  In this paper, we introduce a new benchmark dataset for the challenging
writing in the air (WiTA) task -- an elaborate task bridging vision and NLP.
WiTA implements an intuitive and natural writing method with finger movement
for human-computer interaction (HCI). Our WiTA dataset will facilitate the
development of data-driven WiTA systems which thus far have displayed
unsatisfactory performance -- due to lack of dataset as well as traditional
statistical models they have adopted. Our dataset consists of five sub-datasets
in two languages (Korean and English) and amounts to 209,926 video instances
from 122 participants. We capture finger movement for WiTA with RGB cameras to
ensure wide accessibility and cost-efficiency. Next, we propose spatio-temporal
residual network architectures inspired by 3D ResNet. These models perform
unconstrained text recognition from finger movement, guarantee a real-time
operation by processing 435 and 697 decoding frames-per-second for Korean and
English, respectively, and will serve as an evaluation standard. Our dataset
and the source codes are available at https://github.com/Uehwan/WiTA.
