Example-Based Logical Labeling of Document Title Page Images

Joost van Beusekom, Daniel Keysers, Faisal Shafait, Thomas Breuel

In: 9th Int. Conf. on Document Analysis and Recognition (ICDAR-200/). International Conference on Document Analysis and Recognition (ICDAR) Seiten 919-923 2 IEEE 9/2007.


This paper presents a flexible and effective example-based approach for labeling title pages which can be used for automated extraction of bibliographic data. The labels of interest are "Title", "Author", "Abstract" and "Affiliation". The method takes a set of labeled document layouts and a single unlabeled document layout as input and finds the best matching layout in the set. The labels of this layout are used to label the new layout. The similarity measure for layouts combines structural layout similarity and textural similarity on the block-level. Experimental results yield accuracy rates from 94.8% to 99.6% obtained on the publicly available MARG dataset. This shows that our lightweight method has equivalent and partially better performance when compared to other more complex labeling methods known from the literature.

Weitere Links

beusekom--example-based-logical-labeling-of-document-title-page-images--ICDAR--2007.pdf (pdf, 133 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence