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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5627
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dc.contributor.authorKumar P.-
dc.contributor.authorSaini R.-
dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorDogra D.P.-
dc.date.accessioned2020-10-06T15:56:54Z-
dc.date.available2020-10-06T15:56:54Z-
dc.date.issued2018-
dc.identifier.citationMultimedia Tools and Applications (2018), 77(7): 8823-8846-
dc.identifier.issn13807501-
dc.identifier.urihttps://doi.org/10.1007/s11042-017-4776-9-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/5627-
dc.description.abstractSign language is the only means of communication for speech and hearing impaired people. Using machine translation, Sign Language Recognition (SLR) systems provide medium of communication between speech and hearing impaired and others who have difficulty in understanding such languages. However, most of the SLR systems require the signer to sign in front of the capturing device/sensor. Such systems fail to recognize some gestures when the relative position of the signer is changed or when the body occlusion occurs due to position variations. In this paper, we present a robust position invariant SLR framework. A depth-sensor device (Kinect) has been used to obtain the signer’s skeleton information. The framework is capable of recognizing occluded sign gestures and has been tested on a dataset of 2700 gestures. The recognition process has been performed using Hidden Markov Model (HMM) and the results show the efficiency of the proposed framework with an accuracy of 83.77% on occluded gestures. © 2017, Springer Science+Business Media New York.-
dc.language.isoen_US-
dc.publisherSpringer New York LLC-
dc.relation.ispartofMultimedia Tools and Applications-
dc.subjectDepth sensors-
dc.subjectHidden Markov Model (HMM)-
dc.subjectOccluded gestures-
dc.subjectSign language-
dc.titleA position and rotation invariant framework for sign language recognition (SLR) using Kinect-
dc.typeArticle-
dc.scopusid57212043589-
dc.scopusid57190288840-
dc.scopusid56880478500-
dc.scopusid35408975400-
dc.affiliationKumar, P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India-
dc.affiliationSaini, R., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India-
dc.affiliationRoy, P.P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India-
dc.affiliationDogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, India-
dc.description.correspondingauthorKumar, P.; Department of Computer Science and Engineering, IIT RoorkeeIndia; email: pradeep.iitr7@gmail.com-
Appears in Collections:Journal Publications [CS]

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