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.
CITATION STYLE
Stuckelberg, M. V., & Doermann, D. (2000). Model-based graphics recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1941, pp. 121–132). Springer Verlag. https://doi.org/10.1007/3-540-40953-x_10
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