Investigation on deep learning for off-line handwritten Arabic Character Recognition using Theano research platform

The recognition of Arabic writing is still an important challenge due to its cursive nature and high topological variability. Traditional machine-learning techniques required careful engineering and considerable domain expertise to transform raw data into a feature vector from which the classifier could classify the input pattern. In recent years, deep learning approach has acquired a reputation for solving many computer vision problems, and its application to the field of Handwritten Arabic Character Recognition (HACR) has been shown to provide significantly better results than traditional methods. In this work, we investigate the applicability of Deep Convolutional Neural Network on the recently proposed database, referred to as OIHACDB. The proposed model normalizes the handwritten character images and then employs Deep Convolutional Neural Network to classify them. The model was trained under Theano framework with dropout as a regularization technique to avoid over-fitting to give a better generalization performance. Our results shown satisfactory recognition accuracy (97.32%) and outperform some other prominent exiting methods.