American Sign Language (ASL) Detection System using Machine Learning

American Sign Language (ASL) Detection System using Machine Learning

Akshunya Banjarey1, Bhavya Chauhan2, Vibhor Sharma3, Nitesh Kumar4, Sachin Garg5
Department of Information Technology Maharaja Agrasen Institute of Technology, Delhi, India

Abstract: One of the main challenges of communicating with people who have hearing disabilities is to understand their sign language. This paper presents a dedicated research project to investigate the difficulties involved in recognizing characters in American Sign Language (ASL), which is the most widely used sign language in the world. Sign language is essential for communication among people with hearing or speech impairments, but it can be hard for those who are not familiar with it, as the signs made by people with disabilities may look complicated or messy. Effective communication requires a two-way exchange. This paper proposes a Sign Language detection system that uses American Sign Language, where users can take pictures of hand gestures through a web camera and get them analyzed. The system aims to predict and show the name that matches the picture. The proposed research proposes a method for detecting sign language that uses the collaborative features of the OpenCV and MediaPipe frameworks. The Convolutional Neural Network (CNN) is used to train the model and identify the pictures. The proposed method shows a high detection rate and excellent accuracy.

Keywords: American Sign Language, OpenCV, MediaPipe, Convolutional Neural Network