AIR CANVAS APPLICATION USING OPENCV AND NUMPY IN PYTHON

Abstract

In recent years, air writing has emerged as an attractive and challenging research area in image processing and pattern recognition. This has the potential to significantly advance automation processes and improve human-machine interfaces in a variety of applications. Numerous studies have been conducted to develop new techniques and methods that can reduce processing time while providing higher detection accuracy. Object tracking is an important task in computer vision. The popularity of object tracking techniques is increasing due to the availability of faster computers, cheaper and better video cameras, and the demand for automated video analysis. In general, video analysis methods include three main steps: detecting objects, tracking their movement from frame to frame, and analyzing their behavior. Object tracking includes four different topics: choosing a suitable representation of an object, choosing a tracking feature, object detection, and object tracking. Object tracking algorithms are widely used in various applications such as real-world automatic surveillance, video profiling, and vehicle navigation. The goal of the project is to develop a motion to text converter that can be used as software for smart wearable devices. It will allow users to type from the air by following the path of their finger using computer vision. The generated text can be used for various purposes such as sending messages and emails. This project is a reporter of occasional gestures and will be a powerful means of communication for the deaf. It is an effective communication method that reduces the use of mobile devices and laptops by eliminating the need for typing.