About: Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	ftp://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-Prusa-Hlavac-CVWW-2013.pdf
Description	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 of segmented 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 of true symbols per diagram in average. 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 of segmented 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 of true symbols per diagram in average. (en)
Title	Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor (en)
skos:prefLabel	Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor (en)
skos:notation	RIV/68407700:21230/13:00212520!RIV14-GA0-21230___
http://linked.open...avai/predkladatel	Fakulta elektrotechnická
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GAP103/10/0783)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Hlaváč, Václav Průša, Daniel Bresler, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	105313
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00212520
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Diagram recognition; Flowchart2076-1465s; Pattern recognition; SVM (en)
http://linked.open.../riv/klicoveSlovo	Flowchart2076-1465s SVM Pattern recognition Diagram recognition
http://linked.open...ontrolniKodProRIV	[0F64FA33B127]
http://linked.open...v/mistoKonaniAkce	Hernstein
http://linked.open...i/riv/mistoVydani	Vienna
http://linked.open...i/riv/nazevZdroje	CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Structure and its impact for recognition
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Bresler, Martin Hlaváč, Václav Průša, Daniel
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-02-04 (xsd:date)
number of pages	8 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Vienna University of Technology
https://schema.org/isbn	978-3-200-02943-9
http://localhost/t...ganizacniJednotka	21230

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