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Proceedings Paper

Arabic sign language recognition based on HOG descriptor
Author(s): Ahmed Ben Jmaa; Walid Mahdi; Yousra Ben Jemaa; Abdelmajid Ben Hamadou
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Paper Abstract

We present in this paper a new approach for Arabic sign language (ArSL) alphabet recognition using hand gesture analysis. This analysis consists in extracting a histogram of oriented gradient (HOG) features from a hand image and then using them to generate an SVM Models. Which will be used to recognize the ArSL alphabet in real-time from hand gesture using a Microsoft Kinect camera. Our approach involves three steps: (i) Hand detection and localization using a Microsoft Kinect camera, (ii) hand segmentation and (iii) feature extraction using Arabic alphabet recognition. One each input image first obtained by using a depth sensor, we apply our method based on hand anatomy to segment hand and eliminate all the errors pixels. This approach is invariant to scale, to rotation and to translation of the hand. Some experimental results show the effectiveness of our new approach. Experiment revealed that the proposed ArSL system is able to recognize the ArSL with an accuracy of 90.12%.

Paper Details

Date Published: 8 February 2017
PDF: 10 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250H (8 February 2017); doi: 10.1117/12.2266453
Show Author Affiliations
Ahmed Ben Jmaa, Multimedia, Information Systems and Advanced Computing Lab. (Tunisia)
Walid Mahdi, Multimedia, Information Systems and Advanced Computing Lab. (Tunisia)
Yousra Ben Jemaa, Signal and System Research Unit (Tunisia)
Abdelmajid Ben Hamadou, Multimedia, Information Systems and Advanced Computing Lab. (Tunisia)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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