Results 1 to 10 of about 762 (197)

Part-Wise Graph Fourier Learning for Skeleton-Based Continuous Sign Language Recognition. [PDF]

J Imaging
Sign language is a visual language articulated through body movements. Existing approaches predominantly leverage RGB inputs, incurring substantial computational overhead and remaining susceptible to interference from foreground and background noise.
Wei D, Hu H, Ma GF.
europepmc   +3 more sources

Korean Sign Language Recognition Using Transformer-Based Deep Neural Network

Applied Sciences (Switzerland), 2023
Sign language recognition (SLR) is one of the crucial applications of the hand gesture recognition and computer vision research domain. There are many researchers who have been working to develop a hand gesture-based SLR application for English, Turkey ...
Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka   +2 more
exaly   +4 more sources

An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

Sensors, 2019
For online sign language recognition (SLR) based on inertial measurement unit (IMU) and a surface electromyography (sEMG) sensor, achieving high-accuracy is a major challenge.
Fei Wang, Xingqun Zhou, Mingyao Li
exaly   +4 more sources

Sign Language Recognition Using Artificial Rabbits Optimizer with Siamese Neural Network for Persons with Disabilities [PDF]

Journal of Disability Research, 2023
Sign language recognition is an effective solution for individuals with disabilities to communicate with others. It helps to convey information using sign language.
Fadwa Alrowais, Radwa Marzouk, Anwer Mustafa Hilal, Fahd N. Al-Wesabi   +3 more
core   +3 more sources

Machine learning methods for sign language recognition: a critical review and analysis. [PDF]

Intelligent Systems with Applications, 2021
Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts ...
Adegboye, M.A., Bello, O.O., Adeyanju, I.A.   +2 more
core   +3 more sources

Deep Forest-Based Monocular Visual Sign Language Recognition

Applied Sciences, 2019
Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to ...
Qifan Xue, Dong Wang, Xuanpeng Li, Weigong Zhang   +3 more
core   +2 more sources

A Survey on Sign Language Recognition and Training Module [PDF]

ITM Web of Conferences, 2023
Communication among the deaf and non-verbal communities has long reliedon sign language recognition. From all researchers around from early electric signal-based sign language identification to more recent recognition using machine/deep learning ...
V. Anjana Devi, P. Dharishinie, T. Charulatha   +2 more
core   +2 more sources

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework

Sensors, 2016
Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors ...
Xiang Chen, Xidong Yang, Shuai Cao, Xu Zhang, Shengjing Wei   +4 more
core   +2 more sources

SignVLM: a pre-trained large video model for sign language recognition. [PDF]

PeerJ Comput Sci
Sign language recognition (SLR) plays a vital role in including people with hearing impairment in the community. It facilitates the recognition of sign gestures and converts them into spoken languages.
Luqman H.
europepmc   +3 more sources

Re-Evaluation Method by Index Finger Position in the Face Area Using Face Part Position Criterion for Sign Language Recognition

Sensors, 2023
Several researchers have proposed systems with high recognition rates for sign language recognition. Recently, there has also been an increase in research that uses multiple recognition methods and further fuses their results to improve recognition rates.
Noriaki Hori, Masahito Yamamoto
exaly   +3 more sources