@techreport{TR-IC-11-10,
   number = {IC-11-10},
   author  =  {Rodrigo Minetto and Nicolas Thome and Matthieu Cord and
                   Neucimar J. Leite and Jorge Stolfi},
   title  =  {{Fuzzy  Histogram  of Oriented Gradients to characterize
                   Single Line Text Regions}},
   month = {May},
   year = {2011},
   institution  =  {Institute of Computing, University of Campinas and
                   Universite Pierre et Marie Curie},
   note = {In English, 15 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       In  this  work  we discuss the use of the histogram of oriented
       gradients  (HOG)  descriptors  as  an  effective  tool for text
       description  and  recognition. Specifically, we propose a Fuzzy
       HOG-based  texture  descriptor (F-HOG) that uses a partition of
       the  image  into  three  horizontal  cells  with fuzzy adaptive
       boundaries, to characterize single-line texts in outdoor scenes
       and  video  frames. The input of our algorithm is a rectangular
       image  presumed  to contain a single line of text in latin like
       characters.  The  output  is  a  relatively short (54-features)
       descriptor   that   provides  an  effective  input  to  an  SVM 
       classifier.  Tests  show that F-HOG is more accurate than Dalal
       and  Triggs  original  HOG-based classifier using a 54-features
       descriptor, and comparable to their best classifier (which uses
       a 108-features descriptor) while being half as long.
  }
}