Image matching technique based on SURF descriptors for offline handwritten Arabic word segmentation

Abstract

Image matching is an important task with many applications in computer vision and robotics. Recently, several scale-invariant features have been proposed in the literature and one of them is the local descriptors namely speeded-up robust features (SURF). Those features are scale and rotation-invariant descriptor, and have the advantage to being calculated quickly and efficiently. In this paper we presents a new segmentation system of handwritten Arabic words based on SURF descriptors. Firstly, a set of Arabic characters images were used to build 106 characters' patterns, which are used by a segmentation process based on an image matching technique. Tests were performed on our new databese of handwritten Arabic words. A high correct segmentation rate was reported.