Towards Unsupervised Learning for Arabic Handwritten Recognition Using Deep Architectures

In the pattern recognition field and especially in the Handwriting recognition one, the Deep learning is becoming the new trend in Artificial Intelligence with the sheer size of raw data available nowadays. In this paper, we highlights how Deep Learning techniques can be effectively applied for recognizing Arabic handwritten script, our field of interest, and this by investigating two deep architectures: Deep Belief Network (DBN) and Convolutional Neural Networks (CNN). The two proposed architectures take the raw data as input and proceed with a greedy layer-wise unsupervised learning algorithm. The experimental study has proved promising results which are comparable or even superior to the standard classifiers with an efficiency of DBN over CNN architecture.