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      Logical segmentation for article extraction in digitized old newspapers

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          Abstract

          Newspapers are documents made of news item and informative articles. They are not meant to be red iteratively: the reader can pick his items in any order he fancies. Ignoring this structural property, most digitized newspaper archives only offer access by issue or at best by page to their content. We have built a digitization workflow that automatically extracts newspaper articles from images, which allows indexing and retrieval of information at the article level. Our back-end system extracts the logical structure of the page to produce the informative units: the articles. Each image is labelled at the pixel level, through a machine learning based method, then the page logical structure is constructed up from there by the detection of structuring entities such as horizontal and vertical separators, titles and text lines. This logical structure is stored in a METS wrapper associated to the ALTO file produced by the system including the OCRed text. Our front-end system provides a web high definition visualisation of images, textual indexing and retrieval facilities, searching and reading at the article level. Articles transcriptions can be collaboratively corrected, which as a consequence allows for better indexing. We are currently testing our system on the archives of the Journal de Rouen, one of France eldest local newspaper. These 250 years of publication amount to 300 000 pages of very variable image quality and layout complexity. Test year 1808 can be consulted at plair.univ-rouen.fr.

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          A Realistic Dataset for Performance Evaluation of Document Layout Analysis

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            Document Segmentation Using Pixel-Accurate Ground Truth

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              Continuous CRF with Multi-scale Quantization Feature Functions Application to Structure Extraction in Old Newspaper

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                Author and article information

                Journal
                03 October 2012
                Article
                1210.0999
                8aea2e8b-59da-4d69-aeaa-2a33c5bc469b

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                ACM Document Engineering, France (2012)
                cs.IR cs.CV cs.DL
                ccsd

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