Sign Language Translation Techniques Using Artificial Intelligence for the Hearing Impaired Community in Sri Lanka: A Review

Hearing Impaired individuals routinely encounter limitations in their involvement in social interactions, access to intriguing information, and participation in everyday activities, among various other aspects. However, the hardest part of their interactions with regular people is communication, because sign language is the primary language of those who are hearing impaired. However, the general public is unaware of sign language. Each country has its own sign language. However, there are some striking similarities between them. In Sri Lanka, hearing impaired people use Sri Lankan Sign Language (SLSL) as their communication language. There is several research done on Sign Language recognition and translation. But no fully functioning system is utilized for Sri Lankan Sign Language translation. To find the gap in this area, we conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method that analyses 12 studies on Sign Language Translation (SLT). As per the literature review, Image Processing (IP) and Convolutional Neural Networks (CNN) are the most used techniques for Sign Language translation. But these methods have limitations: not enough data, differences in how people use sign language, difficulty in translating in real-time, not capturing cultural aspects, needing specific equipment, and understanding the context of conversations. Recognizing and solving these problems is important, especially for languages like SLSL. Future research should focus on getting more data, making translation work for different cultures, and improving real-time translation. This will help hearing impaired people communicate better with others.