arpita739/Real-time-Vernacular-Sign-Language-Recognition-using-MediaPipe-and-Machine-Learning

The deaf-mute community have undeniable communication problems in their daily life. Recent developments in artificial intelligence tear down this communication barrier. The main purpose of this paper is to demonstrate a methodology that simplified Sign Language Recognition using MediaPipe’s open-source framework and machine learning algorithm. The predictive model is lightweight and adaptable to smart devices. Multiple sign language datasets such as American, Indian, Italian and Turkey are used for training purpose to analyze the capability of the framework. With an average accuracy of 99%, the proposed model is efficient, precise and robust. Real-time accurate detection using Support Vector Machine (SVM) algorithm without any wearable sensors makes use of this technology more comfortable and easy.

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This project offers a way to interpret sign language in real-time, helping bridge communication gaps for the deaf-mute community. It takes live video of a person signing, processes their hand movements, and translates them into recognized words or phrases. This tool is designed for anyone interacting with sign language users, such as educators, customer service professionals, or family members, to facilitate smoother conversations.

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Use this if you need a lightweight, real-time sign language recognition system that doesn't require special wearable sensors.

Not ideal if you require recognition for sign languages or dialects not included in the American, Indian, Italian, or Turkish datasets.

sign-language-interpretation inclusive-communication assistive-technology deaf-community-support
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 17 / 25

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Jupyter Notebook

License

MIT

Last pushed

Jun 06, 2021

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