A study on deep learning-driven gesture controlled air canvas.

One essential component of human-computer interaction is gesture recognition, plays a pivotal role in our study on a Deep Learning-Driven Gesture Controlled Air Canvas. Analogous to emotions in the realm of human experiences,gestures serve as a rich non-verbal communication medium, shaping interactions with digital canvases. A Digital canvas is a white board that turns our hand gestures into required text. Our system introduces a groundbreaking flexible air canvas model that capitalizes on gesture recognition to enable users to manipulate a standard screen as their painting canvas. Gesture-Controlled air canvas empowers users to effortlessly generate artistic expressions directly through their gestureson the screen. The study aims to leverage deep learning techniques for enhanced gesture recognition. By integrating both vocal and linguistic features and employing deep neural networks for classification tasks, our research aspires to elevate the performance of gesture recognition systems. This study critically examines existing methodologies, addresses drawbacks in current techniques, and seeks to advance our understanding of these characteristics to foster the development of a more intuitive and emotionally resonant interaction with digital canvases. [ABSTRACT FROM AUTHOR]