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

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.