Arabic handwritten characters recognition using Deep Belief Neural Networks

In the handwriting recognition field, the deep learning is becoming the new trend thanks to their ability to deal with unlabeled raw data especially with the huge size of raw data available nowadays. In this paper, we investigate Deep Belief Neural Network (DBNN) for Arabic handwritten character/word recognition. The proposed system takes the raw data as input and proceeds with a grasping layer-wise unsupervised learning algorithm. The approach was tested on two different databases. For the character level one, the results were promising with an error classification rate of 2.1% on the HACDB database. Unlike, the character level, the evaluation on the ADAB database to deal with word level shows an error rate which exceeds the 40%. Hence, the proposed DBNN structure is not already able to deal with high-level dimensional data and thus has to be improved.