Adaptive document image binarization

By Jaakko J. Sauvola, Matti Pietikäinen. Pattern Recognition, 2000

2,464 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/be97923dbcdaf8b1496b637ed156656d8874f552

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In Computer Vision and Pattern Recognition

Citations in the corpus

A framework for improved video text detection and recognition (2012, methodology): “[23], which apply a single threshold for the entire document, and adapted local thresholding algorithms [21, 27, 34], which assign a threshold for each pre-defined local region of the document.”
A New Local Adaptive Thresholding Technique in Binarization (2012, methodology): “Using this approach we are able to achieve binarization speed close to the global binarization methods with the performance as good as that of local binarization schemes of Sauvola and Pietikainen [7],[21].”
Adaptive local thresholding for detection of nuclei in diversity stained cytology images (2011, methodology): “Based on the technique used in the method proposed by Sauvola and Pietikainen for local thresholding of document images [4], we developed a method suitable for our problem.”
Adaptive Thresholding using the Integral Image (2007, methodology): “A number of adaptive thresholding methods exist [White and Rohrer 1983; Bernsen 1986; Parker 1991; Wellner 1993; Yang et al. 1994; Shen and Ip 1997; Chan et al. 1998; Savakis 1998; Sauvola and Pietikainen 2000; Yang and Yan 2000].”
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications (2006, methodology): “For the binarization of image I 2 , the locally adaptive thresholding method of Sauvola [41] was chosen.”
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction (2015, methodology): “For binarization of the interest map Sauvola’s [10] local adaptive thresholding technique is used.”
Multiscale X-ray tomography of cementitious materials: A review (2020, background): “A straightforward extension is local thresholding [174], where, for each voxel, the threshold is found by analyzing the histogram of gray-levels in a neighbor-”
Adaptive degraded document image binarization (2006, methodology): “Sauvola and Pietikainen [11] propose a method that solves this problem by adding a hypothesis on the gray values of text and background pixels (text pixels have gray values near 0 and background pixels have gray values near 255), which results in the following formula for the threshold: where R is…”
License Plate Recognition From Still Images and Video Sequences: A Survey (2008, methodology): “Anagnostopoulos et al. report a success rate of 96.5% for plate localization with proper parameterization of the method in conjunction with CCA measurements and the Sauvola binarization method [42] A novel method for position estimation and tracking of LPs in 3-”
Automatic thresholding for defect detection (2004, background): “More recent studies on this subject can be found in Sauvola and Pietikainen [ 6 ], and Yen et al. [8].”
New Local Thresholding Method for Soil Images by Minimizing Grayscale Intra‐Class Variance (2013, methodology): “Development of a Selection Criterion to Determine the Best Window Size for the Local Implementation Most of the local segmentation methods are implemented for a specific value of the window size (White and Rohrer, 1983; Niblack, 1986; Oh and Lindquist, 1999; Sauvola and Pietikäinen, 2000), which can”