1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3PFS8CH |
Repository | sid.inpe.br/sibgrapi/2017/08.22.02.22 |
Last Update | 2017:08.22.02.22.06 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2017/08.22.02.22.06 |
Metadata Last Update | 2022:06.14.00.09.03 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2017.66 |
Citation Key | Julca-AguilarMaiaHira:2017:TeClCo |
Title | Text/non-text classification of connected components in document images |
Format | On-line |
Year | 2017 |
Access Date | 2024, Apr. 28 |
Number of Files | 1 |
Size | 1930 KiB |
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2. Context | |
Author | 1 Julca-Aguilar, Frank Dennis 2 Maia, Ana Lucia Lima Marreiros 3 Hirata, Nina Sumiko Tomita |
Affiliation | 1 University of São Paulo 2 State University of Feira de Santana, University of São Paulo 3 University of São Paulo |
Editor | Torchelsen, 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 Address | nina@ime.usp.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ, Brazil |
Date | 17-20 Oct. 2017 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2017-08-22 02:22:06 :: nina@ime.usp.br -> administrator :: 2022-06-14 00:09:03 :: administrator -> :: 2017 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | text segmentation connected component convolutional neural network |
Abstract | Text 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 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Text/non-text classification of... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Text/non-text classification of... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3PFS8CH |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFS8CH |
Language | en |
Target File | PID4960469.pdf |
User Group | nina@ime.usp.br |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2017/09.12.13.04 7 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy 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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