Nabil ghanmy

Mohamed Ali Mahjoub, Nabil Ghanmy, Khlifia jayech et al.

In this paper we address the problem of offline Arabic handwriting word recognition. Off-line recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition using multiple Bayesian networks. First, we cut the image in several blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including Zernik and Hu moments. Finally, we apply four variants of Bayesian networks classifiers (Naïve Bayes, Tree Augmented Naïve Bayes (TAN), Forest Augmented Naïve Bayes (FAN) and DBN (dynamic bayesian network) to classify the whole image of tunisian city name. The results demonstrate FAN and DBN outperform good recognition rates

Nabil ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.