
Proceedings Paper
Old document image segmentation using the autocorrelation function and multiresolution analysisFormat | Member Price | Non-Member Price |
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Paper Abstract
Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges
in information retrieval in digital libraries and document layout analysis. Therefore, in order to control
the quality of historical document image digitization and to meet the need of a characterization of their content
using intermediate level metadata (between image and document structure), we propose a fast automatic layout
segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation
function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The
method proposed in this article has the advantage that it is performed without any hypothesis on the document
structure, either about the document model (physical structure), or the typographical parameters (logical
structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly,
we detail our proposal to characterize the content of old documents by extracting the autocorrelation features
in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find
the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of
clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our
method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of
French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization
of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure,
which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85%
of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure
and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of
homogeneous blocks and their topology.
Paper Details
Date Published: 4 February 2013
PDF: 12 pages
Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580K (4 February 2013); doi: 10.1117/12.2002365
Published in SPIE Proceedings Vol. 8658:
Document Recognition and Retrieval XX
Richard Zanibbi; Bertrand Coüasnon, Editor(s)
PDF: 12 pages
Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580K (4 February 2013); doi: 10.1117/12.2002365
Show Author Affiliations
Maroua Mehri, L3i, Univ. of La Rochelle (France)
LITIS, Univ. of Rouen (France)
Petra Gomez-Krämer, L3i, Univ. of La Rochelle (France)
LITIS, Univ. of Rouen (France)
Petra Gomez-Krämer, L3i, Univ. of La Rochelle (France)
Pierre Héroux, LITIS, Univ. of Rouen (France)
Rémy Mullot, L3i, Univ. of La Rochelle (France)
Rémy Mullot, L3i, Univ. of La Rochelle (France)
Published in SPIE Proceedings Vol. 8658:
Document Recognition and Retrieval XX
Richard Zanibbi; Bertrand Coüasnon, Editor(s)
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