Optical Character Recognition is much more than character classification. An industrial OCR application combines
algorithms studied in detail by different researchers in the area of image processing, pattern recognition, machine
learning, language analysis, document understanding, data mining, and other, artificial intelligence domains. There is no
single perfect algorithm for any of the OCR problems, so modern systems try to adapt themselves to the actual features
of the image or document to be recognized. This paper describes the architecture of a modern OCR system with an
emphasis on this adaptation process.
In this paper we focus on some features frequently employed in Optical Character Recognition (OCR) especially in algorithms based on contour analysis. These features are of a topological and/or geometric kind somehow describing the characters. The results of an experiment to examine the identification and separation power of some features are summarized.