Recognition System for On-line Sketched Diagrams

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223235" target="_blank" >RIV/68407700:21230/14:00223235 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICFHR.2014.100" target="_blank" >http://dx.doi.org/10.1109/ICFHR.2014.100</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICFHR.2014.100" target="_blank" >10.1109/ICFHR.2014.100</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recognition System for On-line Sketched Diagrams

Popis výsledku v původním jazyce

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 ofour 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 met

Název v anglickém jazyce

Recognition System for On-line Sketched Diagrams

Popis výsledku anglicky

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 ofour 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 met

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

ICFHR 2014: Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition

ISBN

978-1-4799-4334-0

ISSN

2167-6445

e-ISSN

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Počet stran výsledku

6

Strana od-do

563-568

Název nakladatele

IEEE Computer Society Press

Místo vydání

Los Alamitos

Místo konání akce

Crete Island

Datum konání akce

1. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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