Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212520" target="_blank" >RIV/68407700:21230/13:00212520 - isvavai.cz</a>

Výsledek na webu

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

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor

Popis výsledku v původním jazyce

This work deals with recognition of hand-drawn graphical symbols in diagrams. We present two contributions. First, we designed a new composite descriptor expressing overall appearance of symbols. We achieved rather favorable accuracy in classification ofsegmented symbols on benchmark databases, which is 98.93 prec. for a database of flow charts, 98.33 prec. for a database of crisis management icons, and 92.94 perc. for a database of digits. Second, we used the descriptor in the task of simultaneous segmentation and recognition of graphical symbols. Our method creates symbol candidates by grouping spatially close strokes. Symbol candidates are classified by a multiclass SVM classifier learned on a dataset with negative examples. Thus, some portion of the candidates is filtered out. The joint segmentation and classification was tested on diagrams from the flowchart database. We were able to find 91.85 prec. of symbols while generating 8.8 times more symbol candidates than is the number

Název v anglickém jazyce

Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor

Popis výsledku anglicky

This work deals with recognition of hand-drawn graphical symbols in diagrams. We present two contributions. First, we designed a new composite descriptor expressing overall appearance of symbols. We achieved rather favorable accuracy in classification ofsegmented symbols on benchmark databases, which is 98.93 prec. for a database of flow charts, 98.33 prec. for a database of crisis management icons, and 92.94 perc. for a database of digits. Second, we used the descriptor in the task of simultaneous segmentation and recognition of graphical symbols. Our method creates symbol candidates by grouping spatially close strokes. Symbol candidates are classified by a multiclass SVM classifier learned on a dataset with negative examples. Thus, some portion of the candidates is filtered out. The joint segmentation and classification was tested on diagrams from the flowchart database. We were able to find 91.85 prec. of symbols while generating 8.8 times more symbol candidates than is the number

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F10%2F0783" target="_blank" >GAP103/10/0783: Struktura a její využití při rozpoznávání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop

ISBN

978-3-200-02943-9

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

16-23

Název nakladatele

Vienna University of Technology

Místo vydání

Vienna

Místo konání akce

Hernstein

Datum konání akce

4. 2. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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