Gesture Recognition to Text Conversion for Human-Computer Interaction Through Computer Vision Technology

The development of computer vision technology in recent years has heightened the need for more intuitive and natural human-computer interaction. This research offers a novel technique for real-time gesture identification and text conversion to address the problem of bridging the communication gap between people and digital systems. Our research goal is to make it possible for users to engage with computers using gestures and then convert those gestures into text-based commands or input, promoting natural human-machine interaction. In order to quickly recognize and decipher human gestures, this study blends deep learning methods with contemporary computer vision algorithms. We suggest a strong framework that can recognize a variety of hand and body actions, enabling users to effortlessly transmit complex directions. Our system also makes use of also known as NLP, to translate identified movements into usable text, enabling users to operate applications and carry out tasks using gesture-based commands.