Poker Vision: Playing Cards and Chips Identification Based on Image Processing

  • Paulo Martins
  • Luís Paulo Reis
  • Luís Teófilo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)


This paper presents an approach to the identification of playing cards and counting of chips in a poker game environment, using an entry-level webcam and computer vision methodologies. Most of the previous works on playing cards identification rely on optimal camera position and controlled environment. The presented approach is intended to suit a real and uncontrolled environment along with its constraints. The recognition of playing cards lies on template matching, while the counting of chips is based on colour segmentation combined with the Hough Circles Transform. With the proposed approach it is possible to identify the cards and chips in the table correctly. The overall accuracy of the rank identification achieved is around 94%.


Poker playing cards identification image processing template matching colour segmentation chips counting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Billings, D.: Algorithms and Assessment in Computer Poker (2006)Google Scholar
  2. 2.
    Felix, D., Reis, L.P.: An Experimental Approach to Online Opponent Modeling in Texas Hold’em Poker. In: Zaverucha, G., da Costa, A.L. (eds.) SBIA 2008. LNCS (LNAI), vol. 5249, pp. 83–92. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Van, G., Kurt, B., Ramon, D.J.: Monte-Carlo Tree Search in Poker using Expected Reward Distributions. In: 1st Asian Conference on Machine Learning: Advances in Machine Learning, Nanjing, China, pp. 367–381 (2009)Google Scholar
  4. 4.
    Sklansky, D.: The Theory of Poker: A Professional Poker Player Teaches You How to Think Like One. Two Plus Two (2002)Google Scholar
  5. 5.
    Zheng, C., Green, R.: Playing Card Recognition Using Rotational Invariant Template Matching. University of Canterbury, Christchurch (2007)Google Scholar
  6. 6.
    Zutis, K., Hoey, J.: Who’s Counting?: Real-Time Blackjack Monitoring for Card Counting Detection. University of Dundee (2009)Google Scholar
  7. 7.
    Hollinger, G., Ward, N.: Introducing Computers to Blackjack: Implementation of a Card Recognition System using Computer Vision Techniques. Colby College, Waterville (2003)Google Scholar
  8. 8.
    Chen, W.-Y., Chung, C.-H.: Robust poker image recognition scheme in playing card machine using Hotelling transform, DCT and run-length techniques. In: Digital Signal Processing, 3rd edn., vol. 20, pp. 769–779 (May 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paulo Martins
    • 1
  • Luís Paulo Reis
    • 2
  • Luís Teófilo
    • 2
  1. 1.DEEC Electrical Engineering DepartmentFaculdade de Engenharia da Universidade do PortoPortoPortugal
  2. 2.LIACC Artificial Intelligence and Computer Science Lab.Faculdade de Engenharia da Universidade do PortoPortoPortugal

Personalised recommendations