Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFS8CH |
Repository | sid.inpe.br/sibgrapi/2017/08.22.02.22 |
Last Update | 2017:08.22.02.22.06 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.22.02.22.06 |
Metadata Last Update | 2020:02.19.02.01.40 administrator |
Citation Key | Julca-AguilarMaiaHira:2017:TeClCo |
Title | Text/non-text classification of connected components in document images  |
Format | On-line |
Year | 2017 |
Date | Oct. 17-20, 2017 |
Access Date | 2021, Jan. 21 |
Number of Files | 1 |
Size | 1930 KiB |
Context area | |
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 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-22 02:22:06 :: nina@ime.usp.br -> administrator :: 2020-02-19 02:01:40 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/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 |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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