Reference TypeConference Proceedings
Citation KeyMaiaJulcHira:2018:MaLeAp
Author1 Maia, Ana Lucia Lima Marreiros
2 Julca-Aguilar, Frank Dennis
3 Hirata, Nina Sumiko Tomita
Affiliation1 University of São Paulo/State University of Feira de Santana
2 University of São Paulo
3 University of São Paulo
TitleA Machine Learning approach for Graph-based Page Segmentation
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Book TitleProceedings
DateOct. 29 - Nov. 1, 2018
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationFoz do Iguaçu, PR, Brazil
KeywordsPage segmentation, document image, machine learning, graph, connected components classification, convolutional neural network.
AbstractWe propose a new approach for segmenting a document image into its page components (e.g. text, graphics and tables). Our approach consists of two main steps. In the first step, a set of scores corresponding to the output of a convolutional neural network, one for each of the possible page component categories, is assigned to each connected component in the document. The labeled connected components define a fuzzy over-segmentation of the page. In the second step, spatially close connected components that are likely to belong to a same page component are grouped together. This is done by building an attributed region adjacency graph of the connected components and modeling the problem as an edge removal problem. Edges are then kept or removed based on a pre-trained classifier. The resulting groups, defined by the connected subgraphs, correspond to the detected page components. We evaluate our method on the ICDAR2009 dataset. Results show that our method effectively segments pages, being able to detect the nine types of page components. Furthermore, as our approach is based on simple machine learning models and graph-based techniques, it should be easily adapted to the segmentation of a variety of document types.
Tertiary TypeFull Paper
Size3626 KiB
Number of Files1
Target FileFinal_PaperID_50.pdf
Last Update2018: administrator
Metadata Last Update2020: administrator {D 2018}
Document Stagecompleted
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Content TypeExternal Contribution
Document Stagenot transferred
Next Higher Units8JMKD3MGPAW/3RPADUS
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History2018-09-02 11:29:09 :: -> administrator :: 2018
2020-02-19 03:10:44 :: administrator -> :: 2018
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Access Date2020, Nov. 29