Structural Mixtures for Statistical Layout Analysis

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

In: Proceedings of the 8th IAPR International Workshop on Document Analysis Systems. IAPR International Workshop on Document Analysis Systems (DAS-2008), September 16-19, Nara, Japan, IEEE, 2008.


A key limitation of current layout analysis methods is that they rely on many hard-coded assumptions about doc- ument layouts and can not adapt to new layouts for which the underlying assumptions are not satisfied. Another ma- jor drawback of these approaches is that they do not return confidence scores for their outputs. These problems pose major challenges in large scale digitization efforts where a large number of different layouts need to be handled and manual inspection of the results on each individual page is not feasible. This paper presents a novel statistical ap- proach to layout analysis that aims at solving the above- mentioned problems for Manhattan layouts. The presented approach models known page layouts as a structural mix- ture model. A probabilistic matching algorithm is presented that gives multiple interpretations of input layout with asso- ciated probabilities. First experiments on documents from the publicly available MARG dataset achieved below 5% error rate for geometric layout analysis.


Weitere Links

2008-IUPR-07Aug_0828.pdf (pdf, 2 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence