People with hearing impairments are found worldwide; therefore, the development of
effective local level sign language recognition (SLR) tools is essential. We conducted a …

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 …

Vision-based sign language recognition aims at helping the hearing-impaired people to
communicate with others. However, most existing sign language datasets are limited to a …

Sign language translation (SLT) aims to interpret sign video sequences into text-based
natural language sentences. Sign videos consist of continuous sequences of sign gestures …

Word-level sign language recognition (WSLR) is a fundamental task in sign language
interpretation. It requires models to recognize isolated sign words from videos. However …

Indian Sign Language (ISL) is a complete language with its own grammar, syntax,
vocabulary and several unique linguistic attributes. It is used by over 5 million deaf people in …

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 …

Abstract Isolated Sign Language Recognition (ISLR) fits nicely in the domain of problems
that can be handled by graph-structured spatial-temporal algorithms. A recent multiscale …

Sign languages are complex languages. Research into them is ongoing, supported by large
video corpora of which only small parts are annotated. Sign language recognition can be …

In this paper, we propose a novel hybrid model, 3D recurrent convolutional neural networks
(3DRCNN), to recognize American Sign Language (ASL) gestures and localize their …