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\begin{document}
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\title{A Systematic Literature Review on the Robustness of Sign Language
Recognition Methods in Low-Light Environments}



\author[1]{Laveneswary Krishnan}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 06, 2026}


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\begin{abstract}
This paper presents a comprehensive review of the robustness of hand
gesture recognition systems in low light intensity environments spanning
the years 2018 to 2023. The primary objective is to assess the progress
made during this period and identify areas requiring further attention.
An extraction of 20 relevant journals from reputable online databases
was conducted using selected keywords. Most of the reviewed articles
delve into three crucial aspects of hand gesture recognition systems:
data acquisition, data environment, and hand gesture representation. The
system performance evaluation reveals that machine learning models
achieve recognition accuracy between 94\% and 98\%, while computer
vision models report accuracy within the range of 90\% to 95\%. The deep
learning approach shows a broader accuracy range, spanning from 90\% to
98\%. Notably, the studies reviewed utilized datasets comprising 37 hand
gestures, including 26 letters of American Sign Language (ASL) and
numeric gestures ranging from 0 to 9. This paper sheds light on the
current state of hand gesture recognition in low light environments and
provides insights into potential opportunities for further research and
development.%
\end{abstract}%



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