Real-Time American Sign Language Detection System Using Raspberry Pi and Sequential CNN

This paper presents the development of a real time American Sign Language (ASL) detection system using Raspberry Pi and a Sequential Convolutional Neural Network (CNN) model. The system aims to bridge the communication gap between the Deaf and Hard of Hearing (DHH) community and the hearing population by translating ASL gestures into text and audio outputs. The proposed system leverages the Raspberry Pi 5, a cost effective and scalable hardware platform, combined with deep learning techniques to achieve high accuracy in gesture recognition. The system utilizes the custom dataset for training and employs hand landmark detection using MediaPipe for real-time gesture analysis. The results demonstrate an 85% accuracy in recognizing ASL gestures, with real-time text and audio outputs. The system is designed for personal, educational, and public applications, offering a practical solution for enhancing communication accessibility for the DHH community. Key Words: American Sign Language(ASL), Raspberry Pi, Machine Learning, Gesture recognition, Sign Language Translation, Text-to-Speech conversion