Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4092
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dc.contributor.authorBiswas, Sougatamoy-
dc.contributor.authorNandy, Anup-
dc.contributor.authorNaskar, Asim Kumar-
dc.contributor.authorSaw, Rahul-
dc.date.accessioned2023-11-16T12:37:19Z-
dc.date.available2023-11-16T12:37:19Z-
dc.date.issued2023-11-
dc.identifier.citation8th International Conference On Computer Vision and Image Processing (CVIP) 2023 At IIT Jammu During 3rd -5th November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4092-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractGesture recognition plays a vital role in the area of research for human-computer interaction (HCI). The integration of MediaPipe with Long Short Term Memory (LSTM) architecture holds tremendous potential for real-time hand gesture recognition. MediaPipe provides a robust and versatile framework for capturing and processing multimedia input, such as video streams from cameras or pre-recorded video files. The temporal modeling capabilities of LSTM captures the temporal dynamics of hand gestures. This research paper aims to present a novel method utilizing the MediaPipe with LSTM architecture for real-time hand gesture recognition. A test on real-time gesture recognition is performed to evaluate the performance of the suggested model. Our results demonstrate that the suggested method outperforms other state-of-theart approaches on our custom made dataset with an accuracy of 98.99%.en_US
dc.subjectGesture recognitionen_US
dc.subjectLSTMen_US
dc.subjectMediaPipeen_US
dc.subjectHuman-computer interactionen_US
dc.titleMediaPipe with LSTM Architecture for Real-Time Hand Gesture Recognizationen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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