Abstract
In this paper, we illustrate the use of a novel probabilistic framework for document analysis on typical problems of document layout analysis and graphics recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, our system carries out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty.
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Vuilleumier Stückelberg, M., Doermann, D. (2000). Model-Based Graphics Recognition. In: Chhabra, A.K., Dori, D. (eds) Graphics Recognition Recent Advances. GREC 1999. Lecture Notes in Computer Science, vol 1941. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40953-X_10
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DOI: https://doi.org/10.1007/3-540-40953-X_10
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