Real-Time American Sign Language Recognition Using Machine Learning

This paper presents the design and implementation of a real-time sign language recognition system focused on detecting static hand gestures representing the English alphabets (A–Z). The project leverages computer vision and machine learning to provide an accessible, low-cost tool for communication between hearing-impaired individuals and non-signers. The system captures webcam input, extracts hand landmarks using Google’s Mediapipe framework, and classifies gestures through a trained Random Forest model. A Streamlit-based user interface displays the detected letters and enables real-time sentence construction with editing controls. The model, trained on a custom dataset, achieved an accuracy of 99.71% in testing. This paper discusses the system’s methodology, architecture, dataset preparation, model training, and evaluation. The results highlight the potential of combining machine learning and vision-based solutions to support inclusive communication technologies. Key Words: Sign Language, Gesture Recognition, Mediapipe, Random Forest, Streamlit, Computer Vision, Accessibility, Human-Computer Interaction