Towards Offline Arabic Handwritten Character Recognition Based on Unsupervised Machine Learning Methods: A Perspective Study

This paper proposes an alternative approach for the problem of Arabic handwritten character recognition. The proposed model is based on Deep Belief Networks (DBNs) which are unsupervised machine learning methods. A greedy layer-wise fashion based on Restricted Boltzmann Machines and contrastive divergence learning algorithm will be used to train such model. Previous studies have shown that DBNs are capable to extract a set of sparse features, which can be used to code the initial data in an efficient way. The assumption is that such representation must improve the linear separation among the different classes and thus a simple classification algorithm, like softmax regression, should be sufficient to achieve accurate recognition rates. The literature reviewed showed that this alternative approach has not been considered yet in the context of Arabic character recognition, which deserves to be investigated and evaluate its performance for such problem.