International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2844 Controlling Computer using Hand Gestures Pradnya Kedari 1 , Shubhangi Kadam 2 , Rajesh Prasad 3 1 Student, Department of Computer Science and Engineering, MIT School of Engineering, MIT Art, Design and Technology, Pune, Maharashtra, India 2 Student, Department of Computer Science and Engineering, MIT School of Engineering, MIT Art, Design and Technology, Pune, Maharashtra, India 3 Professor, Department of Computer Science and Engineering, MIT School of Engineering, MIT Art, Design and Technology, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Human computer interaction platform have many ways to implement as webcams and other devices like sensors are inexpensive and can get easily available in the market. The most powerful way for communication between human and machine is through gesture. For higher conveyance between the human and machine/computer to convey information, hand gesture system is very useful. Hand gestures are a sort of nonverbal type to communicate that may be employed in several fields. Research and survey papers included hand gestures applications have acquire different alternative techniques, including those supported on sensor technology and computer vision. In this system, we aimed to build a real-time gesture recognition system using hand gestures. Particularly, we will use the convolutional neural network (CNN) in throughout the process. This application presents a hand gesture-based system to control a computer that is performing different operations using neural network. Our application is defined in five phases, Image frame acquisition, Hand tracking, Features extraction, Recognition of gestures and Classification (perform desired operation). An image from the webcam will be captured, and so hand detection, hand shape features extraction, and hand gesture recognition are done. Key Words: Deep Learning, Computer Vision, Hand Gestures, Convolutional Neural Network, Python, OpenCV. 1. INTRODUCTION Gesture recognition is a popular and in-demand analysis field in Human Computer Interaction technology. It has several employments in virtual environment management, medical applications, sign language translation, robot control, music creation, or home automation. There has been a special significance recently on HCI study. Hand is the one which is most helpful communication tool in several body parts, because of its expertise. The word gesture is employed for several cases involving human motion particularly of the hands, arms, and face, just some of these are informative. The convolutional neural networks are the most popular employed technique for the image classification task. An image classifier takes an input image, or input sequence of images and categories them into one among the possible classes that it was trained to classify. They have applications in different fields such as medical domain, self-driving cars, educational domain, fraud detection, defense, etc. There are several techniques and algorithms for image classification task and also there are some challenges like data overfitting. During this project Controlling Computer using Hand Gestures, we are aimed to make a real-time application using OpenCV and Python. OpenCV is a real-time open-source computer vision and image-processing library. We’ll use it with the help of the OpenCV python package. Fig 1. Methodology of Proposed System 1.1 Market Survey Over the traditional mechanical communication technologies, gesture recognition system has become known as a most popular technology. The domain market is divided on the different basis like Technology, Type, Practice, Product, Use and Geography. Assistive robotics, Sign language detection, Immersive gaming technology, smart TV, virtual controllers, Virtual mouse, etc. 1.2 Research Gap Most of the methods used Arduino and sensors, directly device webcam is used in very few methods. Then there might be miss-recognitions of gestures in case the background environment has elements that appears like human skin. Also hand should be within the range limit. Dataset overfitting is the main concern.