### 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 language | English (US) |
---|---|

Pages (from-to) | 412-414 |

Number of pages | 3 |

Journal | IEEE Transactions on Systems, Man and Cybernetics |

Volume | SMC-8 |

Issue number | 5 |

State | Published - Jan 1 1978 |

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### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*IEEE Transactions on Systems, Man and Cybernetics*,

*SMC-8*(5), 412-414.

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

Research output: Contribution to journal › Article

*IEEE Transactions on Systems, Man and Cybernetics*, vol. SMC-8, no. 5, pp. 412-414.

}

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, Godfried

PY - 1978/1/1

Y1 - 1978/1/1

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.

UR - http://www.scopus.com/inward/record.url?scp=0017969061&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0017969061&partnerID=8YFLogxK

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 -