  Effective communication is paramount for the inclusion of deaf individuals in
society. However, persistent communication barriers due to limited Sign
Language (SL) knowledge hinder their full participation. In this context, Sign
Language Recognition (SLR) systems have been developed to improve communication
between signing and non-signing individuals. In particular, there is the
problem of recognizing isolated signs (Isolated Sign Language Recognition,
ISLR) of great relevance in the development of vision-based SL search engines,
learning tools, and translation systems. This work proposes an ISLR approach
where body, hands, and facial landmarks are extracted throughout time and
encoded as 2-D images. These images are processed by a convolutional neural
network, which maps the visual-temporal information into a sign label.
Experimental results demonstrate that our method surpassed the state-of-the-art
in terms of performance metrics on two widely recognized datasets in Brazilian
Sign Language (LIBRAS), the primary focus of this study. In addition to being
more accurate, our method is more time-efficient and easier to train due to its
reliance on a simpler network architecture and solely RGB data as input.
