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Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition

2014-05-20 · Khaoula jayech, Nesrine Trimech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara

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

📄 PDF Abstract BibTeX arXiv:1405.5248

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