Abstract
Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models that can be automatically acquired by applying machine learning techniques. In particular, by properly encapsulating knowledge of the inherent spatial nature of the layout of a document image, spatial relations among logical components of interest can play a key role in the learned models. For this reason, we are investigating the application of (multi-)relational learning techniques, which successfully allows relations between components to be effectively and naturally represented. Goal of this paper is to evaluate and systematically compare two different approaches to relational learning, that is, a statistical approach and a logical approach in the task of document image understanding. For a fair comparison, both methods are tested on the same dataset consisting of multi-page articles published in an international journal. An analysis of pros and cons of both approaches is reported.
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Ceci, M., Berardi, M., Malerba, D. (2005). Relational Learning: Statistical Approach Versus Logical Approach in Document Image Understanding. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_42
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DOI: https://doi.org/10.1007/11558590_42
Publisher Name: Springer, Berlin, Heidelberg
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