Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

TL;DR

This paper explores the concept of artificial perception in OCR and iris recognition, emphasizing the interdependence of perception and learning, and demonstrating that artificial perceptions are inherently fuzzy compared to human perceptions.

Contribution

It introduces a perception-based framework for analyzing OCR and iris recognition, highlighting the fuzzy nature of artificial perceptions versus human perceptions.

Findings

Artificial perceptions are fuzzy in OCR and iris recognition.

Human perceptions of characters and irides are crisp.

Perception and learning are interconnected in artificial systems.

Abstract

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.