SIMPLIFIED HEURISTIC VERSION OF A RECURSIVE BAYES ALGORITHM FOR USING CONTEXT IN TEXT RECOGNITION.

R. Shinghal, D. Rosenberg, Godfried Toussaint

Research output: Contribution to journalArticle

Abstract

Word position independent and work position dependent n-gram probabilities were estimated from a large English language corpus. A text-recognition problem was simulated, and using the estimated n-grain probabilities, four experiments were conducted by the following methods of classification: the context-free Bayes algorithm, the recursive Bayes algorithm exhibited by Raviv, the modified Viterbi algorithm, and a heuristic approximation to the recursive Bayes algorithm. Based on the estimates of the probabilities of misclassification observed in the four experiments, the above methods are compared. The heuristic approximation of the recursive Bayes algorithm reduced computation without degradation in performance.

Original languageEnglish (US)
Pages (from-to)412-414
Number of pages3
JournalIEEE Transactions on Systems, Man and Cybernetics
VolumeSMC-8
Issue number5
StatePublished - Jan 1 1978

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Viterbi algorithm
Experiments
Degradation

ASJC Scopus subject areas

  • Engineering(all)

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SIMPLIFIED HEURISTIC VERSION OF A RECURSIVE BAYES ALGORITHM FOR USING CONTEXT IN TEXT RECOGNITION. / Shinghal, R.; Rosenberg, D.; Toussaint, Godfried.

In: IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-8, No. 5, 01.01.1978, p. 412-414.

Research output: Contribution to journalArticle

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