Published Paper Details:

The aim of this project is to build a smart system that can turn sign language into spoken words and written text. It is designed to help people who are deaf or cannot speak communicate more easily with those who do not understand sign language. Most sign language translator devices today are expensive and can only show text. To solve this, the system uses modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML)[2]. These allow it to quickly recognize hand gestures and convert them into clear speech and readable text. The system uses computer vision to track and understand hand movements, along with a speech module that produces natural-sounding voice. It is simple, affordable, and easy to carry, making it useful in everyday situations like schools, hospitals, and public places. Another important feature is that the system can improve over time. As it collects more data, it can learn new signs and support different sign languages, becoming more accurate. We extract optical flow features based on human pose estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%.