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
This project offers an experimental system for real-time recognition of German Sign Language (DGS). It processes live video input of a person signing and aims to predict the specific DGS signs being made. The system is designed for researchers or developers exploring the challenges of live sign language subtitling.
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.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work