Survey on Hand Gestures Recognition for Sign Translation using Artificial Intelligence

Sign language recognition is the process of converting hand signals used in the sign format into spoken or written language. It involves analysing and deciphering hand and finger gestures using computer vision and machine learning algorithms, then mapping those actions to corresponding words or phrases. There are various uses for sign language recognition, including facilitating communication between hearing and deaf persons, increasing accessibility for those with hearing loss, and improving sign language interpreting education and training programmes. Training data is diverse, covering different signers, lighting conditions, and backgrounds. Collect data with a variety of signing styles and gestures to improve the model's generalization and generate the sign values as vector format. The way we engage with people who primarily communicate through sign language may change significantly if this technology is used. Sign language can be recognized using a variety of techniques, such as sensor-based techniques, computer vision techniques, and hybrid techniques that combine both. Computer vision algorithms use visual data from cameras to identify hand movements and gestures. Sensorbased methods capture motion and record data by attaching sensors to the hands or fingers. Consequently, it may be possible to investigate various approaches to sign language recognition and predict a better method with a greater accuracy rate.