Structure Extraction in Printed Documents Using Neural Approaches

Abdel Belaïd 1, * Yves Rangoni 1
* Auteur correspondant
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LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper addresses the problem of layout and logical structure extraction from image documents. Two classes of approaches are first studied and discussed in general terms: data-driven and model-driven. In the latter, some specific approaches like rule-based or formal grammar are usually studied on very stereotyped documents providing honest results, while in the former artificial neural networks are often considered for small patterns with good results. Our understanding of these techniques let us to believe that a hybrid model is a more appropriate solution for structure extraction. Based on this standpoint, we proposed a Perceptive Neural Network based approach using a static topology that possesses the characteristics of a dynamic neural network. Thanks to its transparency, it allows a better representation of the model elements and the relationships between the logical and the physical components. Furthermore, it possesses perceptive cycles providing some capacities in data refinement and correction. Tested on several kinds of documents, the results are better than those of a static Multilayer Perceptron.
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Chapitre d'ouvrage
Simone Marinai and Hiromichi Fujisawa. Machine Learning in Document Analysis and Recognition, 90, Springer, pp.21-43, 2008, Studies in Computational Intelligence, 978-3-540-76279-9
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https://hal.inria.fr/inria-00287681
Contributeur : Abdel Belaid <>
Soumis le : jeudi 12 juin 2008 - 14:47:21
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59
Document(s) archivé(s) le : vendredi 28 mai 2010 - 18:50:31

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  • HAL Id : inria-00287681, version 1

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Abdel Belaïd, Yves Rangoni. Structure Extraction in Printed Documents Using Neural Approaches. Simone Marinai and Hiromichi Fujisawa. Machine Learning in Document Analysis and Recognition, 90, Springer, pp.21-43, 2008, Studies in Computational Intelligence, 978-3-540-76279-9. 〈inria-00287681〉

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