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Proceedings Paper

Detection of figure and caption pairs based on disorder measurements
Author(s): Claudie Faure; Nicole Vincent
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Paper Abstract

Figures inserted in documents mediate a kind of information for which the visual modality is more appropriate than the text. A complete understanding of a figure often necessitates the reading of its caption or to establish a relationship with the main text using a numbered figure identifier which is replicated in the caption and in the main text. A figure and its caption are closely related; they constitute single multimodal components (FC-pair) that Document Image Analysis cannot extract with text and graphics segmentation. We propose a method to go further than the graphics and text segmentation in order to extract FC-pairs without performing a full labelling of the page components. Horizontal and vertical text lines are detected in the pages. The graphics are associated with selected text lines to initiate the detector of FC-pairs. Spatial and visual disorders are introduced to define a layout model in terms of properties. It enables to cope with most of the numerous spatial arrangements of graphics and text lines. The detector of FC-pairs performs operations in order to eliminate the layout disorder and assigns a quality value to each FC-pair. The processed documents were collected in medic@, the digital historical collection of the BIUM (Bibliothèque InterUniversitaire Médicale). A first set of 98 pages constitutes the design set. Then 298 pages were collected to evaluate the system. The performances are the result of a full process, from the binarisation of the digital images to the detection of FC-pairs.

Paper Details

Date Published: 18 January 2010
PDF: 9 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340S (18 January 2010); doi: 10.1117/12.838592
Show Author Affiliations
Claudie Faure, CNRS-LTCI, TELECOM-ParisTech (France)
Nicole Vincent, LIPADE, Univ. Paris Descartes (France)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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