Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

  • Cristina Madalina Noaica
  • Robert Badea
  • Iulia Maria Motoc
  • Claudiu Gheorghe Ghica
  • Alin Cristian Rosoiu
  • Nicolaie Popescu-Bodorin
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)


This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.


crisp human perception fuzzy artificial perception perceptron 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristina Madalina Noaica
    • 1
  • Robert Badea
    • 1
  • Iulia Maria Motoc
    • 1
  • Claudiu Gheorghe Ghica
    • 1
    • 2
  • Alin Cristian Rosoiu
    • 3
  • Nicolaie Popescu-Bodorin
    • 1
  1. 1.Artificial Intelligence & Computational Logic Laboratory, Mathematics & Computer Science Dept.Spiru Haret UniversityBucharestRomania
  2. 2.Clintelica ABStockholmSweden
  3. 3.Game Tester at UbiSoftBucharestRomania

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