Comparison of some thresholding algorithms for text/background segmentation in difficult document images

Leedham, Graham; Chen, Yan; Takru, K.; Tan, Joie Hadi Nata; Li, Mian

doi:10.1109/icdar.2003.1227784

Cited by 108 publications

(56 citation statements)

References 7 publications

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“…Yanowitz and Bruckstein suggested using the grey-level values at high gradient regions as known data to interpolate the threshold surface of image document texture features [14].The key steps of this method are:…”

Section: Yanowitz and Bruckstein's Methodsmentioning

confidence: 99%

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Image Segmentation By Using Thresholding Techniques For Medical Images

Senthilkumaran¹,

Vaithegi²

2016

CSEIJ

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Section: Yanowitz and Bruckstein's Methodsmentioning

confidence: 99%

“…The locally adaptive binarization method is used in gray scale images with low contrast, Varity of background intensity and presence of noise. Niblack's method was found for better thresholding in gray scale image, but still it has been modified for fine and better result [4].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation By Using Thresholding Techniques For Medical Images

Senthilkumaran¹,

Vaithegi²

2016

CSEIJ

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“…Through the similarity to document im- age analysis we use the top hat transformation to plain the background followed by a global threshold produced by Otsu's method to segment the strokes from the background. The method showed best result in [7]. Morphological operations remove artifacts from the background and inside the strokes.…”

Section: Segmentationmentioning

confidence: 99%

Texture Analysis for Stroke Classification in Infrared Reflectogramms

Lettner

Sablatnig

2005

Image Analysis

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Abstract. The recognition of painted strokes is an important step in analyzing underdrawings in infrared reflectogramms. But even for art experts, it is difficult to recognize all drawing tools and materials used for the creation of the strokes. Thus the use of computer-aided imaging technologies brings a new and objective analysis and assists the art experts. This work proposes a method to recognize strokes drawn by different drawing tools and materials. The method uses texture analysis algorithms performing along the drawing trace to distinguish between different types of strokes. The benefit of this method is the increased content of textural information within the stroke and simultaneously in the border region. We tested our algorithms on a set of six different types of strokes: 3 classes of fluid and 3 classes of dry drawing materials.

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“…It is obvious that differ-ent values of the PS lead to different binarization results, which means that there is not a set of proper PS values for all types of document images. This has already proved by many evaluations that have been made so far [24][25][26][27]. Therefore, for every technique, to achieve the best binarization result the proper PS values must initially be estimated.…”

Section: Introductionmentioning

confidence: 99%