Classifying manual and non-manual gestures in sign language recognition is a complex and
challenging task. Sign language gestures are the combination of hand, face, and body …

Continuous sign language recognition is a weakly supervised task dealing with the
identification of continuous sign gestures from video sequences, without any prior …

In this paper, we propose a set of features called temporal accumulative features (TAF) for
representing and recognizing isolated sign language gestures. By incorporating sign …

In this paper, we propose an approach to apply the Transformer with reinforcement learning
(RL) for continuous sign language recognition (CSLR) task. The Transformer has an …

Sign language recognition aims to recognize meaningful movements of hand gestures and
is a significant solution in intelligent communication between the deaf community and …

Continuous sign language recognition (CSLR) is a very challenging task in intelligent
systems, since it requires to produce real-time responses while performing computationally …

Continuous sign language recognition (CSLR) is challenging due to the complexity of video
background, hand gesture variability, and temporal modeling difficulties. This work proposes …

The Sign Language Recognition system intends to recognize the Sign language used by the
hearing and vocally impaired populace. The interpretation of isolated sign language from …

Sign language is a visual language used by persons with hearing and speech impairment to
communicate through fingerspellings and body gestures. This paper proposes a framework …

Sign Language Recognition (SLR) targets on interpreting the sign language into text or
speech, so as to facilitate the communication between deaf-mute people and ordinary …