Modeling Flowchart Structure Recognition as a Max-Sum Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212145" target="_blank" >RIV/68407700:21230/13:00212145 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-Prusa-Hlavac-ICDAR-2013.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-Prusa-Hlavac-ICDAR-2013.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDAR.2013.246" target="_blank" >10.1109/ICDAR.2013.246</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling Flowchart Structure Recognition as a Max-Sum Problem

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modeling Flowchart Structure Recognition as a Max-Sum Problem

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICDAR 2013: Proceedings of the 12th International Conference on Document Analysis and Recognition

ISBN

—

ISSN

1520-5363

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1215-1219

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Washington DC

Datum konání akce

25. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—