<![CDATA[This study presents a systematic literature review of Hand Sign Recognition (HSR) technologies, focusing on advancements from 2015 to 2025. Analyzing 500 articles from Google Scholar using VOSViewer, we identify key trends, challenges, and gaps in the field. Findings reveal a predominant focus on static gesture recognition using deep learning models like CNNs and YOLO, with accuracies exceeding 90% in many cases. However, dynamic gesture recognition, robustness to lighting variations, and integration of facial expressions remain understudied. Bibliometric analysis highlights declining publication trends in recent years, signaling a need for innovative approaches, such as hybrid models and interdisciplinary collaboration. This review underscores the importance of addressing real-world deployment challenges to enhance accessibility for individuals with hearing or speech disabilities.]]>
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