Sign Language Recognition Using Convolutional Neural Network

Abstract

This project introduces a real-time sign language detection system powered by Convolutional Neural Networks (CNNs), designed to aid individuals with hearing impairments in communication. Leveraging OpenCV for video capture and hand detection, along with custom modules for hand tracking and image classification, the system seamlessly integrates deep learning methodologies. A pre-trained CNN model, trained on a comprehensive dataset of sign language gestures, forms the core of the system, ensuring accurate classification in real-time. By capturing video frames from a webcam, detecting hands, and processing them through the CNN model, the system provides immediate feedback by overlaying predicted gestures onto the video stream. Through its effective implementation, this project underscores the potential of CNNs in facilitating accessibility and inclusivity, while also paving the way for future enhancements and applications in assistive technology and human-computer interaction. Top of Form Keywords— Real-time, Sign Language Detection, Convolutional Neural Networks (CNNs), Communication, Hearing Impairments, Deep Learning.