Gesturenet: Real-time Sign Language Recognition using a Hybrid Neural Network

Author(s)	Aditya Narayan, Soumitra Das, Aditya Pimpale, Sandeep Verma, Gaurav Vetal
Country	India
Abstract	This system bridges the sign-to-speech gap for deaf individuals. A real-time sign language recognition system uses a hybrid neural network to translate hand gestures into text. By combining pre-trained and custom models, it achieves high accuracy (>90%) while efficiently processing video data. This innovative approach promotes inclusive communication and shatters communication barriers.
Keywords	Neural Network Model, Sign Language Recognition, Sign language, muteness, deep learning models, Custom Sign Language. Region of Interest, MediaPipe model, Support Vector Machine, Artificial Neural Network, American Sign Language, Vietnamese Sign Language.
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-30
Cite This	Gesturenet: Real-time Sign Language Recognition using a Hybrid Neural Network - Aditya Narayan, Soumitra Das, Aditya Pimpale, Sandeep Verma, Gaurav Vetal - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18296
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.18296
Short DOI	https://doi.org/gts4s7

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