Modelling the Flow of Character Recognition Results in Video Stream

The paper considers problems of developing stochastic models consistent with results of character image recognition in video stream. A set of assumptions that define the models structure and properties is stated. A class of distributions, namely the Dirichlet distribution and its generalizations, that set a description of the model components is pointed out; and methods for statistical estimation of the distribution parameters are given. To rank the models, the Akaike information criterion is used. The proposed theoretical distributions are verified vs sample data.

Keywords

stochastic model; video stream; the character recognition; Dirichlet distribution; Akaike criterion; goodness-of-fit Anderson-Darling tests.

References

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