With the increasing development of technology and the emergence of smart devices in today’s life, interactive communication with these smart devices has become inevitable. Among the many methods of interactive communication, which include the use of voice and clothes equipped with motion sensors, as well as image-based methods, each of which is focused on identifying and monitoring a specific part of the body, human hand-based methods due to their characteristics Especially the hand is of double importance in this field, and therefore the estimation of the three-dimensional state of the hand using image processing has become one of the most attractive and challenging research fields related to human-machine interaction. In this article, different methods of estimating the three-dimensional state of the hand are reviewed with an emphasis on methods based on image processing, and their strengths and weaknesses are stated and compared. This comparison is based on traditional methods as well as methods related to deep learning. Also, the databases used in hand posture estimation have been introduced and the characteristics of each of them have been investigated in different applications.