Robust Stamps Detection and Classification by Means of General Shape Analysis

Forczmański, Paweł; Frejlichowski, Dariusz

doi:10.1007/978-3-642-15910-7_41

Cited by 25 publications

(12 citation statements)

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“…Concerning stamp detection, or localization of stamps in document images, a reliable general solution has also not been given yet [6]. As prior knowledge of the structure (shape or color) of the stamp is helpful to localize it in images, previous research has been focused on detection of stamps of a particular type.…”

Section: Related Workmentioning

confidence: 99%

“…Examples are [7][8][9], that use color clustering in the RGB color space to detect stamps of a specific color. Forczmański et al [6] presented a method that uses color clustering in the Y C b C r color space to detect red and blue stamps. (2) Shape information approaches: these approaches assume that the stamp has a known, fixed shape, e.g.…”

Section: Related Workmentioning

confidence: 99%

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Stamp Verification for Automated Document Authentication

Micenková

Beusekom

2015

Computational Forensics

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Abstract. Stamps, along with signatures, can be considered as the most widely used extrinsic security feature in paper documents. In contrast to signatures, however, for stamps little work has been done to automatically verify their authenticity. In this paper, an approach for verification of color stamps is presented. We focus on photocopied stamps as nongenuine stamps. Our previously presented stamp detection method is improved and extended to verify that the stamp is genuine and not a copy. Using a variety of features, a classifier is trained that allows successful separation between genuine stamps and copied stamps. Sensitivity and specificity of up to 95% could be obtained on a data set that is publicly available.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Stamp Verification for Automated Document Authentication

Micenková

Beusekom

2015

Computational Forensics

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“…These features are based, in most cases, on color or edge information. The algorithms proposed in [3,6] use color representation. Candidate areas are extracted using the projection of and matrices onto both axes.…”

Section: Existing Approachesmentioning

confidence: 99%

“…The literature survey shows that algorithms of stamp detection and recognition employ different features of digital images. Most of the methods can be classified as ones using shape information [4,5] or ones based on color features [3,6]. There are only a few algorithms that join features from those domains (e.g., [7]).…”

Section: Introductionmentioning

confidence: 99%

Stamps Detection and Classification Using Simple Features Ensemble

Forczmański

Markiewicz

2015

Mathematical Problems in Engineering

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The paper addresses a problem of detection and classification of rubber stamp instances in scanned documents. A variety of methods from the field of image processing, pattern recognition, and some heuristic are utilized. Presented method works on typical stamps of different colors and shapes. For color images, color space transformation is applied in order to find potential color stamps. Monochrome stamps are detected through shape specific algorithms. Following feature extraction stage, identified candidates are subjected to classification task using a set of shape descriptors. Selected elementary properties form an ensemble of features which is rotation, scale, and translation invariant; hence this approach is document size and orientation independent. We perform two-tier classification in order to discriminate between stamps and no-stamps and then classify stamps in terms of their shape. The experiments carried out on a considerable set of real documents gathered from the Internet showed high potential of the proposed method.

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“…It could be an integral part of any content-based image retrieval system, or simply a filter that would select only documents containing specific elements [4], segregate them in terms of importance (colored documents containing stamps and signatures are more valuable than monochromatic ones, which suggest a copy [5,10]), etc. Presented approach is document-type independent; hence, it can be applied to any formal documents, diploma, newspapers, postcards, envelopes, bank checks, etc.…”

Section: Introductionmentioning

confidence: 99%

Two-stage approach to extracting visual objects from paper documents

Forczmański

Markiewicz

2016

Machine Vision and Applications

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In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haarlike features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machinelearning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

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Robust Stamps Detection and Classification by Means of General Shape Analysis

Cited by 25 publications

References 10 publications

Stamp Verification for Automated Document Authentication

Stamp Verification for Automated Document Authentication

Stamps Detection and Classification Using Simple Features Ensemble

Two-stage approach to extracting visual objects from paper documents

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