  Comprehensive understanding of key players and actions in multiplayer sports
broadcast videos is a challenging problem. Unlike in news or finance videos,
sports videos have limited text. While both action recognition for multiplayer
sports and detection of players has seen robust research, understanding
contextual text in video frames still remains one of the most impactful avenues
of sports video understanding. In this work we study extremely accurate
semantic text detection and recognition in sports clocks, and challenges
therein. We observe unique properties of sports clocks, which makes it hard to
utilize general-purpose pre-trained detectors and recognizers, so that text can
be accurately understood to the degree of being used to align to external
knowledge. We propose a novel distant supervision technique to automatically
build sports clock datasets. Along with suitable data augmentations, combined
with any state-of-the-art text detection and recognition model architectures,
we extract extremely accurate semantic text. Finally, we share our
computational architecture pipeline to scale this system in industrial setting
and proposed a robust dataset for the same to validate our results.
