Marcel Brun,  Junior Barrera,  Nina S. T. Hirata,  Nestor W. Trepode,  Daniel Dantas,  Routo Terada. 

This paper recalls the idea of classification trees in OCR (Optical Character Recognition) systems and proposes a technique for the automatic design of these classification trees. The design of both the classification trees and of the classification operators are based on training from sample pairs of observed-ideal images, allowing the development of customized OCRs.

http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/banon/2002/11.29.09.53

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