context for correcting garbled English text
ACM '64 Proceedings of the 1964 19th ACM national conference
An investigation of the use of context in character recognition using graph searching
An investigation of the use of context in character recognition using graph searching
Programming Languages, Information Structures, and Machine Organization.
Programming Languages, Information Structures, and Machine Organization.
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
Techniques for replacing characters that are garbled on input
AFIPS '66 (Spring) Proceedings of the April 26-28, 1966, Spring joint computer conference
Experiments in the recognition of hand-printed text, part II: context analysis
AFIPS '68 (Fall, part II) Proceedings of the December 9-11, 1968, fall joint computer conference, part II
Experiments in the Contextual Recognition of Cursive Script
IEEE Transactions on Computers
IEEE Transactions on Computers
A Contextual Postprocessing System for Error Correction Using Binary n-Grams
IEEE Transactions on Computers
IEEE Transactions on Computers
Contextual Postprocessing System for Cooperation with a Multiple-Choice Character-Recognition System
IEEE Transactions on Computers
A Method for the Correction of Garbled Words Based on the Levenshtein Metric
IEEE Transactions on Computers
Multifont OCR postprocessing system
IBM Journal of Research and Development
Spelling correction using probabilistic methods
Pattern Recognition Letters
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This paper describes a special-purpose character recognition system which uses contextual information for the recognition of words from any given dictionary of words. Previous techniques that utilized context involved letter transition probabilities of digrams and trigrams. This research introduces the concept of binary digrams which overcomes some of the problems of past approaches. They can be used to extract offectively the "syntax" of the dictionary while requiring very modest amounts of storage. A computationally feasible procedure is described which allows the accuracy requirements of the character recognizer to be relaxed if it is followed by a contextual postprocessor. The modified recognition system is allowed to output several alternatives for each character, while the postprocessor selects the proper string of characters by having access to both the dictionary and the dictionary syntax. A theoretical estimate of the recognition rate is derived, and experimental results demonstrate the ability of the system to achieve low error and rejection rates.