Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
This work addresses the integration of learning and perception in AI systems, but it is incremental as it builds on existing perceptron methods without major breakthroughs.
The paper tackles the problem of modeling learning and perception together by proposing artificial perceptions as a mirror of human perceptions in a computational space, using perceptrons, and applies this to Optical Character Recognition and Iris Recognition, finding that artificial perceptions are fuzzy while human ones are crisp.
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.