Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic

TL;DR

This paper presents a real-time continuous sign language recognition system using deep learning and MediaPipe Holistic, achieving 88.23% accuracy on an Indian Sign Language dataset.

Contribution

It introduces a novel deep learning approach employing LSTM with MediaPipe Holistic for real-time sign language recognition.

Findings

Achieved 88.23% recognition accuracy.

Utilized MediaPipe Holistic for comprehensive landmark tracking.

Demonstrated real-time sign language translation capability.

Abstract

Sign languages are the language of hearing-impaired people who use visuals like the hand, facial, and body movements for communication. There are different signs and gestures representing alphabets, words, and phrases. Nowadays approximately 300 sign languages are being practiced worldwide such as American Sign Language (ASL), Chinese Sign Language (CSL), Indian Sign Language (ISL), and many more. Sign languages are dependent on the vocal language of a place. Unlike vocal or spoken languages, there are no helping words in sign language like is, am, are, was, were, will, be, etc. As only a limited population is well-versed in sign language, this lack of familiarity of sign language hinders hearing-impaired people from communicating freely and easily with everyone. This issue can be addressed by a sign language recognition (SLR) system which has the capability to translate the sign language into vocal language. In this paper, a continuous SLR system is proposed using a deep learning model employing Long Short-Term Memory (LSTM), trained and tested on an ISL primary dataset. This dataset is created using MediaPipe Holistic pipeline for tracking face, hand, and body movements and collecting landmarks. The system recognizes the signs and gestures in real-time with 88.23% accuracy.