Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition
This work addresses the problem of Arabic handwriting recognition for specific applications like city name identification, representing an incremental improvement in domain-specific methods.
The paper tackles Arabic handwritten word recognition for Tunisian city names by developing a dynamic hierarchical Bayesian network model that uses vertical histogram projection segmentation with Zernike and HU moments for feature extraction, achieving promising results on the IFN/ENIT database.
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.