A Coupon Classification Method Based on Adaptive Image Vector Matching

Nagasaki, Takeshi; Marukawa, Katsumi; Kagehiro, Tatsuhiko; Suzuki, Hiroshi

doi:10.1109/icpr.2006.58

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Nagasaki et all. [5] recently worked on coupon identification by means of supervised learning. In that work, a set of classes is learned and modeled using feature vectors computed from averaged images around the most discriminant points found among coupons.…”

Section: Previous Workmentioning

confidence: 99%

Identification of Very Similar Filled-in Forms with a Reject Option

Arlandis

Pérez-Cortés

Ungria

2009

2009 10th International Conference on Document Analysis and Recognition

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In this work, a technique addressed to the reliable identification of very similar filled-in forms, with a reject option, is proposed. The method is based on the automatic detection of the most discriminant regions at the image level, to be used by a distance-based classifier. Experiments included multi-page form images and the results suggest that a very high accuracy can be achieved when identifying previously known types of forms, even when unpredictable fillin data and significant noise from the scanning process appear on the test images. Likewise, it is shown that the probability that an unknown type of document is not rejected is very low. The presented technique could also be applied to other kinds of documents.

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Section: Previous Workmentioning

confidence: 99%

Identification of Very Similar Filled-in Forms with a Reject Option

Arlandis

Pérez-Cortés

Ungria

2009

2009 10th International Conference on Document Analysis and Recognition

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“…Other works rely on using character and string codes to achieve the document identification (Sako et al, 2003), as well as, on computing pixel densities from image regions (Heroux et al, 1998). Within form-type documents, specific applications are addresed to coupons (Nagasaki et al, 2006), banking (Ogata et al, 2003) or business (Ting and Leung, 1996) form identification.…”

Section: Related Workmentioning

confidence: 99%

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

Carrion-Robles¹,

Castello-Fos

Pérez-Cortés

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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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.

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