Recognition of Typewritten Characters Using Hidden Markov Models

Adeyanju, Ibrahim; Ojo, Olusola Samuel; Omidiora, E. O.

doi:10.9734/bjmcs/2016/20376

Cited by 2 publications

(2 citation statements)

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“…In evolution if the process in the HMM is a first order Markov Chain, the probabilities of the system in particular state s (t) at time t depends on its state at s (t-1) [86,87,91,92] . The probability of the HMM being in state s j at time t having generated the first t emission that is the partial probability αj (t) [86,87,92]: This is the algorithm that produces the most probable sequence of hidden states given some observations [90,93]. It applies viterbi algorithm, which is also a trellis algorithm.…”

Section: Evolutionmentioning

confidence: 99%

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Stochastic Modeling Technology for Grain Crops Storage Application : Review

Mazima¹,

Johnson²,

Manasseh³

et al. 2016

IJAIA

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Stochastic modeling is a key technique in event prediction and forecasting applications. Recently, stochastic models such as the Artificial Neural Network, Hidden Markov, and Markov Chain have received a significant attention in agricultural application. These techniques are capable of predicting the actions for the better planning and management in various fields. This work comprehensively summarizes and compares their applications such as their processing techniques, performance, as well as their strengths and limitations with regard to event prediction and forecasting. The work ends with recommendations on the appropriate techniques for cereal grain storage application.

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