  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 (Na\"ive Bayes, Tree Augmented Na\"ive Bayes (TAN), Forest
Augmented Na\"ive 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
