An adaptive water flow model for binarization of degraded document images

Valizadeh, Morteza; Kabir, Ehsanollah

doi:10.1007/s10032-012-0182-z

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…To prove the effectiveness of our method for uneven lighting images with strong noise injection, experimental [23], and Sauvola's method [24]; related works for the local methods, Bradley's method [29], Chou' s method [35], and Valizadeh's method [31]; based on an improved water flow model-based method, Wei's method [36]; and several related global two-dimensional methods with space information, the two-dimensional Otsu method (2DOtsu) [13] proposed by Liu et al, the fuzzy c-means clustering algorithm with nonlocal spatial information (FCM_NLS) [16] proposed by Zhao et al, and the twodimensional weak fuzzy partition entropy-based method (2DWFPE) [14] proposed FCM_NLS, which have relative good performance in anti-noise interference, cannot extract some "rice" objects in the dark regions. 2DWFPE [14] is our previously proposed method which maximizes weak fuzzy partition entropy on the two-dimensional histogram to obtain optimum segmentation results.…”

Section: Performance Of Segmentation Of Uneven Lighting Images With Nmentioning

confidence: 99%

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A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Fan

2017

EURASIP J. Adv. Signal Process.

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Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.

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Section: Performance Of Segmentation Of Uneven Lighting Images With Nmentioning

confidence: 99%

“…Lastly, they apply a global thresholding algorithm to find the text regions. Moreover, M. Valizadeh and E. Kabir in [31] improve the water flow model and propose an adaptive method to segment degraded document images.…”

Section: Introductionmentioning

confidence: 99%

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Fan

2017

EURASIP J. Adv. Signal Process.

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“…This technique provides more robustness to changes in illumination. A number of adaptive thresholding methods are reported in the literature [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive–interpolative binarization with stroke preservation for restoration of faint characters in degraded documents

Bag

Bhowmick

2015

Journal of Visual Communication and Image Representation

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“…In general, global binarization methods are efficient when they are applied to documents with uniform illumination. However, degraded documents, including stains, and uneven contrast, are not well processed by global methods [12]. To overcome this problem, local methods are used to binarize degraded documents.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid binarization method based on Kmeans

Soua

Kachouri

Akil

2014

2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP)

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International audienceThe document binarization is a fundamental processing step toward Optical Character Recognition (OCR). It aims to separate the foreground text from the document background. In this article, we propose a novel binarization technique combining local and global approaches using the clustering algorithm Kmeans. The proposed Hybrid Binarization, based on Kmeans (HBK), performs a robust binarization on scanned documents. According to several experiments, we demonstrate that the HBK method improves the binarization quality while minimizing the amount of distortion. Moreover, it out-performs several well-known state of the art methods in the OCR evaluation

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An adaptive water flow model for binarization of degraded document images

Cited by 19 publications

References 27 publications

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Adaptive–interpolative binarization with stroke preservation for restoration of faint characters in degraded documents

A new hybrid binarization method based on Kmeans

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