Paper Contents
Abstract
Recent advancements in hardware and internet connectivity have enabled the widespread application of AI and big data in fields like computer vision. This thesis presents a stereo vision-based handwriting recognition system that tracks finger movements in 3D space using depth data, improving accuracy with techniques such as Particle Swarm Optimization. Additionally, it explores the growing role of gesture-controlled systems in human-computer interaction (HCI), showcasing a Virtual Calculator that leverages hand gestures for calculations and a personalized interface for custom gesture input. The thesis also introduces a deep learning approach for air-writing recognition, which aids communication for the deaf, and highlights gesture-driven creative applications like the Air Canvas. Finally, it reviews optimization strategies for large language models (LLMs) and the role of code-based self-verification in enhancing computational tasks
Copyright
Copyright © 2024 Ansari Ariba Tabrez Ahmed. This is an open access article distributed under the Creative Commons Attribution License.
