  Air-writing is the process of writing characters or words in free space using
finger or hand movements without the aid of any hand-held device. In this work,
we address the problem of mid-air finger writing using web-cam video as input.
In spite of recent advances in object detection and tracking, accurate and
robust detection and tracking of the fingertip remains a challenging task,
primarily due to small dimension of the fingertip. Moreover, the initialization
and termination of mid-air finger writing is also challenging due to the
absence of any standard delimiting criterion. To solve these problems, we
propose a new writing hand pose detection algorithm for initialization of
air-writing using the Faster R-CNN framework for accurate hand detection
followed by hand segmentation and finally counting the number of raised fingers
based on geometrical properties of the hand. Further, we propose a robust
fingertip detection and tracking approach using a new signature function called
distance-weighted curvature entropy. Finally, a fingertip velocity-based
termination criterion is used as a delimiter to mark the completion of the
air-writing gesture. Experiments show the superiority of the proposed fingertip
detection and tracking algorithm over state-of-the-art approaches giving a mean
precision of 73.1 % while achieving real-time performance at 18.5 fps, a
condition which is of vital importance to air-writing. Character recognition
experiments give a mean accuracy of 96.11 % using the proposed air-writing
system, a result which is comparable to that of existing handwritten character
recognition systems.
