Published Paper Details:

A current field of study in computer vision and machine learning is the recognition of hand gestures and sign language for human-computer interaction. Making systems that can recognize certain gestures and utilize them to transmit information or control devices is one of its main objectives. However, hand postures are the static structure of the hand, but gestures are the dynamic movement of the hand, and gestures need to be described in the spatial and temporal domains. The two main methods used to recognize hand gestures are vision-based methods and data glove methods. A vision-based system capable of real-time sign language recognition is the major goal of this effort. The rationale behind opting for a vision-based system is that it offers a more straightforward and natural means of interaction between a person and a computer. Given that the human hand is one of the most crucial communication tools in daily life and thanks to the ongoing development of image and video processing methods, research on human-machine interaction through gesture recognition has led to the use of such technology in a huge variety of applications, including touch screens, video game consoles, virtual reality, medical applications, and sign language recognition. Deaf individuals tend to communicate with one another most naturally using sign language, however it has been noted that they have trouble interacting with hearing people in a regular fashion. In the same way that spoken language has a vocabulary of words, sign language has a vocabulary of signs.