@techreport{min-to-cor-lei-sto-11-fuzzhog-tr,
  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},
  number = {IC-11-10},
  note = {In English},
  pages = {15},
  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.}
}