Advancements in Sign Language Recognition: A Comprehensive Review and Future Prospects

Sign language (SL) is a vital mode of communication, bridging the gap between the hearing impaired and hearing communities. However, SL, despite its paramount importance, has received relatively limited attention from researchers. Its unique structural characteristics, distinct from those of natural languages, present novel challenges that require innovative solutions. Remarkable technological advancements, notably in Artificial Intelligence (AI) and machine learning, offer promising avenues for automated Sign Language translation Systems (SLTS). This review study addresses the crucial need for a comprehensive synthesis of existing research by systematically examining and evaluating the progress made in SLTS. By analyzing 58 research papers, with a particular emphasis on the most frequently cited papers from each year up to 2023, we shed light on the field’s current state, identifying key advancements and challenges. This review followed a systematic approach based on clear guidelines. The methodology involved defining research questions, formulating queries, selecting studies based on clear criteria, and extracting pertinent information to address the research objectives. This review found that deep learning techniques, such as convolutional and recurrent neural networks, have shown high accuracy in sign language recognition, and their performance in recognizing the variety of signs has steadily improved over time. Additionally, integrating non-manual features has proven pivotal in enhancing recognition accuracy. Future research should refine advanced deep learning models and integrate non-manual features to improve system accuracy and applicability. These ongoing advancements hold the potential to revolutionize communication and break down barriers for individuals who rely on sign language as their primary mode of communication.