  Jersey number recognition (JNR) has always been an important task in sports
analytics. Improving recognition accuracy remains an ongoing challenge because
images are subject to blurring, occlusion, deformity, and low resolution.
Recent research has addressed these problems using number localization and
optical character recognition. Some approaches apply player identification
schemes to image sequences, ignoring the impact of human body rotation angles
on jersey digit identification. Accurately predicting the number of jersey
digits by using a multi-task scheme to recognize each individual digit enables
more robust results. Based on the above considerations, this paper proposes a
multi-task learning method called the angle-digit refine scheme (ADRS), which
combines human body orientation angles and digit number clues to recognize
athletic jersey numbers. Based on our experimental results, our approach
increases inference information, significantly improving prediction accuracy.
Compared to state-of-the-art methods, which can only handle a single type of
sport, the proposed method produces a more diverse and practical JNR
application. The incorporation of diverse types of team sports such as soccer,
football, basketball, volleyball, and baseball into our dataset contributes
greatly to generalized JNR in sports analytics. Our accuracy achieves 64.07% on
Top-1 and 89.97% on Top-2, with corresponding F1 scores of 67.46% and 90.64%,
respectively.
