Printed/handwritten Arabic script identification using local features and GMMs

Since printed/handwritten Arabic text recognition is a very challenging research field and the recognition methodologies are different, it is important to separate these two types of texts before the recognition phase. In this paper, we introduce a simple and effective method to identify printed and handwritten Arabic words using local features. A Gaussian Mixture Models (GMMs) based approach is used to model the printed and handwritten classes. Experimental results using some parts of the freely available IFN/ENIT, AHTID/MW and APTI databases show that our method is robust and provides very good identification performance.