Modelling the Flow of Character Recognition Results in Video Stream

Abstract

The paper considers problems of developing stochastic models consistent with results of character image recognition in video stream. A set of assumptions that dene 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 veried vs sample data.