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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212145%21RIV14-MSM-21230___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-Prusa-Hlavac-ICDAR-2013.pdf
dcterms:description
This work deals with the on-line recognition of hand-drawn graphical sketches with structure. We present a novel approach, in which the search for a suitable interpretation of the input is formulated as a combinatorial optimization task -- the max-sum problem. The recognition pipeline consists of two main stages. First, groups of strokes possibly representing symbols of a sketch (symbol candidates) are segmented and relations between them are detected. Second, a combination of symbol candidates best fitting the input is chosen by solving the optimization problem. We focused on flowchart recognition. Training and testing of our method was done on a freely available benchmark database. We correctly segmented and recognized 82.7 prec. of the symbols having 31.5 prec. of the diagrams recognized without any error. It indicates that our approach has promising potential and can compete with the state-of-the-art methods. This work deals with the on-line recognition of hand-drawn graphical sketches with structure. We present a novel approach, in which the search for a suitable interpretation of the input is formulated as a combinatorial optimization task -- the max-sum problem. The recognition pipeline consists of two main stages. First, groups of strokes possibly representing symbols of a sketch (symbol candidates) are segmented and relations between them are detected. Second, a combination of symbol candidates best fitting the input is chosen by solving the optimization problem. We focused on flowchart recognition. Training and testing of our method was done on a freely available benchmark database. We correctly segmented and recognized 82.7 prec. of the symbols having 31.5 prec. of the diagrams recognized without any error. It indicates that our approach has promising potential and can compete with the state-of-the-art methods.
dcterms:title
Modeling Flowchart Structure Recognition as a Max-Sum Problem Modeling Flowchart Structure Recognition as a Max-Sum Problem
skos:prefLabel
Modeling Flowchart Structure Recognition as a Max-Sum Problem Modeling Flowchart Structure Recognition as a Max-Sum Problem
skos:notation
RIV/68407700:21230/13:00212145!RIV14-MSM-21230___
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n3:aktivita
n21:S n21:P
n3:aktivity
P(7E13018), P(TE01020197), S
n3:dodaniDat
n14:2014
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n3:duvernostUdaju
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n3:entitaPredkladatele
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n3:idSjednocenehoVysledku
88838
n3:idVysledku
RIV/68407700:21230/13:00212145
n3:jazykVysledku
n19:eng
n3:klicovaSlova
Diagram recognition; Structural-Analysis; Max-Sum; Optimization; Flowcharts
n3:klicoveSlovo
n15:Structural-Analysis n15:Diagram%20recognition n15:Optimization n15:Flowcharts n15:Max-Sum
n3:kontrolniKodProRIV
[73E5EDCDAB68]
n3:mistoKonaniAkce
Washington DC
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
ICDAR 2013: Proceedings of the 12th International Conference on Document Analysis and Recognition
n3:obor
n23:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n8:7E13018 n8:TE01020197
n3:rokUplatneniVysledku
n14:2013
n3:tvurceVysledku
Hlaváč, Václav Bresler, Martin Průša, Daniel
n3:typAkce
n20:WRD
n3:zahajeniAkce
2013-08-25+02:00
s:issn
1520-5363
s:numberOfPages
5
n16:doi
10.1109/ICDAR.2013.246
n7:hasPublisher
IEEE Computer Society
n18:organizacniJednotka
21230