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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15920
Title: Real-time recognition of sign language gestures and air-writing using leap motion
Authors: Kumar P.
Saini R.
Behera S.K.
Dogra D.P.
Pratim Roy, Partha
Published in: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Abstract: A sign language is generally composed of three main parts, namely manual signas that are gestures made by hand or fingers movements, non-manual signs such as facial expressions or body postures, and finger-spelling where words are spelt out using gestures by the signers to convey the meaning. In literature, researchers have proposed various Sign Language Recognition (SLR) systems by focusing only one part of the sign language. However, combination of different parts has not been explored much. In this paper, we present a framework to recognize manual signs and finger spellings using Leap motion sensor. In the first phase, Support Vector Machine (SVM) classifier has been used to differentiate between manual and finger spelling gestures. Next, two BLSTM-NN classifiers are used for the recognition of manual signs and finger-spelling gestures using sequence-classification and sequence-transcription based approaches, respectively. A dataset of 2240 sign gestures consisting of 28 isolated manual signs and 28 finger-spelling words, has been recorded involving 10 users. We have obtained an overall accuracy of 63.57% in real-time recognition of sign gestures. © 2017 MVA Organization All Rights Reserved.
Citation: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017, (2017), 157- 160
URI: https://doi.org/10.23919/MVA.2017.7986825
http://repository.iitr.ac.in/handle/123456789/15920
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Support vector machines
Facial Expressions
Finger spelling
Motion sensors
NN classifiers
Overall accuracies
Real time recognition
Sequence classification
Sign Language recognition
Computer vision
ISBN: 9.7849E+12
Author Scopus IDs: 57212043589
57190288840
37057132600
35408975400
56880478500
Author Affiliations: Kumar, P., Deptt of CSE, IIT, Roorkee, India
Saini, R., Deptt of CSE, IIT, Roorkee, India
Behera, S.K., Deptt of Electrical Sciences, IIT, Bhubaneswar, India
Dogra, D.P., Deptt of Electrical Sciences, IIT, Bhubaneswar, India
Roy, P.P., Deptt of CSE, IIT, Roorkee, India
Appears in Collections:Conference Publications [CS]

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