  Continuous Sign Language Recognition (CSLR) focuses on the interpretation of
a sequence of sign language gestures performed continually without pauses. In
this study, we conduct an empirical evaluation of recent deep learning CSLR
techniques and assess their performance across various datasets and sign
languages. The models selected for analysis implement a range of approaches for
extracting meaningful features and employ distinct training strategies. To
determine their efficacy in modeling different sign languages, these models
were evaluated using multiple datasets, specifically RWTH-PHOENIX-Weather-2014,
ArabSign, and GrSL, each representing a unique sign language. The performance
of the models was further tested with unseen signers and sentences. The
conducted experiments establish new benchmarks on the selected datasets and
provide valuable insights into the robustness and generalization of the
evaluated techniques under challenging scenarios.
