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

Authors: Maamar Kef; Leila Chergui

Addresses: Department of Computer Sciences, Batna 2 University, Batna 05000, Algeria ' Department of Computer Sciences, Batna 2 University, Batna 05000, Algeria

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.

Keywords: image matching; SURF descriptors; Arabic handwriting recognition; keypoints; segmentation.

DOI: 10.1504/IJISTA.2020.10030203

International Journal of Intelligent Systems Technologies and Applications, 2020 Vol.19 No.3, pp.216 - 233

Accepted: 08 Feb 2019
Published online: 26 Jun 2020 *

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