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Authors: Maroua Mehri 1 ; Pierre Héroux 2 ; Nabil Sliti 3 ; Petra Gomez-Krämer 4 ; Najoua Essoukri Ben Amara 3 and Rémy Mullot 2

Affiliations: 1 University of La Rochelle and University of Rouen, France ; 2 University of Rouen, France ; 3 University of Sousse, Tunisia ; 4 University of La Rochelle, France

ISBN: 978-989-758-091-8

ISSN: 2184-4321

Keyword(s): Historical Document Images, Segmentation, SLIC Superpixels, Gabor Filters, Multi-Scale Analysis, ARLSA.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Imaging for Cultural Heritage (Modeling/Simulation, Virtual Restoration) ; Segmentation and Grouping

Abstract: To reach the objective of ensuring the indexing and retrieval of digitized resources and offering a structured access to large sets of cultural heritage documents, a raising interest to historical document image segmentation has been generated. In fact, there is a real need for automatic algorithms ensuring the identification of homogenous regions or similar groups of pixels sharing some visual characteristics from historical documents (i.e. distinguishing graphic types, segmenting graphical regions from textual ones, and discriminating text in a variety of situations of different fonts and scales). Indeed, determining graphic regions can help to segment and analyze the graphical part in historical heritage, while finding text zones can be used as a pre-processing stage for character recognition, text line extraction, handwriting recognition, etc. Thus, we propose in this article an automatic segmentation method for historical document images based on extraction of homogeneous or simi lar content regions. The proposed algorithm is based on using simple linear iterative clustering (SLIC) superpixels, Gabor filters, multi-scale analysis, majority voting technique, connected component analysis, color layer separation, and an adaptive run-length smoothing algorithm (ARLSA). It has been evaluated on 1000 pages of historical documents and achieved interesting results. (More)


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Paper citation in several formats:
Mehri, M.; Héroux, P.; Sliti, N.; Gomez-Krämer, P.; Ben Amara, N. and Mullot, R. (2015). Extraction of Homogeneous Regions in Historical Document Images.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, ISSN 2184-4321, pages 47-54. DOI: 10.5220/0005265500470054

author={Mehri, M. and Pierre Héroux. and Nabil Sliti. and Petra Gomez{-}Krämer. and Najoua Essoukri Ben Amara. and Rémy Mullot.},
title={Extraction of Homogeneous Regions in Historical Document Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},


JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Extraction of Homogeneous Regions in Historical Document Images
SN - 978-989-758-091-8
AU - Mehri, M.
AU - Héroux, P.
AU - Sliti, N.
AU - Gomez-Krämer, P.
AU - Ben Amara, N.
AU - Mullot, R.
PY - 2015
SP - 47
EP - 54
DO - 10.5220/0005265500470054

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