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dc.contributor.authorJiřinec, Jakub
dc.contributor.authorRot, David
dc.contributor.authorJiřinec, Stanislav
dc.date.accessioned2019-11-11T11:00:22Z-
dc.date.available2019-11-11T11:00:22Z-
dc.date.issued2019
dc.identifier.citationMARTÍNEK, J., LENC, L., KRÁL, P. Training Strategies for OCR Systems for Historical Documents. In: Artificial Intelligence Applications and Innovation. Cham: Springer, 2019. s. 362-373. ISBN 978-3-030-19822-0, ISSN 1868-4238.en
dc.identifier.isbn978-3-030-19822-0
dc.identifier.uri2-s2.0-85065915057
dc.identifier.urihttp://hdl.handle.net/11025/35864
dc.format12 s.
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesArtificial Intelligence Applications and Innovationen
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© Springeren
dc.titleOptimization of the energy consumption of a building with PVen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper presents an overview of training strategies for optical character recognition of historical documents. The main issue is the lack of the annotated data and its quality. We summarize several ways of synthetic data preparation. The main goal of this paper is to show and compare possibilities how to train a convolutional recurrent neural network classifier using the synthetic data and its combination with a real annotated dataset.en
dc.subject.translatedoptimizationen
dc.subject.translatedenergy consuptionen
dc.subject.translatedphotovoltaic system (PV)en
dc.subject.translatedmeasuring systemen
dc.subject.translatedregulation systemen
dc.subject.translatedprogrammable logic controller (PLC)en
dc.subject.translatedheat pumpen
dc.type.statusPeer-revieweden
dc.identifier.obd43926993
dc.project.IDSGS-2019-018/Zpracov heterogenn dat a jejich specializovanplikacecs
Appears in Collections:Konferenční příspěvky / Conference papers (NTIS)
Konferenční příspěvky / Conference Papers (KIV)
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