This work presents a meta study covering around 300 published sign language recognition
papers with over 400 experimental results. It includes most papers between the start of the …

Sign language relies on visual gestures of human body parts to convey meaning and plays
a vital role in modern society to communicate and interact with people having hearing …

Recently, automatic hand gesture recognition has gained increasing importance for two
principal reasons: the growth of the deaf and hearing-impaired population, and the …

This paper presents a framework for hand gesture recognition based on the information
fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) …

Hand gesture recognition has attracted the attention of many researchers due to its wide
applications in robotics, games, virtual reality, sign language and human-computer …

Hand gestures are a well-known and intuitive method of human-computer interaction. The
majority of the research has concentrated on hand gesture recognition from the RGB …

In this paper, we propose a glove-based Arabic sign language recognition system using a
novel technique for sequential data classification. We compile a sensor-based dataset of 40 …

Pose-based approaches for sign language recognition provide light-weight and fast models
that can be adopted in real-time applications. This article presents a framework for isolated …

Image identification is becoming a crucial step in most of the modern world problem-solving
systems. Approaches for image detection, analysis and classification are available in glut …

Bio-inspired deep learning models have revolutionized sign language classification,
achieving extraordinary accuracy and human-like video understanding. Recognition and …