  Despite significant progress in optical character recognition (OCR) and
computer vision systems, robustly recognizing text and identifying people in
images taken in unconstrained \emph{in-the-wild} environments remain an ongoing
challenge. However, such obstacles must be overcome in practical applications
of vision systems, such as identifying racers in photos taken during off-road
racing events. To this end, we introduce two new challenging real-world
datasets - the off-road motorcycle Racer Number Dataset (RND) and the Muddy
Racer re-iDentification Dataset (MUDD) - to highlight the shortcomings of
current methods and drive advances in OCR and person re-identification (ReID)
under extreme conditions. These two datasets feature over 6,300 images taken
during off-road competitions which exhibit a variety of factors that undermine
even modern vision systems, namely mud, complex poses, and motion blur. We
establish benchmark performance on both datasets using state-of-the-art models.
Off-the-shelf models transfer poorly, reaching only 15% end-to-end (E2E) F1
score on text spotting, and 33% rank-1 accuracy on ReID. Fine-tuning yields
major improvements, bringing model performance to 53% F1 score for E2E text
spotting and 79% rank-1 accuracy on ReID, but still falls short of good
performance. Our analysis exposes open problems in real-world OCR and ReID that
necessitate domain-targeted techniques. With these datasets and analysis of
model limitations, we aim to foster innovations in handling real-world
conditions like mud and complex poses to drive progress in robust computer
vision. All data was sourced from PerformancePhoto.co, a website used by
professional motorsports photographers, racers, and fans. The top-performing
text spotting and ReID models are deployed on this platform to power real-time
race photo search.
