One of the aims of the EU project COLLATE (IST-1999- 20882 Collaboratory for annotation, indexing and retrieval of digitized historical archive material) is to design and implement a Web-based collaboratory for archives, scientists and end-users working with digitized cultural material. Since the originals of such a material are often unique and scattered in various archives, severe problems arise for their wide fruition. A solution would be to develop intelligent document processing tools that automatically transform printed documents into a webaccessible form such as XML. Here, we propose the use of a document processing system, WISDOM++, which uses heavily machine learning techniques in order to perform such a task, and report promising results obtained in preliminary experiments.
Machine Learning methods for automatically processing historical documents: from paper acquisition to XML transformation
ESPOSITO, Floriana;MALERBA, Donato;SEMERARO, Giovanni;FERILLI, Stefano;BASILE, TERESA MARIA;CECI, MICHELANGELO;DI MAURO, NICOLA
2004-01-01
Abstract
One of the aims of the EU project COLLATE (IST-1999- 20882 Collaboratory for annotation, indexing and retrieval of digitized historical archive material) is to design and implement a Web-based collaboratory for archives, scientists and end-users working with digitized cultural material. Since the originals of such a material are often unique and scattered in various archives, severe problems arise for their wide fruition. A solution would be to develop intelligent document processing tools that automatically transform printed documents into a webaccessible form such as XML. Here, we propose the use of a document processing system, WISDOM++, which uses heavily machine learning techniques in order to perform such a task, and report promising results obtained in preliminary experiments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
