Abstract
Document image analysis represents one of the most relevant topics in the field of image processing: many research efforts have been devoted to devising automatic strategies for document region classification. In this paper, we present a peculiar strategy to extract numerical features from segmented image regions, and their employment for classification purposes by means of the KERNEL system, a particular neuro-fuzzy framework suitable for application in predictive tasks. The knowledge discovery process performed by KERNEL proved to be effective in solving the problem of distinguishing between textual and graphical components of a document image. The information embedded into sample data is organised in form of a fuzzy rule base, which results to be accurate and comprehensible for human users.
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Castiello, C., Górecki, P., Caponetti, L. (2006). Neuro-fuzzy Analysis of Document Images by the KERNEL System. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_46
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DOI: https://doi.org/10.1007/11676935_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
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