Title: SIFT descriptors for Arabic handwriting recognition

Authors: Leila Chergui; Maamar Kef

Addresses: Research Laboratory on Computer Science's Complex Systems, ReLa (CS), University Larbi Ben Mhidi, Street 1 Novembre 1945, Oum El Bouaghi 04000, Algeria ' Department of Computer Sciences, University Hadj Lakhder, Street Chahid Boukhlouf, Batna 05000, Algeria

Abstract: In this paper, a new recognition system for off-line Arabic handwritten words is presented. The proposed system uses for the first time scale invariant feature transform (SIFT) descriptors to extract features from Arabic handwritten words. It is also based on keypoints matching for achieving classification. In our method, word's images are divided vertically into five frames; from each frame a set of keypoints using SIFT descriptors is extracted and compared to classes' models which includes the most discriminating keypoints extracted from words samples of the same class. Finally, the recognition process was achieved through a keypoints matching procedure, using the Euclidean distance. The paper presents also a new large Arabic handwritten word database. This database provides a new framework for benchmarking and gives a new freely available handwritten word dataset. Several tests have been performed using our new database and the well known IFN/ENIT database for comparison purposes. The reported results show the robustness and efficiency of the proposed approach.

Keywords: Arabic handwriting recognition; feature extraction; SIFT descriptors; keypoint matching; Arabic database; Arabic handwritten word database; benchmarking.

DOI: 10.1504/IJCVR.2015.072193

International Journal of Computational Vision and Robotics, 2015 Vol.5 No.4, pp.441 - 461

Received: 25 Jan 2014
Accepted: 18 Oct 2014
Published online: 03 Oct 2015 *

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