Despite existing pioneering works on sign language translation (SLT), there is a non-trivial
obstacle, ie, the limited quantity of parallel sign-text data. To tackle this parallel data …

Abstract Machine learning for sign languages is bottlenecked by data. In this paper, we
present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language …

Hand gesture serves as a crucial role during the expression of sign language. Current deep
learning based methods for sign language understanding (SLU) are prone to over-fitting due …

Movement dataset reviews exist but are limited in coverage, both in terms of size and
research discipline. While topic-specific reviews clearly have their merit, it is critical to have a …

Sign language recognition is a challenging problem where signs are identified by
simultaneous local and global articulations of multiple sources, ie hand shape and …

Given the achievements in automatically translating text from one language to another, one
would expect to see similar advancements in translating between signed and spoken …

Signed languages are the primary means of communication for many deaf and hard of
hearing individuals. Since signed languages exhibit all the fundamental linguistic properties …

Existing work on sign language translation-that is, translation from sign language videos into
sentences in a written language-has focused mainly on (1) data collected in a controlled …

Sign language recognition using computational models is a challenging problem that
requires simultaneous spatio-temporal modeling of the multiple sources, ie faces, hands …

Every day we see many people with disabilities like the deaf, the dumb and the blind, etc.
Sign language is one of the communication tools for the hard-of-hearing people community …