# Training dataset generation for bridge game registration

arXiv: https://arxiv.org/abs/2109.11861 · HTML: https://mlsanity.com/paper/2109.11861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)

## Built on by (most cited first, 1 of 1)

- [TCG-AR: Real-Time Multi-View Augmented Reality for Trading Card Game Streaming](https://mlsanity.com/paper/2607.02090/) (2026)

## Abstract (arXiv)

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.