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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFS8CH
Repositorysid.inpe.br/sibgrapi/2017/08.22.02.22
Last Update2017:08.22.02.22.06 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.22.02.22.06
Metadata Last Update2022:06.14.00.09.03 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.66
Citation KeyJulca-AguilarMaiaHira:2017:TeClCo
TitleText/non-text classification of connected components in document images
FormatOn-line
Year2017
Access Date2025, July 03
Number of Files1
Size1930 KiB
2. Context
Author1 Julca-Aguilar, Frank Dennis
2 Maia, Ana Lucia Lima Marreiros
3 Hirata, Nina Sumiko Tomita
Affiliation1 University of São Paulo
2 State University of Feira de Santana, University of São Paulo
3 University of São Paulo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressnina@ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-22 02:22:06 :: nina@ime.usp.br -> administrator ::
2022-06-14 00:09:03 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordstext segmentation
connected component
convolutional neural network
AbstractText segmentation is an important problem in document analysis related applications. We address the problem of classifying connected components of a document image as text or non-text. Inspired from previous works in the literature, besides common size and shape related features extracted from the components, we also consider component images, without and with context information, as inputs of the classifiers. Muli-layer perceptrons and convolutional neural networks are used to classify the components. High precision and recall is obtained with respect to both text and non-text components.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Text/non-text classification of...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Text/non-text classification of...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFS8CH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFS8CH
Languageen
Target FilePID4960469.pdf
User Groupnina@ime.usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 38
sid.inpe.br/sibgrapi/2022/06.10.21.49 5
sid.inpe.br/banon/2001/03.30.15.38.24 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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