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This paper presents a new probabilistic graphical model used to model andrecognize words representing the names of Tunisian cities. In fact, this workis based on a dynamic hierarchical Bayesian network. The aim is to find thebest model of Arabic handwriting to reduce the complexity of the recognitionprocess by permitting the partial recognition. Actually, we propose asegmentation of the word based on smoothing the vertical histogram projectionusing different width values to reduce the error of segmentation. Then, weextract the characteristics of each cell using the Zernike and HU moments,which are invariant to rotation, translation and scaling. Our approach istested using the IFN / ENIT database, and the experiment results are verypromising.

Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition