Currently, around 1 million to 2.5 million Vietnamese belong to the Deaf and Hard of Hearing group; however, only an alarming 10 Vietnamese reach the acceptable proficiency for interpreting Vietnamese Sign Language (Kiều Tuyết & Nguyễn Yên, 2020). As a matter of consequence, their minority leads to tremendous problems including the feeling of isolation and lack of self-confidence, as well as struggles in other aspects of daily life (Xuân Mai, 2020). Concerning healthcare, although the Vietnamese Ministry of Health has stated the A2.5 principles, which assure that the Deaf and Hard of Hearing is supported by ever-present translator agents while being treated, the policies are poorly executed in real life. Hospitals lack resources or even neglect these special principles concerning the needs of Deaf and Hard of Hearing.

Current Sign Language training/learning systems have limitations that can be seen as barriers for Vietnamese Sign Language learners such as little time flexibility as the course gets harder, and inconsistency of materials (​​Hồng Quý, 2007). Due to our survey with information from 100 learners who are Deaf/ Hard of Hearing people, their families, doctors, and nurses, we also found that other problems include limited practice ability without an expert tutor, and course prices.

VieSign has been able to solve some of these dominant problems like course prices and little time flexibility with our free MOOC curriculum; however, lack of in-person practice is still untackled. Therefore, we have been looking into ways to create a Sign Language Recognition and Translation Model that can be integrated into our free MOOC platform. The model will be able to solve both aforementioned problems by providing students a way to practice sign language just through live cameras right on the platform. Our previous attempts include creating the alphabetical sign language detection using TensorFlow and Handpose model; however, we have been looking into newer computer visual detection and natural language processing technologies that may achieve greater performance in recognizing and translating continuous sign language.