On the Recognition of Information With a Digital Computer
Journal of the ACM (JACM)
Can A Priori Probabilities Help in Character Recognition?
Journal of the ACM (JACM)
Spelling correction in systems programs
Communications of the ACM
String similarity and misspellings
Communications of the ACM
A technique for computer detection and correction of spelling errors
Communications of the ACM
Retrieval of misspelled names in an airlines passenger record system
Communications of the ACM
The use of context for correcting garbled English text
ACM '64 Proceedings of the 1964 19th ACM national conference
Contextual Word Recognition Using Binary Digrams
IEEE Transactions on Computers
Error Bounds for a Contextual Recognition Procedure
IEEE Transactions on Computers
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
A Multifont Word Recognition System for Postal Address Reading
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
Online spelling correction for query completion
Proceedings of the 20th international conference on World wide web
Spelling correction using probabilistic methods
Pattern Recognition Letters
| Hi-index | 14.99 |
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.