A SURVEY ON AIR WRITING CHARACTER RECOGNITION AND TRANSLATION

Abstract

Computer systems are increasingly using airwriting recognition since it can be done quickly and intelligently. The term "air-writing acknowledgment apparatus" refers to the use of a stylus to draw an articulated character or expression in free space. This study presents a simple yet effective convolutional neural network-based air-writing recognition method (CNN). It is suggested to use a plausible algorithm tracking hands to reward air-writing circles captured through a web camera. This study provides a straightforward but efficient method for recognizing air-writing that uses deep convolutional neural networks (CNNs). With the aid of hand motions, users can write characters in the air that are subsequently translated into written text using an air writing character recognition and translation technology. Convolutional neural networks (CNN), which are trained to recognise and decipher human hand gestures, power this technology. The device uses a camera to record the hand movements and then runs the data through CNN to identify the characters being typed. The identified characters are then converted into text that may be presented on a screen using text-to-speech technology.