Deep Learning in Sinhala Sign Language Recognition: A Systematic Decade Review

The paper reviews the progress in Sinhala Sign Language recognition (SSL) over the last decade, tracing the techniques from early image processing to advanced deep learning models, indicating methodologies involved, datasets used, and challenges. The paper shows that the evolution has progressed from basic classifiers to sophisticated machine learning techniques that integrate CNNs, RNNs, and transformer architectures. It also presents the implementation of multimodal systems using hand gestures, facial expressions, and body postures. The review identifies the limitations of current recognition in SSL, such as scarce complete datasets, technical difficulties, and a lack of robust continuous recognition. Finally, it points out future research directions, stressing that further work must focus on growing datasets, making lightweight models for edge devices, and collaboration for further growth in SSL recognition technology.