Vision-based hand posture detection and recognition for Sign Language—A study

Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/452

Title:	Vision-based hand posture detection and recognition for Sign Language—A study
Authors:	Bilal, Sara Akmeliawati, Rini Salami, Momoh-Jimoh E. Shafie, Amir A.
Keywords:	Hand detection Hand posture recognition Feature extraction
Issue Date:	17-May-2011
Publisher:	IEEE
Citation:	Bilal, S., Akmeliawati, R., El Salami, M. J., & Shafie, A. A. (2011, May). Vision-based hand posture detection and recognition for Sign Language—A study. In 2011 4th International Conference on Mechatronics (ICOM) (pp. 1-6). IEEE.
Abstract:	Unlike general gestures, Sign Languages (SLs) are highly structured so that it provides an appealing test bed for understanding more general principles for hand shape, location and motion trajectory. Hand posture shape in other words static gestures detection and recognition is crucial in SLs and plays an important role within the duration of the motion trajectory. Vision-based hand shape recognition can be accomplished using three approaches 3D hand modelling, appearance-based methods and hand shape analysis. In this survey paper, we show that extracting features from hand shape is so essential during recognition stage for applications such as SL translators.
URI:	http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/452
ISSN:	10.1109/ICOM.2011.5937178
Appears in Collections:	Research Articles

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