  In this paper, we present a framework for reading analog clocks in natural
images or videos. Specifically, we make the following contributions: First, we
create a scalable pipeline for generating synthetic clocks, significantly
reducing the requirements for the labour-intensive annotations; Second, we
introduce a clock recognition architecture based on spatial transformer
networks (STN), which is trained end-to-end for clock alignment and
recognition. We show that the model trained on the proposed synthetic dataset
generalises towards real clocks with good accuracy, advocating a Sim2Real
training regime; Third, to further reduce the gap between simulation and real
data, we leverage the special property of "time", i.e.uniformity, to generate
reliable pseudo-labels on real unlabelled clock videos, and show that training
on these videos offers further improvements while still requiring zero manual
annotations. Lastly, we introduce three benchmark datasets based on COCO, Open
Images, and The Clock movie, with full annotations for time, accurate to the
minute.
