  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.
