The recent developments in deep learning techniques evolved to new heights in various
domains and applications. The recognition, translation, and video generation of Sign …

The recent advancements of unsupervised deep generative models have produced
incredible results in image and video generation tasks. However, existing approaches still …

Despite the recent success of deep learning in video-related tasks, deep models typically
focus on the most discriminative features, ignoring other potentially non-trivial and …

In this paper, a comparative experimental assessment of computer vision-based methods for
sign language recognition is conducted. By implementing the most recent deep neural …

Word-level sign language recognition (SLR) is a significant task which transcribes a sign
language video into a word. Currently, deep-learning-based frameworks mostly combine …

Sign language recognition (SLR) plays a crucial role in bridging the communication gap
between the hearing and vocally impaired community and the rest of the society. Word-level …

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

There are two basic problems in sign language recognition (SLR):(a) isolated word SLR and
(b) continuous SLR. Most of the existing continuous SLR methods are extensions of the …

Currently, video-based Sign language recognition (SLR) has been extensively studied using
deep learning models such as convolutional neural networks (CNNs) and recurrent neural …

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 …