IS = { zkontrolovano 09 Jan 2012 },
  UPDATE  = { 2012-01-09 },
author = {Neumann, Luk{\' \a}{\v s} and Matas, Ji{\v r}{\' \i}},
booktitle = {Document Analysis and Recognition (ICDAR), 2011 International Conference on},
title = {Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search},
year = {2011},
month = {September},
day = {18-21},
pages = {687--691},
book_pages = {1534},
publisher = {IEEE Computer Society},
keywords = {ICDAR dataset, character detector, error compensation, grouping stage,
  maximally stable extremal regions, pruned exhaustive search, real-world images,
  region topology, text localizati on, text recognition, word text lines, 
  object recognition, text analysis},
doi = {10.1109/ICDAR.2011.144},
issn = {1520-5363},
isbn = {978-1-4577-1350-7},
prestige = {international},
project = {FP7-ICT-247022 MASH, MSM6840770038, SGS11/125/OHK3/2T/13},
authorship = {50-50},
annote = {An efficient method for text localization and recognition in
  real-world images is proposed. Thanks to effective pruning, it is
  able to exhaustively search the space of all character sequences in
  real time (200ms on a 640x480 image). The method exploits
  higher-order properties of text such as word text lines. We
  demonstrate that the grouping stage plays a key role in the text
  localization performance and that a robust and precise grouping
  stage is able to compensate errors of the character detector. The
  method includes a novel selector of Maximally Stable Extremal
  Regions (MSER) which exploits region topology. Experimental
  validation shows that 95.7% characters in the ICDAR dataset are
  detected using the novel selector of MSERs with a low sensitivity
  threshold. The proposed method was evaluated on the standard ICDAR
  2003 dataset where it achieved state-of-the-art results in both text
  localization and recognition.},
address = {Los Alamitos, United States},
venue = {Beijing, China},