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

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dc.contributor.authorBilal, Sara
dc.contributor.authorAkmeliawati, Rini
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorShafie, Amir A.
dc.date.accessioned2019-08-14T10:40:48Z
dc.date.available2019-08-14T10:40:48Z
dc.date.issued2011-05-17
dc.description.abstractUnlike 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.en_US
dc.identifier.citationBilal, 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.en_US
dc.identifier.issn10.1109/ICOM.2011.5937178
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/452
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectHand detectionen_US
dc.subjectHand posture recognitionen_US
dc.subjectFeature extractionen_US
dc.titleVision-based hand posture detection and recognition for Sign Language—A studyen_US
dc.typeArticleen_US
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