  Jersey number recognition is an important task in sports video analysis,
partly due to its importance for long-term player tracking. It can be viewed as
a variant of scene text recognition. However, there is a lack of published
attempts to apply scene text recognition models on jersey number data. Here we
introduce a novel public jersey number recognition dataset for hockey and study
how scene text recognition methods can be adapted to this problem. We address
issues of occlusions and assess the degree to which training on one sport
(hockey) can be generalized to another (soccer). For the latter, we also
consider how jersey number recognition at the single-image level can be
aggregated across frames to yield tracklet-level jersey number labels. We
demonstrate high performance on image- and tracklet-level tasks, achieving
91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models,
and data are available at https://github.com/mkoshkina/jersey-number-pipeline.
