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Improving recognition accuracy on structured documents by learning structural patterns

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Abstract

In this paper, we present a probabilistic method that can improve the efficiency of document classification when applied to structured documents. The analysis of the structure of a document is the starting point of document classification. Our method is designed to augment other classification schemes and complement pre-filtering information extraction procedures to reduce uncertainties. To this end, a probabilistic distribution on the structure of XML documents is introduced. We show how to parameterise existing learning methods to describe the structure distribution efficiently. The learned distribution is then used to predict the classes of unseen documents. Novelty detection making use of the structure-based distribution function is also discussed. Demonstration on model documents and on Internet XML documents are presented.

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Acknowledgements

This work was supported by the Hungarian National Science Foundation (Grant OTKA 32487) and by EOARD (Grant F61775–00-WE065). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the European Office of Aerospace Research and Development, Air Force Office of Scientific Research or the Air Force Research Laboratory.

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Authors and Affiliations

  1. Department of Information Systems, Eötvös University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary

    Gy. Hévízi, T. Marcinkovics & A. Lőrincz

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  1. Gy. Hévízi
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  2. T. Marcinkovics
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  3. A. Lőrincz
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Correspondence to A. Lőrincz.

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Hévízi, G., Marcinkovics, T. & Lőrincz, A. Improving recognition accuracy on structured documents by learning structural patterns. Pattern Anal Applic 7, 66–76 (2004). https://doi.org/10.1007/s10044-004-0208-3

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  • DOI: https://doi.org/10.1007/s10044-004-0208-3

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