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
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Historical Document Images, Segmentation, SLIC Superpixels, Gabor Filters, Multi-Scale Analysis, ARLSA.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resources and offering a structured access to large sets of cultural heritage documents, a raising interest to historical document image segmentation has been generated.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ensuring the identification of homogenous regions or similar groups of pixels sharing some visual characteristics from historical documents (i.e. distinguishing graphic types, segmenting graphical regions from textual ones, and discriminating text in a variety of situations of different fonts and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 Thus, we propose in this article an automatic segmentation method for historical document images based on extraction of homogeneous or sim
ilar 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 It has been evaluated on 1000 pages of historical documents and achieved interesting results.
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