Scribble based interactive page layout segmentation using gabor filter

Majeed Kassis, Jihad El-Sana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

This paper presents an interactive approach for fast and accurate page layout segmentation. It is a scribble-based interactive segmentation approach, where the user draws scribbles on the various regions and the system performs page layout segmentation. The user can correct and refine the resulting segmentation by drawing new scribbles. To classify the various regions of the page, we apply a bank of Gabor filters, in several orientations and multiple frequencies, to capture the orientation, the stroke width, and size of the text. These properties also implicitly encode the writing style of the document. After combining the responses of the Gabor filter into a feature matrix, we classify various regions of the document by applying graph cuts, while taking into account the user made scribbles. The presented approach is very fast, easy to use, robust to user interaction, and provides accurate results.

Original languageEnglish
Title of host publicationProceedings - 2016 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
PublisherInstitute of Electrical and Electronics Engineers
Pages13-18
Number of pages6
ISBN (Electronic)9781509009817
DOIs
StatePublished - 2 Jul 2016
Event15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016 - Shenzhen, China
Duration: 23 Oct 201626 Oct 2016

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume0
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Conference

Conference15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
Country/TerritoryChina
CityShenzhen
Period23/10/1626/10/16

Keywords

  • Gabor filter
  • Interactive system
  • Page segmentation
  • Scribble-based

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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