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.

连续手语识别 (CSLR) 着重于对连续进行而没有停顿的手语姿势序列的解释。本研究对最新的深度学习 CSLR 技术进行了实证评估，并评估其在各种数据集和手语中的表现。所选择的模型实施了一系列用于提取有意义特征的方法，并采用不同的训练策略。通过在多个数据集上评估这些模型，特别是 RWTH-PHOENIX-Weather-2014、ArabSign 和 GrSL，分别代表着不同的手语，来确定它们对建模不同手语的功效。所进行的实验在所选择的数据集上建立了新的基准，并为在具有挑战性场景下评估的技术的稳健性和泛化性提供了有价值的见解。