A Systematic Study Of Various Fingertip Detection Techniques For Air Writing Using Machine Learning

TL;DR

This paper systematically reviews various machine learning techniques for fingertip detection in air writing, aiming to improve gesture-based human-computer interaction by enabling touchless control.

Contribution

It provides a comprehensive analysis of different machine learning methods for fingertip detection, highlighting their advantages and limitations in air writing applications.

Findings

Several machine learning techniques achieve high accuracy in fingertip detection

Certain methods outperform others in real-time air writing scenarios

The study identifies key challenges and future directions in fingertip detection

Abstract

The recent advancement in technology breaks the barriers to communication between users and computers. The communication between humans and computers includes emotion and gesture recognition. Emotions can be recognized on the face of humans whereas gesture recognition includes hand and body gesture recognition. Fingertip detection is also part of it. Gesture recognition is the way of interaction that is used in air writing. Users can control the devices with simple gestures without touching them. It is how computers can understand human language which will reduce the interaction barriers between them. This paper discusses the different techniques that can be used for fingertip detection in air writing using machine learning