Multithresholding of mixed-type documents

Strouthopoulos, C.; Papamarkos, Nikos

doi:10.1016/s0952-1976(00)00004-x

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…Strouthopoulos [11] proposed a system for multiple thresholding of grayscale documents. A survey of image thresholding techniques is discussed in detail in [12].…”

Section: Related Workmentioning

confidence: 99%

Binarization of Degraded Characters Using Tensor Voting Based Color Clustering

Madhubalan

Lee

2011

2011 IEEE 11th International Conference on Computer and Information Technology

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In this paper, a new method of binarizing degraded characters from scene images is presented. The proposed method helps in obtaining a binarized result even with multicolored characters subjected to heavy degradation. Color information is used in the proposed binarization method to help to separate the character from the image background and to obtain a clean representation of the final result. Images from the ICDAR 2003 robust character recognition database are used to compare the effectiveness and accuracy of the proposed algorithm with other methods.

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“…Strouthopoulos [11] proposed a system for multiple thresholding of grayscale documents. A survey of image thresholding techniques is discussed in detail in [12].…”

Section: Related Workmentioning

confidence: 99%

Binarization of Degraded Characters Using Tensor Voting Based Color Clustering

Madhubalan

Lee

2011

2011 IEEE 11th International Conference on Computer and Information Technology

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“…Logarithmic intensity adjustment can take several forms but typically might be expressed as: (3) I' and I are the output and input gray scale images and s is a scaling factor. This type of logarithmic intensity adjustment can produce an enhanced image as shown in Figure 3. …”

Section: Logarithmic Intensity Adjustmentmentioning

confidence: 99%

“…Thresholding is a simple but effective technique for image segmentation. A variety of thresholding methods have been developed [1][2][3] . The limitation of these techniques is that they only apply to a single-band image, such as a grayscale image or a single band of a multi-band image.…”

Section: Automatic Thresholding and Data Clusteringmentioning

confidence: 99%

Grayscale enhancement techniques of x-ray images of carry-on luggage

et al. 2003

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Very few image processing applications dealt with x-ray luggage scenes in the past. In this paper, a series of common image enhancement techniques are first applied to x-ray data and results shown and compared. A novel simple enhancement method for data de-cluttering, called image hashing, is then described. Initially, this method was applied using manually selected thresholds, where progressively de-cluttered slices were generated and displayed for screeners. Further automation of the hashing algorithm (multi-thresholding) for the selection of a single optimum slice for screener interpretation was then implemented. Most of the existing approaches for automatic multi-thresholding, data clustering, and cluster validity measures require prior knowledge of the number of thresholds or clusters, which is unknown in the case of luggage scenes, given the variety and unpredictability of the scene's content. A novel metric based on the Radon transform was developed. This algorithm finds the optimum number and values of thresholds to be used in any multi-thresholding or unsupervised clustering algorithm. A comparison between the newly developed metric and other known metrics for image clustering is performed. Clustering results from various methods demonstrate the advantages of the new approach.

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“…Based on this assumption, specialised binarisation techniques have been developed for complex document images. Strouthopoulos and Papamarkos [7] considered the problem of the grey level reduction of complex documents by using a combination of a page layout analysis technique [8] and a Kohonen seld-organised feature map (SOFM) neural network [9][10]. Such techniques are suitable for cleaning documents but do not work efficiently with degraded documents.…”

Section: Introductionmentioning

confidence: 99%

A neuro-fuzzy technique for document binarisation

Papamarkos

2003

Neural Computing & Applications

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This paper proposes a new neuro-fuzzy technique suitable for binarisation or, in general, the colour reduction of digital documents. The proposed approach uses the image colour values and additional local spatial features extracted in the neighbourhood of the pixels. Both image and local features values feed a Kohonen self-organised feature map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define a first approach of the final classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to obtain the colours of the final document. The method can be applied to greyscale and colour documents; it is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

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Multithresholding of mixed-type documents

Cited by 26 publications

References 24 publications

Binarization of Degraded Characters Using Tensor Voting Based Color Clustering

Binarization of Degraded Characters Using Tensor Voting Based Color Clustering

Grayscale enhancement techniques of x-ray images of carry-on luggage

A neuro-fuzzy technique for document binarisation

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