Image matching technique based on SURF descriptors for offline handwritten Arabic word segmentation Online publication date: Thu, 02-Jul-2020
by Maamar Kef; Leila Chergui
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 19, No. 3, 2020
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.
Online publication date: Thu, 02-Jul-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com