Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP103%2F10%2F0783" target="_blank" >GAP103/10/0783: Structure and its impact for recognition</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
8
Pages from-to
67-74
Publisher name
Graz University of Technology
Place of publication
Graz
Event location
Seggau
Event date
Feb 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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