A Comprehensive Review of Vietnamese Sign Language Recognition Techniques

The paper presents a systematic quantitative literature review of Vietnamese Sign Language recognition techniques developed between 2015 and 2025. VSL recognition plays a vital role in bridging communication gaps and enhancing accessibility for the deaf and hard-of-hearing community in Vietnam. To identify and synthesize current trends and challenges, we conducted a structured search and screening process across major academic databases. These works were analyzed based on recognition approach (e.g., computer vision, wearable sensors, data-driven methods, and multimodal data fusion), datasets used, feature extraction strategies, classification models, and performance metrics. Descriptive statistics were used to map the evolution of methods over time, while comparative analyses highlighted the strengths and limitations of different techniques across real-time and static recognition tasks. Our findings indicate a growing shift towards deep learning and sensor fusion methods, though limitations persist in dataset availability, model generalizability, and real-world deployment. This review provides critical insights into current research gaps and offers guidance for future work on scalable, culturally adaptive VSL recognition systems.