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Hearthstone Helper - Using Optical Character Recognition Techniques for Cards Detection

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

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Abstract

In this paper we address the problem of capturing, processing and analyzing images from the video stream of the Hearthstone game in order to obtain relevant information on the conduct of parties in this game. Since the information needs to be presented to the user in real-time, we needed to find the most suitable methods of extracting this information. Therefore, techniques such as background subtraction, histograms comparisons, key points matching, optical character recognition were investigated. Driven by the required processing speed, we ended up using optical character recognition on limited areas of interest from the captured image. After developing the application, we tested it in real-world context, while real games were played and presented the obtained results. In the end, we also provided two examples where the application would prove useful for better decision making during the game.

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Acknowledgement

The research presented in this paper was partially supported by the UPB-EX internal research grant provided by the University Politehnica of Bucharest.

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Authors and Affiliations

  1. University Politehnica Bucharest, Splaiul Independenţei 313, 69121, Bucharest, Romania

    Costin-Gabriel Chiru & Florin Oprea

Authors
  1. Costin-Gabriel Chiru
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  2. Florin Oprea
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Corresponding author

Correspondence to Costin-Gabriel Chiru .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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Chiru, CG., Oprea, F. (2016). Hearthstone Helper - Using Optical Character Recognition Techniques for Cards Detection. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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