IS = { zkontrolovano 26 Jun 2015 },
  UPDATE  = { 2015-01-27 },
  author =      {Bresler, Martin and Pr{\accent23 u}{\v s}a, Daniel
                  and Hlav{\'a}{\v c}, V{\' a}clav},
  affiliation =	{13133-13133-13133},
  title =       {Detection of Arrows in On-line Sketched Diagrams
                  using Relative Stroke Positioning},
  year =        {2015},
  pages =       {610-617},
  booktitle =   {WACV 2015: IEEE Winter Conference on Applications of
                  Computer Vision},
  publisher =   {{IEEE} Computer Society},
  address =     {10662 Los Vaqueros Circle, Los Alamitos, USA},
  editor = 		{},
  book_pages =  {1200},
  isbn =        {978-1-4799-6683-7},
  month =       {January},
  day =         {6-9},
  venue =       {Waikoloa Beach, Hawaii, USA},
  annote =      {This paper deals with recognition of arrows in online
                  sketched diagrams. Arrows have varying appearance
                  and thus it is a difficult task to recognize them
                  directly. It is beneficial to detect arrows after
                  other symbols (easier to detect) are already
                  found. We proposed [4] an arrow detector which
                  searches for arrows as arbitrarily shaped connectors
                  between already found symbols. The detection is done
                  two steps: a) a search for a shaft of the arrow, b)
                  a search for its head. The first step is relatively
                  easy. However, it might be quite difficult to find
                  the head reliably. This paper brings two
                  contributions. The first contribution is a design of
                  an arrow recognizer where the head is detected using
                  relative strokes positioning. We embedded this
                  recognizer into the diagram recognition pipeline
                  proposed earlier [4] and increased the overall
                  accuracy. The second contribution is an introduction
                  of a new approach to evaluate the relative position
                  of two given strokes with neural networks
                  (LSTM). This approach is an alternative to the fuzzy
                  relative positioning proposed by Bouteruche et
                  al. [2]. We made a comparison between the two
                  methods through experiments performed on two
                  datasets for two different tasks. First, we used a
                  benchmark database of hand-drawn finite automata to
                  evaluate detection of arrows. Second, we used a
                  database presented in the paper by Bouteruche et
                  al. containing pairs of reference and argument
                  strokes, where argument strokes are classified into
                  18 classes. Our method gave significantly better
                  results for the first task and comparable results
                  for the second task.},
  keywords =    {Relative positioning, Symbols recognition, LSTM
                  neural networks, Diagram recognition},
  prestige =    {international},
  project =     {SGS13/205/OHK3/3T/13, GACR P103/10/0783, TACR TE01020197},
  psurl = { [Bresler-Prusa-Hlavac-WACV-2015.pdf] },
  doi = {10.1109/WACV.2015.87},
  www = {http://wacv2015.org/},
  authorship = {75-20-5},