Improving of handwritten Tunisian City names recognition based on Factorial Hidden Markov Model

Hidden Markov Models (HMMs) are now widely used for off-line Arabic handwriting recognition. Actually, classical HMMs are one-dimensional models, that is why to process an Arabic word image we have developed a discrete Dynamic Bayesian Network (DBN). The DBNs are an extension and a generalization of the classical HMMs, which can model the interaction between several observations and state sequences. In our study, we have represented words by factorizing two streams in different manners, where the interaction is achieved through the causal influence between observable variables in the first model and state variables in the second one. The aim of this is to consider the two flows of information together: The observations on the columns (as well as lines) are obtained by scanning the image horizontally (and also vertically) by a uniform sliding window. We have compared the two models on the recognition of off-line Arabic handwritten words. The experiments show that the first model is better and more adapted to our task than the second one.