Multiple Predicate Learning for Document Image Understanding

Floriana Esposito, Donato Malerba, and Francesca A. Lisi, Università degli Studi di Bari, Italy

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, whose manual specification can be a highly demanding task. In order to automatically acquire these models, we propose the application of machine learning techniques. In this paper, problems raised by possible dependencies between concepts to be learned are illustrated and solved with a computational strategy based on the separate-and-parallel-conquer search. The approach is tested on a set of real multi-page documents processed by the system WISDOM++. New results confirm the validity of the proposed strategy and show some limits of the machine learning system used in this work.

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