Results 31 to 40 of about 762 (197)

SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition [PDF]

Trans. Mach. Learn. Res.
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing ...
Basso Madjoukeng, Ariel, Fink, Jerome, Poitier, Pierre, Kenmogne, Edith Belise, Frénay, Benoît   +4 more
openaire   +5 more sources

ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos

CoRR, 2021
Sign Language Recognition (SLR) is a challenging research area in computer vision. To tackle the annotation bottleneck in SLR, we formulate the problem of Zero-Shot Sign Language Recognition (ZS-SLR) and propose a two-stream model from two input modalities: RGB and Depth videos.
Razieh Rastgoo, Kourosh Kiani, Sergio Escalera   +2 more
openaire   +2 more sources

Multi-cue temporal modeling for skeleton-based sign language recognition

Frontiers in Neuroscience, 2023
Sign languages are visual languages used as the primary communication medium for the Deaf community. The signs comprise manual and non-manual articulators such as hand shapes, upper body movement, and facial expressions.
Oğulcan Özdemir, İnci M. Baytaş, Lale Akarun   +2 more
doaj   +1 more source

A Comparative Review on Applications of Different Sensors for Sign Language Recognition

Journal of Imaging, 2022
Sign language recognition is challenging due to the lack of communication between normal and affected people. Many social and physiological impacts are created due to speaking or hearing disability.
Muhammad Saad Amin, Syed Tahir Hussain Rizvi, Md. Murad Hossain   +2 more
doaj   +1 more source

Recognition of Sign Language Based on Hand Gestures

, 2022
The target of SLR or sign language recognition is to interpret the sign language into text, respectively. So the deaf and mute people can communicate with ordinary people easily.
Dey, Amrita, Gunji, Bala Murali, Zeeshan Mohammed, Isahak Karajagi, Bhargav, Nikhil M., Sathyajith, Sachdev   +4 more
core   +1 more source

Sign Language Recognition

, 2021
Sign Language is mainly used by deaf (hard hearing) and dumbpeople to exchangeinformation between their own community and with other people. It is a language wherepeople use theirhand gestures to communicate as they can’t speak or hear.
Satwik Ram Kodandaram, N Pavan Kumar, Sunil G L
core   +1 more source

A Novel Image-Based Arabic Hand Gestures Recognition Approach Using YOLOv7 and ArSL21L [PDF]

Fayoum University Journal of Engineering
Recognizing and documenting Arabic sign language has recently received much attention because it enhances communication between deaf persons and normal people.
Fatma Mazen, Mai Ezz-Eldin
doaj   +1 more source

Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language

Algorithms, 2020
Sign Languages (SLs) are visual–gestural languages that have developed naturally in deaf communities. They are based on the use of lexical signs, that is, conventionalized units, as well as highly iconic structures, i.e., when the form of an utterance ...
Valentin Belissen, Annelies Braffort, Michèle Gouiffès   +2 more
doaj   +1 more source

Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges

IEEE Access
The Deaf are a large social group in society. Their unique way of communicating through sign language is often confined within their community due to limited understanding by individuals outside of this demographic.
Tangfei Tao, Yizhe Zhao, Tianyu Liu, Jieli Zhu   +3 more
doaj   +1 more source

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors

Sensors, 2016
Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random ...
Ruiliang Su, Xiang Chen, Shuai Cao, Xu Zhang   +3 more
doaj   +1 more source