Arabic handwritten script recognition system based on HOG and gabor features

Considered as among the most thriving applications in the pattern recognition field, handwriting recognition, despite being quite matured, it still raises so many research questions which are a challenge for the Arabic Handwritten Script. In this paper, we investigate Support Vector Machines (SVM) for Arabic Handwritten Script recognition. The proposed method takes the handcrafted feature as input and proceeds with a supervised learning algorithm. As designed feature, Histogram of Oriented Gradients (HOG) is used to extract feature vectors from textual images. The Multi-class SVM with an RBF kernel was chosen and tested on Arabic Handwritten Database named IFN/ENIT. Performances of the feature extraction method are compared with Gabor filter, showing the effectiveness of the HOG descriptor. We present simulation results so that we will be able to prove that the good functioning on the suggested system based-SVM classifier.