Abstract
Over the decades the increasing computational capability and development of new technologies in the field of artificial intelligence have given us the ability to translate sign language in real time. There exist two main approaches to sign language recognition, the hardware-based approach and the software-based approach. The hardware-based approach relies on using special gloves, Kinect-based devices, and different levels of sensors. On the other hand, one of the approaches to working with sign language is using neural networks, which is the softwarebased approach. In this work, I observed existing approaches and experimented with machine learning and neural network models for sign language recognition. I got the dataset of Azerbaijani Sign Language, then trained my models based on that dataset, and got the results and metrics. The dataset contained over thirteen thousand samples of signs, which can be used in Kazakh Sign Language. In the end, I discussed the probable opportunity of using the developed models.