Smart PPT Presentation: Using Hand Gestures

Traditional computer systems depend heavily on physical input devices such as a mouse and keyboard for interaction. While these devices are effective, they have several limitations including hardware dependency, wear and tear, hygiene issues in shared environments, and limited accessibility for physically challenged users. This project presents a Hand Gesture Controlled System that enables users to interact with computers using hand movements captured through a webcam. The system uses Computer Vision and Machine Learning algorithms to detect, track, and interpret hand gestures in real-time. By recognizing specific gestures, the system can perform actions such as cursor movement, clicking, scrolling, and volume control without any physical contact. The proposed system provides a touchless, cost-effective, and user-friendly alternative to traditional input devices. It is especially useful in smart environments, public systems, medical facilities, and situations where maintaining hygiene is essential. This technology enhances accessibility and represents a step forward in Human-Computer Interaction (HCI).

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-28
DOI
https://doi.org/10.5281/zenodo.19843723
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart PPT Presentation: Using Hand Gestures

Sanika Arkile, Renuka Vakhare, Shweta Lilhare, Triveeni Suryawansh et al.
Zenodo (CERN European Organization for Nuclear Research)
Hand Gesture Recognition Systems
article

Smart PPT Presentation: Using Hand Gestures

Sanika Arkile, Renuka Vakhare, Shweta Lilhare, Triveeni Suryawansh, Dr. Devidas Thosar
article en

Abstract

Traditional computer systems depend heavily on physical input devices such as a mouse and keyboard for interaction. While these devices are effective, they have several limitations including hardware dependency, wear and tear, hygiene issues in shared environments, and limited accessibility for physically challenged users. This project presents a Hand Gesture Controlled System that enables users to interact with computers using hand movements captured through a webcam. The system uses Computer Vision and Machine Learning algorithms to detect, track, and interpret hand gestures in real-time. By recognizing specific gestures, the system can perform actions such as cursor movement, clicking, scrolling, and volume control without any physical contact. The proposed system provides a touchless, cost-effective, and user-friendly alternative to traditional input devices. It is especially useful in smart environments, public systems, medical facilities, and situations where maintaining hygiene is essential. This technology enhances accessibility and represents a step forward in Human-Computer Interaction (HCI).

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 4%
Hand Gesture Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.