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.

翻译：连续手语识别（CSLR）致力于对无停顿连续执行的手语手势序列进行解析。本研究对近期基于深度学习的连续手语识别技术进行了实证评估，并考察了它们在不同数据集和手语类型上的性能表现。所选分析模型采用多种特征提取方法，并运用不同的训练策略。为评估这些模型对不同手语的建模能力，我们使用多个数据集进行了测试，具体包括代表不同手语体系的RWTH-PHOENIX-Weather-2014、ArabSign和GrSL数据集。此外，还针对未见过的手语使用者和句子进行了模型性能测试。实验结果为所选数据集建立了新的性能基准，并为评估技术在挑战性场景下的鲁棒性和泛化能力提供了重要见解。