@InProceedings{SilvaBarrTera:1996:SaCoPA,
  author =          "da Silva, F. S. Correa and Barrera, J. and Terada,
                     R.",
  title =           "On the Sample Complexity of PAC-Learning
                     Techniques Applied to Optical Character
                     Recognition",
  booktitle =       "Conferencia Brasileira de IA '96 (SBIA)",
  year =            "1996",
  editor =          "",
  pages =           "",
  month =           "",
  note =            "This work has been supported by ProTeM-CC/CNPq
                     throught the AnIMoMat project, contract
                     680067/94-9.",
  keywords =        "pac learning, mathematical morphology",
  abstract =        "Optical Character Recognition (OCR for short) is
                     among the most important and popular problems for
                     application of Pattern Recognition techniques, and
                     many computer programs for solving it have been
                     proposed in recent years. In the present article
                     we propose the use of the well-established
                     PAC-Learning paradigm to analyse the problem of
                     OCR, and introduce a system for OCR based on this
                     paradigm. The major advantage we see on adopting
                     this paradigm for the problem of OCR is that our
                     end results are rooted on a sound and well-founded
                     mathematical theory, thus providing our results
                     with desirable robustness and explanatory power of
                     the underlying features of this particular
                     problem. In order to preserve mathematical rigour,
                     all functions for image transformation in our
                     system are expressed in terms of the theory of
                     Mathematical Morphology ( MM for short) -- a very
                     general formalism to describe lattice
                     transformations that has been used as means to
                     express image transformations. Besides its
                     ellegance and rigour, MM has been widely adopted
                     as the language to describe image transformations
                     because of its nice computational features. We
                     present the results of our analysis, together with
                     some empirical results that we have observed,
                     suggesting that PAC-Learning may indeed be an
                     appropriate tool to work out the problem of OCR.",
  entrytype =       "InProceedings",
  targetfile =      "compl.ps",
  size =            "281 Kbytes",
  version =         "original",
  repository =      "ime.usp.br/jb/1996/04.03.19.40",
  URL =             "http://hermes.dpi.inpe.br:1907/
                     rep/ime.usp.br/jb/1996/04.03.19.40",
}