TY - JOUR

T1 - SIMPLIFIED HEURISTIC VERSION OF A RECURSIVE BAYES ALGORITHM FOR USING CONTEXT IN TEXT RECOGNITION.

AU - Shinghal, R.

AU - Rosenberg, D.

AU - Toussaint, G. T.

PY - 1978

Y1 - 1978

N2 - 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.

AB - 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.

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U2 - 10.1109/tsmc.1978.4309983

DO - 10.1109/tsmc.1978.4309983

M3 - Article

AN - SCOPUS:0017969061

VL - SMC-8

SP - 412

EP - 414

JO - IEEE Transactions on Systems, Man and Cybernetics

JF - IEEE Transactions on Systems, Man and Cybernetics

SN - 0018-9472

IS - 5

ER -