Sign Language Detection Using Mediapipe and ML
K.M.S.V. Praneetha · International Journal for Research in Applied Science and Engineering Technology · 2025
Sign language recognition systems are crucial for improving communication between the hearing and deaf communities. This paper explores the development of a real-time sign language detection system that uses a combination of computer vision techniques and machine learning algorithms. Specifically, it employs MediaPipe, a computer vision framework, to extract hand landmarks, and a Random Forest Classifier to classify the gestures. This system is capable of recognizing ten distinct signs in real time. The paper provides a detailed description of the design, development, and implementation of the system, as well as its evaluation. The findings demonstrate that the system can detect signs with over 80% accuracy and offers potential for further development in sign language accessibility applications.