Our project develops a system that can recognize the air-written words in 3D space, and then classify the recognized character into one of the possible classes. Air-writing is the new way of writing the linguistic characters or words in free area using hand or finger movements. Writing within the air may be a method to jot down one thing in an exceedingly 3D house with our finger-tip. This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs). A robust and efficient hand tracking algorithm is proposed to extract air-writing trajectories collected by a single web camera. The algorithm addresses the push-to-write problem and avoids restrictions on the users’ writing without using a delimiter and an imaginary box. A novel preprocessing scheme is also presented to convert the writing trajectory into appropriate forms of data, making the CNNs trained with these forms of data simpler and more effective. This project could be a combination of computer vision object chase and handwriting recognition machine learning. The air writing recognition system uses the digital camera of a pc to trace character, digits written within the air by the user and then uses a convolutional neural network to classify the character and digits into one of the possible classes. Several current systems use advanced and high-priced chase setups to realize gesture recognition, however, we tend to get to form a system that may attain similar work with a far cheaper setup.