SIFT descriptors for Arabic handwriting recognition
Leila Chergui, Maâmar Kef · International Journal of Computational Vision and Robotics · 2015
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.