@InProceedings{Neumann-CVPR12,
  IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-10-01 },
  author =      {Neumann, Luk{\' a}{\v s} and Matas, Ji{\v r}{\' i}},
  title =       {Real-time scene text localization and recognition},
  year =        {2012},
  pages =       {3538--3545},
  booktitle =   {CVPR 2012: Proceedings of the 2012 IEEE Computer Society
                 Conference on Computer Vision and Pattern Recognition},
  publisher =   {IEEE Computer Society Press},
  address =     {Los Alamitos, USA},
  isbn =        {978-1-4673-1228-8},
  isbn =        {1063-6919},
  book_pages =  {3696},
  month =       {June},
  day =         {16--21},
  venue =       {Providence, USA},
  annote =      {An end-to-end real-time scene text localization and
    recognition method is presented. The real-time performance is
    achieved by posing the character detection problem as an efficient
    sequential selection from the set of Extremal Regions (ERs). The
    ER detector is robust to blur, illumination, color and texture
    variation and handles low-contrast text. In the first
    classification stage, the probability of each ER being a character
    is estimated using novel features calculated with O(1) complexity
    per region tested. Only ERs with locally maximal probability are
    selected for the second stage, where the classification is
    improved using more computationally expensive features. A highly
    efficient exhaustive search with feedback loops is then applied to
    group ERs into words and to select the most probable character
    segmentation. Finally, text is recognized in an OCR stage trained
    using synthetic fonts. The method was evaluated on two public
    datasets. On the ICDAR 2011 dataset, the method achieves
    state-of-the-art text localization results amongst published
    methods and it is the first one to report results for end-to-end
    text recognition. On the more challenging Street View Text
    dataset, the method achieves state-of-the-art recall. The
    robustness of the proposed method against noise and low contrast
    of characters is demonstrated by false positives caused by
    detected watermark text in the dataset.},
  keywords =    {text localization, text recognition},
  prestige =    {international},
  authorship =  {50-50},
  project =     {GACR P103/12/G084 },
  doi =         {10.1109/CVPR.2012.6248097},
  psurl =       {http://cmp.felk.cvut.cz/~neumalu1/neumann-cvpr2012.pdf},
  ut_isi =      {000309166203089},
}