Textual Information Localization and Retrieval in Document Images Based on Quadtree Decomposition (original) (raw)

Abstract

Textual information extraction is a challenging issue in Information Retrieval. Two main approaches are commonly distinguished: texture-based and region-based. In this paper, we propose a method guided by the quadtree decomposition. The principle of the method is to recursively decompose regions of a document image is four equal regions, starting from the image of the whole document. At each step of the decomposition process an OCR engine is used for retrieving a given textual information from the obtained regions. Experiments on real invoice data provide promising results.

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Authors and Affiliations

1. EA2525-LIM, University of Reunion Island, Ile de La Reunion, Saint Denis, France
   Cynthia Pitou & Jean Diatta

Authors

1. Cynthia Pitou
2. Jean Diatta

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Correspondence toCynthia Pitou .

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Editors and Affiliations

1. Jacobs University Bremen , Bremen, Germany
   Adalbert F.X. Wilhelm
2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany
   Hans A. Kestler

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Pitou, C., Diatta, J. (2016). Textual Information Localization and Retrieval in Document Images Based on Quadtree Decomposition. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1\_6

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• DOI: https://doi.org/10.1007/978-3-319-25226-1\_6
• Published: 04 August 2016
• Publisher Name: Springer, Cham
• Print ISBN: 978-3-319-25224-7
• Online ISBN: 978-3-319-25226-1
• eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)Springer Nature Proceedings excluding Computer Science

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