View in EDS

Complex documents images segmentation based on steerable pyramid features.

Saved in:
Bibliographic Details
Title: Complex documents images segmentation based on steerable pyramid features.
Authors: Benjelil, Mohamed1,2 benjlaiel@yahoo.fr, Kanoun, Slim1 slim.kanoun@yahoo.fr, Mullot, Rémy2 remy.mullot@univ-lr.fr, Alimi, Adel M.1 adel.alimi@ieee.org
Source: International Journal on Document Analysis & Recognition. Sep2010, Vol. 13 Issue 3, p209-228. 20p. 10 Diagrams, 7 Charts, 3 Graphs.
Subjects: Document selection, Visual communication, Graphology, Logos (Symbols), Document imaging systems
Abstract: Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images. [ABSTRACT FROM AUTHOR]
Copyright of International Journal on Document Analysis & Recognition is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 53465711
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Complex documents images segmentation based on steerable pyramid features.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Benjelil%2C+Mohamed%22">Benjelil, Mohamed</searchLink><relatesTo>1,2</relatesTo><i> benjlaiel@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Kanoun%2C+Slim%22">Kanoun, Slim</searchLink><relatesTo>1</relatesTo><i> slim.kanoun@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Mullot%2C+Rémy%22">Mullot, Rémy</searchLink><relatesTo>2</relatesTo><i> remy.mullot@univ-lr.fr</i><br /><searchLink fieldCode="AR" term="%22Alimi%2C+Adel+M%2E%22">Alimi, Adel M.</searchLink><relatesTo>1</relatesTo><i> adel.alimi@ieee.org</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+on+Document+Analysis+%26+Recognition%22">International Journal on Document Analysis & Recognition</searchLink>. Sep2010, Vol. 13 Issue 3, p209-228. 20p. 10 Diagrams, 7 Charts, 3 Graphs.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Document+selection%22">Document selection</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+communication%22">Visual communication</searchLink><br /><searchLink fieldCode="DE" term="%22Graphology%22">Graphology</searchLink><br /><searchLink fieldCode="DE" term="%22Logos+%28Symbols%29%22">Logos (Symbols)</searchLink><br /><searchLink fieldCode="DE" term="%22Document+imaging+systems%22">Document imaging systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal on Document Analysis & Recognition is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=53465711
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10032-010-0113-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 209
    Subjects:
      – SubjectFull: Document selection
        Type: general
      – SubjectFull: Visual communication
        Type: general
      – SubjectFull: Graphology
        Type: general
      – SubjectFull: Logos (Symbols)
        Type: general
      – SubjectFull: Document imaging systems
        Type: general
    Titles:
      – TitleFull: Complex documents images segmentation based on steerable pyramid features.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Benjelil, Mohamed
      – PersonEntity:
          Name:
            NameFull: Kanoun, Slim
      – PersonEntity:
          Name:
            NameFull: Mullot, Rémy
      – PersonEntity:
          Name:
            NameFull: Alimi, Adel M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2010
              Type: published
              Y: 2010
          Identifiers:
            – Type: issn-print
              Value: 14332833
          Numbering:
            – Type: volume
              Value: 13
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: International Journal on Document Analysis & Recognition
              Type: main
ResultId 1