Air-writing recognition using reverse time ordered stroke context

Abstract

Air-writing is a new human and smart device communication approach, permits users to write inputs in a natural and relentless way. This touch-less way can prevent users from virus infection such as COVID-19. Compared with other methods, air writing is more challenging due to its unique characteristics such as redundant lifting strokes, multiplicity (different writing styles from various users), and confusion (different character types written in air are similar). Without the need of any starting trigger, a novel reverse time-ordered algorithm is proposed in this paper to efficiently filter out unnecessary lifting strokes, and thus simplifies the matching procedure. As to the second and third issues, a tiered arrangement structure is proposed by sampling the air-writing results with various sampling rates to solve the multiplicity and confusion problems. Analyzed with other recently proposed air writing algorithms, the proposed approach reaches satisfactory recognition accuracy (above 94%) without any starting triggers.