Sign Language Detection Using AI Machine Learning

In this research, we present an exploration of a sign language recognition system that harnesses the power of AI through TensorFlow and OpenCV. Our aim is to create a system that can quickly recognize and translate sign language gestures in time. By utilizing deep learning techniques and training a convolutional neural network (CNN) model, we effectively categorize the gestures associated with sign language signals. The importance of steps such as extracting the hand region from video frames using OpenCV is emphasized in our study. We have conducted testing to validate the efficiency and effectiveness of our system in identifying sign language motions. Additionally, we address the challenges faced during development offer suggestions to enhance the reliability of the system. Ultimately our work contributes to bridging the communication gap between individuals, with hearing impairments and the wider public by introducing an automated sign language recognition system. The results obtained from our system showcase its potential to make an impact in this field.