IS = { zkontrolovano 20 Jan 2014 },
  UPDATE  = { 2013-08-16 },
   author    = {Bresler, Martin},
   language  = {english},
   title     = {Text/Non-Text Classification of Strokes using the Composite Descroptor},
   year      = {2013},
   month     = {May},
   pages     = {1--5},
   editor    = {Husn{\'i}k, Libor},
   booktitle = {17th International Student Conference on Electrical Engineering},
   publisher = {Czech Technical University in Prague},
   location  = {Prague, Czech Republic},
   address   = {Technick{\'a} 2, Prague, Czech Republic},
   isbn      = {978-80-01-05242-6},
   book_pages= {144},
   annote    = {The task of a single stroke classification into two
     classes (text and non-text) is the subject of this work. We used
     an SVM classifier based on a descriptor created as an extension
     of the existing composite descriptor. It reflects an appearance
     and a local context of strokes. We achieved overall accuracy
     93.1% on a database of handwritten flowcharts. The
     state-of-the-arts methods have a quite poor performance on this
     database with the accuracy 86.3%. Moreover, we showed that our
     approach allows to learn classifiers favouring one class to
     obtain higher accuracy in classification of strokes in that class
     while the accuracy in the other class is decreased
     minimally. This is advantageous for filtering some portion of
     strokes in the input of specialized recognition engines.},
   keywords   = {mode detection, text/non-text classification, SVM},
   authorship = {100},
   project    = {SGS13/205/OHK3/3T/13},
   psurl      = { [Bresler-POSTER-2013.pdf] },
   www        = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-POSTER-2013.pdf},
   day        = {16},
   venue      = {Prague, Czech Republic},
   organization = {Czech Technical University in Prague, Czech Republic},
   acceptance_ratio = {0.93},