&lt;title&gt;Segmentation of envelope address blocks through fractal-based approach&lt;/title&gt;

Eiterer, L. F.; Facon, Jacques; Menoti, David

doi:10.1117/12.591646

Cited by 1 publication

(5 citation statements)

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“…In the context of this work, the digital image I x is a gray level image where each pixel x i is codified by 8 bits [0, .. , 255] (1). A binary segmented image is formed by an array of pixels codified by only two values: {0} or {255} (2). Thus, we can define:…”

Section: The Learning-based Approach 21 Mathematical Definitionsmentioning

confidence: 99%

“…The testes presented here were carried out using the database used by [2] and [7]. This database is composed of 200 complex postal envelope images, with no fixed position for the handwritten address blocks, postmarks and stamps.…”

Section: Postal Envelope Databasementioning

confidence: 99%

“…Table 3 gives the average success accuracy considering pixel by pixel for the address block, stamps, rubber stamps and the noise left in the background. Also, a comparison between this results and prior approaches over the same database is shown (2-D histogram approach [11], best solution of the fractal dimension approach [2] and wavelet approach [7]). Success rates achieved for address block, stamps, rubber stamps and noise suggest that the features used in the proposed learning-based approach improves the segmentation results.…”

Section: Approach Evaluationmentioning

confidence: 99%

“…Eiterer et al [2] propose an address block segmentation approach based on fractal dimension. After computing the local fractal dimension using overlapping square windows of length r, a clustering technique based on K-means is used to label pixels into semantic objects.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we have extended an early brain resonance magnetic image (RMI), signature and fingerprint image segmentation approach [4,5] by modifying and optimizing the best set of feature selection and optimizing the best choice of k parameter into Nearest Neighbor strategy. Also, the success rate of this learning-based approach is compared to other known methods over the same database [2,7,11].…”

Section: Introductionmentioning

confidence: 99%

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Postal Envelope Segmentation using Learning-Based Approach

Legal-Ayala¹,

Facon²,

Barán³

2008

CLEIej

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This paper presents a learning-based approach to segment postal address blocks where the learning step uses only one pair of images (a sample image and its ideal segmented solution). First, this approach learns the available knowledge among pixels (each gray level) in an input image and its corresponding output in the ideal segmented solution. A classification array is generated which is re-utilized during the segmentation of new images. Features are extracted and updated by means of an adaptive square neighborhood. At the moment of new image segmentation, the submitted images are segmented by means of a k-Nearest Neighbor (k-NN) algorithm that seeks, for each pixel, the best solution in the classification array. Tests on a database of 200 complex envelope images were performed and a pixel to pixel accuracy measure validates the new approach. Results compared to other approaches for the same database show the efficiency and performance of the proposed learning-based approach. Success rates achieved for address block, stamps, rubber stamps and noise suggest that the features used in the proposed approach improves the segmentation results.

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Section: The Learning-based Approach 21 Mathematical Definitionsmentioning

confidence: 99%

Section: Postal Envelope Databasementioning

confidence: 99%