Low-Level Image Features for Stamps Detection and Classification

Forczmański, Paweł; Markiewicz, Andrzej

doi:10.1007/978-3-319-00969-8_37

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Authors propose new Siamese Network with Multi-task Learning to recognize the different seals and report the relevant information in real time. Forczmański et al [14] detect and classify stamps in scanned documents. Authors apply color segmentation in order to find potential stamps.…”

Section: Object Classificationmentioning

confidence: 99%

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Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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Section: Object Classificationmentioning

confidence: 99%

“…Where ⃗ is a point of the image, r is an expansion factor, ψ is the wavelet, μ is the multi-fractal measure, and T ψ r μ is the wavelet projection. Among wavelet functions adapted to the singularity analysis, the authors select the Lorentz wavelet (equation (14)) to estimate the singularity exponents.…”

Section: Figurementioning

confidence: 99%

Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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“…It is based on classification of characteristic features, often in a scheme "one versus all". In our previous works [5,9] a similar problem of stamp detection and recognition was described in detail. It applies Hough line and circle transforms, color segmentation and heuristic techniques.…”

Section: Individual Approachmentioning

confidence: 99%

“…So-called low-level features are a result of our previous research on stamp detection and recognition [5,9]. This approach shares common features with measures proposed by Haralic et al Created feature vector contains eleven values, namely contrast, correlation, energy and homogeneity calculated in the same way as in case of GLCM matrix.…”

Section: Low-level Features (Llf)mentioning

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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“…at the decision stage. These simple properties are later concatenated in a single vector having 19 elements [34]. All of them use the following measures as common values ( 1 : minor axis length, 2 : major axis length, : object area defined by the number of pixels, and : object perimeter), which are later employed to build more complex characteristics.…”

Section: Stamps Shape Featuresmentioning

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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Low-Level Image Features for Stamps Detection and Classification

Cited by 8 publications

References 10 publications

Neural Network-based System for Automatic Passport Stamp Classification

Neural Network-based System for Automatic Passport Stamp Classification

Two-stage approach to extracting visual objects from paper documents

Stamps Detection and Classification Using Simple Features Ensemble

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