Real-Time Hand Control for Interactive Presentation

Abstract

In a world where we are constantly interacting with screens, the way we give presentations still feels stuck in the past. We are usually tethered to a laptop or fumbling with a plastic clicker. This paper explores a more natural alternative: using real-time hand gesture recognition to control PowerPoint slides. By combining computer vision with machine learning, we’ve developed a way to turn simple hand movements—like a swipe in the air—into commands. The goal isn\'t just to make presentations \"hands-free\"; it’s about making them more fluid and engaging. When you can pause a video or jump to the next slide with a flick of the wrist, you stay connected to your audience instead of your equipment. We’ll break down the technical side of how these systems track motion, the types of gestures that work best, and the balance between speed and accuracy. Ultimately, we look at how this technology is moving us toward a future where our digital tools finally understand our natural body language

Introduction

The text explains a system that uses hand gesture recognition to control PowerPoint presentations, making interaction more natural and eliminating the need for devices like a mouse or keyboard.

It describes how gestures act as a non-verbal communication method and how a vision-based system detects and interprets hand movements using a camera. The process involves capturing images, detecting the hand using skin color, converting images to black and white, extracting features (like shape, area, and direction), and recognizing gestures to send commands to control slides.

The paper reviews existing methods such as CNN, SIFT, and sensor-based approaches, noting that camera-based systems are more practical and cost-effective. It highlights applications of gesture recognition in areas like robotics, gaming, and human-computer interaction.

The proposed system uses image preprocessing, feature extraction, hand detection, tracking, skin segmentation, and edge detection to accurately identify gestures. The model is trained on hand images and achieves about 96.5% accuracy, demonstrating its effectiveness in controlling presentations efficiently and intuitively.

Conclusion

This article looked at a different ways to do things. It looked at ways to find hands like using a webcam, a remote, KNN, HMM and Markov Model. The hand gesture recognition model the hand gesture recognition model is what this thing is about it is, about the hand gesture recognition model.We read nine articles by writers about recognizing hand movements. The article that we liked the most was called \"Hand Signal Recognition Using A Real Time Tracking Technique And Hidden Markov Models\". This article is about a system that can recognize hand movements. The writer of this article used a method to find out movements in front of a fixed background. This method uses Hidden Markov Models to figure out hand signals. The results are very good. The accuracy of our hand movement recognition system is high. We did tests using pictures of people both men and women. Each person made twenty hand movements. We got sixty pictures of each hand movement. We used a total of twenty hand movements and 1200 pictures to test our hand movement recognition system. Each picture is 256 by 256 pixels. We took thirty pictures per second. It took one second to make each picture. We think this method is very good for recognizing hand movements. It works well for hand movement recognition. So we are happy that our hand movement recognition system is complete. We did not use the SIFT algorithm or other algorithms in our hand movement recognition project because they are not as good for recognizing hand movements and do not give results. We only used the method that we thought was best for recognizing hand movements. Recognizing hand movements is what we were trying to do with our hand movement recognition system. We are happy, with our results. Our hand movement recognition system works well for recognizing hand movements.

References

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Copyright

Copyright © 2026 Mrinmay Deb, Soumen Bhowmik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.