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Application of Bayesian networks for pattern recognition: Character recognition case

Pattern recognition is a wide field in progress. In particular, handwriting recognition has known a great development in the recent years. Several solutions have been directed towards the use of Bayesian networks, which have shown their ability to solve complex problems in many areas, and that is thanks to their ability to model inaccuracies, which are lacunae highly present in the manuscript field. In this paper, we recall the basics of these networks and the difficulties come across in their learning and inference algorithms to make a good decision. We present a state of using the BNs and especially RBDs in the pattern recognition and more exactly in the character recognition. We show, through the various considered works, the contribution of this technique in solving the limitations of the Markov models and its ability to represent efficiently the temporal notion and the dependencies between the variables during the writing process. Moreover, we retain the recorded limitations and some development perspectives.