IS = { zkontrolovano 12 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  author =      {Neumann, Luk{\' a}{\v s} and Matas, Ji{\v r}{\' i}},
  title =       {Scene Text Localization and Recognition with Oriented Stroke Detection},
  year =        {2013},
  pages =       {97-104},
  booktitle =   {2013 IEEE International Conference on Computer Vision (ICCV 2013)},
  publisher =   {IEEE},
  address =     {Piscataway, US},
  isbn =        {978-1-4799-2839-2},
  issn =        {1550-5499},
  book_pages =  {3631},
  month =      {December},
  day =        {3--6},
  venue =      {Sydney, Australia},
  annote =      {An unconstrained end-to-end text localization and
                  recognition method is presented. The method
                  introduces a novel approach for character detection
                  and recognition which combines the advantages of
                  sliding-window and connected component
                  methods. Characters are detected and recognized as
                  image regions which contain strokes of specific
                  orientations in a specific relative position, where
                  the strokes are efficiently detected by convolving
                  the image gradient field with a set of oriented bar
                  filters. Additionally, a novel character
                  representation efficiently calculated from the
                  values obtained in the stroke detection phase is
                  introduced. The representation is robust to shift at
                  the stroke level, which makes it less sensitive to
                  intra-class variations and the noise induced by
                  normalizing character size and positioning. The
                  effectiveness of the representation is demonstrated
                  by the results achieved in the classification of
                  real-world characters using an euclidian
                  nearest-neighbor classifier trained on synthetic
                  data in a plain form. The method was evaluated on a
                  standard dataset, where it achieves state-of-the-art
                  results in both text localization and recognition.},
  keywords =    {Character recognition,Context,Detectors,Image segmentation,Robustness,Standards,Text recognition,photo OCR,scene text localization,scene text recognition,text-in-the-wild,unconstrained end-to-end text recognition},
  prestige =   {international},
  authorship =  {50-50},
  project =     {FP7-ICT-288587 MASELTOV,SGS13/142/OHK3/2T/13,TACR TE01020415 V3C},
  doi =         {10.1109/ICCV.2013.19},
  psurl       = {http://cmp.felk.cvut.cz/~neumalu1/neumann-iccv2013.pdf},