Arabic sign language recognition based on HOG descriptor

Ahmed Ben Jmaa; Walid Mahdi; Yousra Ben Jemaa; Abdelmajid Ben Hamadou

doi:10.1117/12.2266453

8 February 2017 Arabic sign language recognition based on HOG descriptor

Ahmed Ben Jmaa, Walid Mahdi, Yousra Ben Jemaa, Abdelmajid Ben Hamadou

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250H (2017) https://doi.org/10.1117/12.2266453
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

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%.

Citation Download Citation

Ahmed Ben Jmaa, Walid Mahdi, Yousra Ben Jemaa, and Abdelmajid Ben Hamadou "Arabic sign language recognition based on HOG descriptor", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250H (8 February 2017); https://doi.org/10.1117/12.2266453

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