# A General Framework for Jersey Number Recognition in Sports Video

arXiv: https://arxiv.org/abs/2405.13896 · HTML: https://mlsanity.com/paper/2405.13896/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text](https://mlsanity.com/paper/2105.05486/) (2021)
- [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://mlsanity.com/paper/2207.06966/) (2022)
- [On the Unreasonable Effectiveness of Centroids in Image Retrieval](https://mlsanity.com/paper/2104.13643/) (2021)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [Multi-task learning for jersey number recognition in Ice Hockey](https://mlsanity.com/paper/2108.07848/) (2021)
- [Ice hockey player identification via transformers and weakly supervised learning](https://mlsanity.com/paper/2111.11535/) (2021)
- [Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos](https://mlsanity.com/paper/2309.06285/) (2023)
- [Player Identification in Hockey Broadcast Videos](https://mlsanity.com/paper/2009.02429/) (2020)
- [Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data](https://mlsanity.com/paper/2203.00734/) (2022)

## Built on by (most cited first, 11 of 11)

- [Multi-Agent System for Comprehensive Soccer Understanding](https://mlsanity.com/paper/2505.03735/) (2025)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Towards long-term player tracking with graph hierarchies and domain-specific features](https://mlsanity.com/paper/2502.21242/) (2025)
- [From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction](https://mlsanity.com/paper/2504.06357/) (2025)
- [MCAD: Multimodal Context-Aware Audio Description Generation For Soccer](https://mlsanity.com/paper/2511.09448/) (2025)
- [Towards Comprehensive Basketball Understanding](https://mlsanity.com/paper/2608.23435/) (2026)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [Selective Mask Propagation for Multi-Object Tracking](https://mlsanity.com/paper/2606.13033/) (2026)
- [Prompt, Probe, Train, or Annotate? Single-camera sports video understanding in amateur settings](https://mlsanity.com/paper/2609.28049/) (2026)
- [Native Association: Confidence-Aware Human Perception in the Wild with a Foundation VLM](https://mlsanity.com/paper/2609.33450/) (2026)

## Abstract (arXiv)

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.