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Textual Information Localization and Retrieval in Document Images Based on Quadtree Decomposition

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Analysis of Large and Complex Data

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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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Author information

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
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  2. Jean Diatta
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Corresponding author

Correspondence to Cynthia Pitou .

Editor information

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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