Result Paper on Air Writing Recognition Using Machine Learning Algorithms

Abstract

Air writing is a novel gesture-based input technique that enables users to write in the air using their hand movements instead of relying on physical surfaces. This approach is particularly useful in accessibility solutions, human-computer interaction (HCI), and augmented/virtual reality (AR/VR) applications[1]. Traditional input methods like keyboards and touchscreens have inherent limitations, especially for individuals with motor disabilities or in scenarios requiring hands-free interaction.

In this study, we propose a Convolutional Neural Network (CNN)-based air-writing recognition system that processes real-time hand gestures captured via a standard webcam. The system employs OpenCV for hand tracking, extracts key movement features, and classifies them into characters using a deep learning model. Our method achieves high accuracy and real-time performance, making it feasible for applications in education, assistive technology, smart home interfaces, and digital signatures.