ACM Computing Surveys (CSUR)
Computer programs for detecting and correcting spelling errors
Communications of the ACM
Contextual Word Recognition Using Binary Digrams
IEEE Transactions on Computers
A Contextual Postprocessing System for Error Correction Using Binary n-Grams
IEEE Transactions on Computers
Stochastic Error-Correcting Syntax Analysis for Recognition of Noisy Patterns
IEEE Transactions on Computers
A Method for the Correction of Garbled Words Based on the Levenshtein Metric
IEEE Transactions on Computers
Syntactic Decision Rules for Recognition of Spoken Words and Phrases Using a Stochastic Automaton
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in Text Recognition with the Modified Viterbi Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Viterbi algorithm as an aid in text recognition (Corresp.)
IEEE Transactions on Information Theory
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A probabilistic procedure is suggested for the automatic correction of spelling and typing errors in printed English texts. The heart of the procedure is a probabilistic model for the generation of the garbled word from the correct word. The garbler can delete or insert symbols in the word or substitute one or more symbols by other symbols. An expression is derived for P(Y @? X), the probability of generating a garbled word Y from a correct word X. The model is probabilistically consistent. Using the expression for P(Y @? X), we can derive an estimate of the correct word from the garbled word Y so as to minimize the average probability of error in the decision. One of the important features of the expression P(Y @? X) is that it can be computed recursively. Experiments conducted using the dictionary of 1025 most common English words indicate that the accuracy of correction by this scheme is substantially greater than that which can be obtained by other algorithms especially while dealing with garbled words derived from relatively short words of length less than 6.