Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00230204" target="_blank" >RIV/68407700:21230/15:00230204 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System

Popis výsledku v původním jazyce

Symbol segmentation is a critical part of handwriting recognition. Any mistake done in this step is propagating further through the recognition pipeline. It forces researchers to consider methods generating multiple hypotheses for symbol segmentation-over-segmentation. Simple approaches which takes all reasonable combinations of strokes are applied very often, because they allow to achieve high recall rates very easily. However, they generate too much hypotheses. It makes a recognizer considerably slow.This paper presents our experimentation with an alternative method based on a single linkage agglomerative clustering of strokes with trainable distance metric. We embed the method into the state-of-the-art recognizer for on-line sketched diagrams. We show that it results in a decrease in the number of generated hypotheses while still reaching high recall rates. A problem emerges, since the number of bad hypotheses is still significantly higher than the number of symbols and it leads to

Název v anglickém jazyce

Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System

Popis výsledku anglicky

Symbol segmentation is a critical part of handwriting recognition. Any mistake done in this step is propagating further through the recognition pipeline. It forces researchers to consider methods generating multiple hypotheses for symbol segmentation-over-segmentation. Simple approaches which takes all reasonable combinations of strokes are applied very often, because they allow to achieve high recall rates very easily. However, they generate too much hypotheses. It makes a recognizer considerably slow.This paper presents our experimentation with an alternative method based on a single linkage agglomerative clustering of strokes with trainable distance metric. We embed the method into the state-of-the-art recognizer for on-line sketched diagrams. We show that it results in a decrease in the number of generated hypotheses while still reaching high recall rates. A problem emerges, since the number of bad hypotheses is still significantly higher than the number of symbols and it leads to

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

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

CVWW 2015: Proceedings of the 20th Computer Vision Winter Workshop

ISBN

978-3-85125-388-7

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

67-74

Název nakladatele

Graz University of Technology

Místo vydání

Graz

Místo konání akce

Seggau

Datum konání akce

9. 2. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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