Two Methods for Filled-in Document Image Identification Using Local Features

Abstract

In this work, the task of document image classification is dealt with, particularly in the case of pre-printed forms, where a large part of the document can be filled-in with the result of a potentially very different image. A method for the selection of discriminative local features is presented and tested along with two different classification algorithms. The first one is an incremental version of the method proposed in (Arlandis et al., 2009), based on similarity searching around a set anchor points, and the second one is based on a direct voting scheme ((Arlandis et al., 2011)). Experiments on a document database consisting of real office documents with a very high variability, as well as on the NIST SD6 database, are presented. A confidence measure intended to reject unknown documents (those that have not been indexed in advance as a given document class) is also proposed and tested.