SignLanguageRecognition and Real-time-Vernacular-Sign-Language-Recognition-using-MediaPipe-and-Machine-Learning
These are complementary implementations of the same sign language recognition approach—one specialized for German Sign Language (DGS) and the other for vernacular sign languages—that could be used together to build a multilingual sign language recognition system using MediaPipe as the common backbone.
About SignLanguageRecognition
Tachionstrahl/SignLanguageRecognition
Real-time Recognition of german sign language (DGS) with MediaPipe
About Real-time-Vernacular-Sign-Language-Recognition-using-MediaPipe-and-Machine-Learning
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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