  Sign language visual recognition from continuous multi-modal streams is still
one of the most challenging fields.
  Recent advances in human actions recognition are exploiting the ascension of
GPU-based learning from massive data, and are getting closer to human-like
performances.
  They are then prone to creating interactive services for the deaf and
hearing-impaired communities.
  A population that is expected to grow considerably in the years to come.
  This paper aims at reviewing the human actions recognition literature with
the sign-language visual understanding as a scope.
  The methods analyzed will be mainly organized according to the different
types of unimodal inputs exploited, their relative multi-modal combinations and
pipeline steps.
  In each section, we will detail and compare the related datasets, approaches
then distinguish the still open contribution paths suitable for the creation of
sign language related services.
  Special attention will be paid to the approaches and commercial solutions
handling facial expressions and continuous signing.
