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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00223235%21RIV15-MSM-21230___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
We present our recent model of a diagram recognition engine. It extends our previous work which approaches the structural recognition as an optimization problem of choosing the best subset of symbol candidates. The main improvement is the integration of our own text separator into the pipeline to deal with text blocks occurring in diagrams. Second improvement is splitting the symbol candidates detection into two stages: uniform symbols detection and arrows detection. Text recognition is left for postprocessing when the diagram structure is already known. Training and testing of the engine was done on a freely available benchmark database of flowcharts. We correctly segmented and recognized 93.0% of the symbols having 55.1% of the diagrams recognized without any error. Considering correct stroke labeling, we achieved the precision of 95.7%. This result is superior to the state-of-the-art method with the precision of 92.4 %. Additionally, we demonstrate the generality of the proposed method by adapting the system to finite automata domain and evaluating it on own database of such diagrams. We present our recent model of a diagram recognition engine. It extends our previous work which approaches the structural recognition as an optimization problem of choosing the best subset of symbol candidates. The main improvement is the integration of our own text separator into the pipeline to deal with text blocks occurring in diagrams. Second improvement is splitting the symbol candidates detection into two stages: uniform symbols detection and arrows detection. Text recognition is left for postprocessing when the diagram structure is already known. Training and testing of the engine was done on a freely available benchmark database of flowcharts. We correctly segmented and recognized 93.0% of the symbols having 55.1% of the diagrams recognized without any error. Considering correct stroke labeling, we achieved the precision of 95.7%. This result is superior to the state-of-the-art method with the precision of 92.4 %. Additionally, we demonstrate the generality of the proposed method by adapting the system to finite automata domain and evaluating it on own database of such diagrams.
dcterms:title
Recognition System for On-line Sketched Diagrams Recognition System for On-line Sketched Diagrams
skos:prefLabel
Recognition System for On-line Sketched Diagrams Recognition System for On-line Sketched Diagrams
skos:notation
RIV/68407700:21230/14:00223235!RIV15-MSM-21230___
n3:aktivita
n15:S n15:P
n3:aktivity
P(GAP103/10/0783), S
n3:dodaniDat
n12:2015
n3:domaciTvurceVysledku
n5:1542567 n5:8547777 n5:4705009
n3:druhVysledku
n19:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n22:predkladatel
n3:idSjednocenehoVysledku
41609
n3:idVysledku
RIV/68407700:21230/14:00223235
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Diagram recognition; Structural-Analysis; Max-Sum; Optimization; Flowcharts; Finite Automata
n3:klicoveSlovo
n4:Diagram%20recognition n4:Finite%20Automata n4:Optimization n4:Structural-Analysis n4:Flowcharts n4:Max-Sum
n3:kontrolniKodProRIV
[EFC77AB2957D]
n3:mistoKonaniAkce
Crete Island
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
ICFHR 2014: Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition
n3:obor
n11:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
5
n3:projekt
n13:GAP103%2F10%2F0783
n3:rokUplatneniVysledku
n12:2014
n3:tvurceVysledku
Van Phan, T. Hlaváč, Václav Nakagawa, M. Průša, Daniel Bresler, Martin
n3:typAkce
n21:WRD
n3:zahajeniAkce
2014-09-01+02:00
s:issn
2167-6445
s:numberOfPages
6
n17:doi
10.1109/ICFHR.2014.100
n7:hasPublisher
IEEE Computer Society Press
n14:isbn
978-1-4799-4334-0
n18:organizacniJednotka
21230