@inproceedings{Rathousky-TextGram-VISAPP08,
  IS = { zkontrolovano 31 Dec 2009 },
  UPDATE  = { 2009-12-31 },
  author    = {Rathousk{\'y}, Jan and Urban, Martin and Franc, Vojt{\v e}ch},
  title     = {Recognition of Text with Known Geometric and Grammatical Structure},
  booktitle = {3rd International Conference on Computer Vision Theory
               and Applications (VISAPP)},
  year      = {2008},
  venue     = {Funchal, Madeira - Portugal},
  month     = {January},
  day       = {22-25},
  pages     = {194-199},
  publisher = {INSTICC Press},
  address   = {Setubal, Portugal},
  isbn      = {978-989-8111-21-0},
  book_pages = {693},
  annote = {The optical character recognition (OCR) model is a
    fundamental part of each automated text processing system. The OCR
    model translates an input image with a text line into a string of
    symbols. In many applications (e.g. license plate recognition) the
    text has some a priori known geometric and grammatical
    structure. This article proposes an OCR method exploiting this
    knowledge which restricts the set of possible strings to a limited
    set of feasible combinations.  The recognition task is formulated
    as maximization of a similarity function which uses character
    templates as reference. These templates are estimated by a support
    vector machine method from a set of examples. In contrast to the
    common approach, the proposed method performs character
    segmentation and recognition simultaneously. The method was
    successfully evaluated in a car plate recognition system.},
  keywords = {license plate recognition, structured classification, 
              optical character recognition},
  editor    = {Alpesh Ranchordas and Helder Ara{\'u}jo},
  psurl = {[Rathousky-TextGram-VISAPP08.pdf]},
}