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Proceedings Paper

Ground truth model, tool, and dataset for layout analysis of historical documents
Author(s): Kai Chen; Mathias Seuret; Hao Wei; Marcus Liwicki; Jean Hennebert; Rolf Ingold
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Paper Abstract

In this paper, we propose a new dataset and a ground-truthing methodology for layout analysis of historical documents with complex layouts. The dataset is based on a generic model for ground-truth presentation of the complex layout structure of historical documents. For the purpose of extracting uniformly the document contents, our model defines five types of regions of interest: page, text block, text line, decoration, and comment. Unconstrained polygons are used to outline the regions. A performance metric is proposed in order to evaluate various page segmentation methods based on this model. We have analysed four state-of-the-art ground-truthing tools: TRUVIZ, GEDI, WebGT, and Aletheia. From this analysis, we conceptualized and developed Divadia, a new tool that overcomes some of the drawbacks of these tools, targeting the simplicity and the efficiency of the layout ground truthing process on historical document images. With Divadia, we have created a new public dataset. This dataset contains 120 pages from three historical document image collections of different styles and is made freely available to the scientific community for historical document layout analysis research.

Paper Details

Date Published: 8 February 2015
PDF: 10 pages
Proc. SPIE 9402, Document Recognition and Retrieval XXII, 940204 (8 February 2015); doi: 10.1117/12.2075858
Show Author Affiliations
Kai Chen, Univ. de Fribourg (Switzerland)
Mathias Seuret, Univ. de Fribourg (Switzerland)
Hao Wei, Univ. de Fribourg (Switzerland)
Marcus Liwicki, Univ. of Fribourg (Switzerland)
Jean Hennebert, Univ. de Fribourg (Switzerland)
Haute Ecole Spécialisée de Suisse Occidentale (Switzerland)
Rolf Ingold, Univ. de Fribourg (Switzerland)

Published in SPIE Proceedings Vol. 9402:
Document Recognition and Retrieval XXII
Eric K. Ringger; Bart Lamiroy, Editor(s)

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