Distance Measures for Layout-Based Document Image Retrieval

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

In: International Conference on Document Image Analysis for Libraries (DIAL 2006). International Conference on Document Image Analysis for Libraries (DIAL-06) Lyon France Seiten 232-242 IEEE 4/2006.


Most methods for document image retrieval rely solely on text information to find similar documents. This paper describes a way to use layout information for document image retrieval instead. A new class of distance measures is introduced for documents with Manhattan layouts, based on a two-step procedure: First, the distances between the blocks of two layouts are calculated. Then, the blocks of one layout are assigned to the blocks of the other layout in a matching step. Different block distances and matching methods are compared and evaluated using the publicly available MARG database. On this dataset, the layout type can be determined successfully in 92.6% of the cases using the best distance measure in a nearest neighbor classifier. The experiments show that the best distance measure for this task is the overlapping area combined with the Manhattan distance of the corner points as block distance together with the minimum weight edge cover matching.

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beusekom--distance-measure-for-layout-based-document-image-retrieval--DIAL--2006.pdf (pdf, 380 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence