Adaptive document binarization

Sauvola, J.; Seppänen, Tapio; Haapakoski, S.; Pietikäinen, Matti

doi:10.1109/icdar.1997.619831

Cited by 191 publications

(132 citation statements)

References 5 publications

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“…For example, in documents with uniform noise, the global binarisation techniques of Otsu [1] and FCM [6] give satisfactory binarisation results and therefore must be selected. For document images, with non-uniform background having shadows and different types of illuminations, the local binarisation techniques of Bernsen [7], Niblack [11] and Sauvola and Pietikainen [14,15] lead to better binarisation results. Especially in more complex document images and in order to exploit structural characteristics of the characters, the ALLT [19] and IIFA [21] must be included to the system.…”

Section: System Descriptionmentioning

confidence: 99%

“…† Sauvola and Pietikainen [14,15] † an improvement version of ALLT [19] † an improvement version of IIFA [21] The selection of the above seven IBTs has been made due to their complementarity. That is, in most of the cases, the simultaneous application of all IBT and their combination with the KSOM leads to satisfactory results.…”

Section: System Descriptionmentioning

confidence: 99%

“…This is important, especially for the fuzzy pixels of the documents, that is for the pixels that cannot be easily classified. The techniques that are incorporated in the proposed system are the following: Otsu [1], FCM [6], Bernsen [7], Niblack [11], Sauvola and Pietikainen [14,15] and improvement versions of ALLT [19] and IIFA [21]. It is noticed that, in most of the cases, the simultaneous application of all binarisation techniques leads to satisfactory results due to their complementarity.…”

Section: Introductionmentioning

confidence: 99%

“…The best technique, in this test, is LLT. Recently, Sezgin and Sankur [26] compare 40 binarisation techniques and they conclude that the local based techniques of Sauvola and Pietikainen [14,15] as well as the technique of White and Rohrer (IIFA) [21] are the best performing document binarisation techniques. Except the above techniques, we include in the proposed system the powerful global thresholding technique of Otsu [1] and the fuzzy C-mean (FCM) technique [6].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques estimate a different threshold for each pixel according to the grey-scale information of the neighbouring pixels. The techniques of Bernsen [7], Chow and Kaneko [8], Eikvil [9], Mardia and Hainsworth [10], Niblack [11], Taxt [12], Yanowitz and Bruckstein [13] and Sauvola and Pietikainen [14,15] belong to this category. The hybrid techniques, which combine information of global and local thresholds belong to another category.…”

Section: Introductionmentioning

confidence: 99%

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Document binarisation using Kohonen SOM

Badekas

Papamarkos

2007

IET Image Process.

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An integrated system for the binarisation of normal and degraded printed documents for the purpose of visualisation and recognition of text characters is proposed. In degraded documents, where considerable background noise or variation in contrast and illumination exists, there are many pixels that cannot be easily classified as foreground or background pixels. For this reason, it is necessary to perform document binarisation by combining and taking into account the results of a set of binarisation techniques, especially for document pixels that have high vagueness. The proposed binarisation technique takes advantages of the benefits of a set of selected binarisation algorithms by combining their results using a Kohonen selforganising map neural network. In order to improve further the binarisation results, significant improvements are proposed for two of the most powerful document binarisation techniques used, that is for the adaptive logical level technique and for the improvement of integrated function algorithm. The proposed binarisation technique is extensively tested with a variety of degraded documents. Several experimental and comparative results, demonstrating the performance of the proposed technique, are presented.

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Section: System Descriptionmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Document binarisation using Kohonen SOM

Badekas

Papamarkos

2007

IET Image Process.

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Automation‐assisted cervical cancer screening in manual liquid‐based cytology with hematoxylin and eosin staining

et al. 2013

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Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a costefficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 lm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. V C 2013 International Society for Advancement of Cytometry

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Adaptive Fuzzy Text Segmentation in Images with Complex Backgrounds Using Color and Texture

Gllavata

Freisleben

2005

Computer Analysis of Images and Patterns

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Abstract. Textual information present in images can help to achieve the aim of automatic content based annotation and retrieval of images. In this paper, we address the problem of text segmentation (TS) in images with complex background for recognition purposes. The proposed TS method takes as input the localized text and proceeds as follows: First, the number of initial clusters is determined by analyzing the colors of the image. Second, the image pixels are clustered using the number of clusters defined in the first step. The compactness of the clusters is evaluated in each step and improved iteratively to avoid possible oversegmentation of characters. Finally, an algorithm based on a rating scheme is proposed to determine the cluster where the text pixels are classified. The proposed method is evaluated on the basis of recognition results instead of visual segmentation results. Comparative experimental results using a test set of 2684 characters are reported.

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Adaptive document binarization

Cited by 191 publications

References 5 publications

Document binarisation using Kohonen SOM

Document binarisation using Kohonen SOM

Automation‐assisted cervical cancer screening in manual liquid‐based cytology with hematoxylin and eosin staining

Adaptive Fuzzy Text Segmentation in Images with Complex Backgrounds Using Color and Texture

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