Optimal Blackjack Strategy Recommender: A Comprehensive Study on
  Computer Vision Integration for Enhanced Gameplay
Krishnanshu Gupta Devon Bolt Ben Hinchliff
This research project investigates the application of several computer vision
techniques for playing card detection and recognition in the context of the
popular casino game, blackjack. The primary objective is to develop a robust
system that is capable of detecting and accurately classifying playing cards in
real-time, and displaying the optimal move recommendation based on the given
image of the current game. The proposed methodology involves using K-Means for
image segmentation, card reprojection and feature extraction, training of the
KNN classifier using a labeled dataset, and integration of the detection system
into a Blackjack Basic Strategy recommendation algorithm. Further, the study
aims to observe the effectiveness of this approach in detecting various card
designs under different lighting conditions and occlusions. Overall, the
project examines the potential benefits of incorporating computer vision
techniques, with a specific focus on card detection, into commonly played games
aiming to enhance player decision-making and optimize strategic outcomes. The
results obtained from our experimental evaluations with models developed under
considerable time constraints, highlight the potential for practical
implementation in real-world casino environments and across other similarly
structured games.