This paper proposes an attentional network for the task of Continuous Sign Language
Recognition. The proposed approach exploits co-independent streams of data to model the …

Hand and face play an important role in expressing sign language. Their features are
usually especially leveraged to improve system performance. However, to effectively extract …

Sign language, as a different form of the communication language, is important to large
groups of people in society. There are different signs in each sign language with variability …

Sign language recognition (SLR) is a very challenging task due to the complexity of learning
or developing descriptors to represent its primary parameters (location, movement, and …

In recent years, Word-level Sign Language Recognition (WSLR) research has gained
popularity in the computer vision community, and thus various approaches have been …

Sign language plays a crucial role as a distinct and vital mode of communication for diverse
groups of people in society. Each sign language encompasses a wide array of signs, each …

Sign Language Recognition (SLR) has witnessed a boost in recent years, particularly with
the surge of deep learning techniques. However, most existing methods do not exploit the …

Continuous sign language recognition (SLR) aims to translate a signing sequence into a
sentence. It is very challenging as sign language is rich in vocabulary, while many among …

The performance of existing sign language recognition approaches is typically limited by the
scale of training data. To address this issue, we propose a mutual enhancement network …

Continuous sign language recognition (CSLR) is a challenging task involving various signal
processing techniques to infer the sequences of glosses performed by signers. Existing …