@InProceedings{Bresler-Phan-Prusa-Nakagawa-Hlavac-ICFHR-2014,
  IS = { zkontrolovano 02 Jan 2015 },
  UPDATE  = { 2014-10-27 },
  author =      {Bresler, Martin and Van Phan, Truyen and Pr{\accent23 u}{\v s}a, Daniel and Nakagawa, Masaki and Hlav{\'a}{\v c}, V{\' a}clav},
  affiliation =	{13133-NULL-13133-NULL-13133},
  title =       {Recognition System for On-line Sketched Diagrams},
  year =        {2014},
  pages =       {563-568},
  booktitle =   {ICFHR 2014: Proceedings of the 14th International
                 Conference on Frontiers in Handwriting Recognition},
  publisher =   {{IEEE} Computer Society},
  address =     {10662 Los Vaqueros Circle, Los Alamitos, USA},
  editor =      {Guerrero, Juan E.},
  book_pages =  {824},
  isbn =        {978-1-4799-4334-0},
  issn = 		{2167-6445},
  month =       {September},
  day =         {1-4},
  venue =       {Hersonissos, Crete Island, Greece},
  annote =      {We present our recent model of a diagram recognition
                  engine. It extends our previous work which
                  approaches the structural recognition as an
                  optimization problem of choosing the best subset of
                  symbol candidates. The main improvement is the
                  integration of our own text separator into the
                  pipeline to deal with text blocks occurring in
                  diagrams. Second improvement is splitting the symbol
                  candidates detection into two stages: uniform
                  symbols detection and arrows detection. Text
                  recognition is left for postprocessing when the
                  diagram structure is already known. Training and
                  testing of the engine was done on a freely available
                  benchmark database of flowcharts. We correctly
                  segmented and recognized 93.0% of the symbols having
                  55.1% of the diagrams recognized without any
                  error. Considering correct stroke labeling, we
                  achieved the precision of 95.7%. This result is
                  superior to the state-of-the-art method with the
                  precision of 92.4 %. Additionally, we demonstrate
                  the generality of the proposed method by adapting
                  the system to finite automata domain and evaluating
                  it on own database of such diagrams.},
  keywords =    {Diagram recognition, Structural-Analysis, Max-Sum, Optimization, Flowcharts, Finite Automata},
  prestige =    {international},
  project =     {SGS13/205/OHK3/3T/13, GACR P103/10/0783, JSPS (B)24300095},
  psurl = { [Bresler-Phan-Prusa-Nakagawa-Hlavac-ICFHR-2014.pdf] },
  doi = {10.1109/ICFHR.2014.100},
  www = {http://www.icfhr2014.org/},
  authorship = {60-20-10-5-5},
}