Offline Arabic Handwritten recognition system with dropout applied in Deep networks based-SVMs

As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this study, a deep learning model based on Support Vector Machine (SVM) named Deep SVM (DSVM) is represented. We applied the dropout technique on the Deep SVM (DSVM). It is worth noting that this model has an inherent capacity to choose data points crucial to classify good generalization capacities. The deep SVM is built by a stack of SVMs permitting to extracting/learning automatically features from the raw images and to realize classification, too. We chose and tested the Multi-class Support Vector Machine with an RBF kernel, as non-linear discriminative features for classification, on Handwritten Arabic Characters Database (HACDB). Further to these advantages, our model is safeguarded against over-fitting because of strong performance of dropout. Simulation outcomes prove the efficiency of the suggested model.