Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset
Jacob Tyo Youngseog Chung Motolani Olarinre Zachary C. Lipton
This paper introduces the off-road motorcycle Racer number Dataset (RnD), a
new challenging dataset for optical character recognition (OCR) research. RnD
contains 2,411 images from professional motorsports photographers that depict
motorcycle racers in off-road competitions. The images exhibit a wide variety
of factors that make OCR difficult, including mud occlusions, motion blur,
non-standard fonts, glare, complex backgrounds, etc. The dataset has 5,578
manually annotated bounding boxes around visible motorcycle numbers, along with
transcribed digits and letters. Our experiments benchmark leading OCR
algorithms and reveal an end-to-end F1 score of only 0.527 on RnD, even after
fine-tuning. Analysis of performance on different occlusion types shows mud as
the primary challenge, degrading accuracy substantially compared to normal
conditions. But the models struggle with other factors including glare, blur,
shadows, and dust. Analysis exposes substantial room for improvement and
highlights failure cases of existing models. RnD represents a valuable new
benchmark to drive innovation in real-world OCR capabilities. The authors hope
the community will build upon this dataset and baseline experiments to make
progress on the open problem of robustly recognizing text in unconstrained
natural environments. The dataset is available at
https://github.com/JacobTyo/SwinTextSpotter.