Abstract:
Sign Language is the method of communication of mute people all over the world. There
are about 70 Million sign language users around the world. But only a few percent of people
who can hear and speak know sign language. This makes it difficult for mute people to communicate.
Computer-based Sign Language Recognition is a breakthrough technology to overcome
this problem. After pandemic businesses and organizations have started adapting online video
conferencing platforms for carrying out meetings, workshops, interviews, collaborations, etc.
The aim of this project is to provide a practical solution for sign language interpretation. Here
we propose a lightweight real-time and integrable sign language detection application, that can
be used in any video conferencing platform such as google meet, microsoft teams, zoom, discord,
etc. Here we have used deep learning algorithms, image processing and the concept of virtual
cameras to achieve our goal. We describe a desktop application to sign language detection in
the browser in order to demonstrate its usage possibility in videoconferencing applications. We
use the MediaPipe Holistic pipeline and LSTM for pose detection and to train and predict sign
languages. It shows 91%-93% prediction accuracy while the latency is still under 4ms.
Keywords: Sign Language Translation,American Sign Language, LSTM, Virtual Camera,
Hand Gesture Recognition, OpenCV, video-conferencing, Sign Language Translator for Meeting
Apps, Real Time Sign Language Translation, Google Meet, Microsoft Teams, Zoom.
