Binarization method based on local contrast enhancement

被引：1

作者：

Lu D. ^{[1
]}

Huang X. ^{[1
]}

Liu C. ^{[1
]}

Lin X. ^{[1
]}

Zhang H. ^{[1
]}

Yan J. ^{[1
]}

机构：

[1] School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2017年 / 39卷 / 01期

关键词：

Binarization; Image processing; Local contrast enhancement; Local threshold; Quadtree;

D O I：

10.11999/JEIT160197

中图分类号：

学科分类号：

摘要：

Binarization for degraded document images is a difficult point in image processing. This paper presents a new binarization method for the degraded document images by analyzing the differences of image grayscale contrast in different areas. Firstly, theory of quadtree is used to divide areas adaptively. Secondly, various contrast enhancements are selected to adjust local grayscale contrast for different contrast areas. Lastly, the frequency of gray value is utilized to calculate threshold. The proposed algorithm is tested on random shooting degraded images and datasets of Document Image Binarization COntest (DIBCO). Compared with other four classical algorithms, the binaried images using the proposed algorithm gain the highest F-measure and PSNR (Peak Signal-to-Noise Ratio). © 2017, Science Press. All right reserved.

引用

页码：240 / 244

页数：4

共 12 条

[1]

Otsu N., A threshold selection method from gray level histograms, IEEE Transactions on Systems, Man and Cybernetics, 9, 1, pp. 62-66, (1979)

[2]

Shen X., Long J., Chen H., Et al., Otsu thresholding algorithm based on rebuilding and dimension reduction of the 3-dimensional histogram, Acta Electronica Sinica, 39, 5, pp. 1108-1114, (2011)

[3]

Niblack W., An Introduction to Digital Image Processing, pp. 115-116, (1986)

[4]

Sauvola J., Pietikainen M., Adaptive document image binarization, Pattern Recognit, 33, 2, pp. 225-236, (2000)

[5]

Ma L., Staunton R.C., A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns, Pattern Recognition, 40, 11, pp. 3005-3011, (2007)

[6]

Chou C.H., Lin W.H., Chang F., A binarization method with learning-build rules for document images produced by cameras, Pattern Recognition, 43, 4, pp. 1518-1530, (2010)

[7]

Long J., Shen X., Zang H., Et al., An adaptive thresholding algorithm by background estimation in Gaussian scale space, Acta Automatica Sinica, 40, 8, pp. 1773-1782, (2014)

[8]

Singh B.M., Sharma R., Ghosh D., Et al., Adaptive binarization of severely degraded and non-uniformly illuminated documents, International Journal of Document Analysis and Recognition, 17, 4, pp. 393-412, (2014)

[9]

Mesquita R.G., Mello C.A.B., Almeida L.H.E.V., A new thresholding algorithm for document images based on the perception of objects by distance, Integrated Computer-Aided Engineering, 21, 2, pp. 133-146, (2014)

[10]

Milyaev S., Barinova O., Novikova T., Et al., Fast and accurate scene text understanding with image binarization and off-the-shelf OCR, International Journal of Document Analysis and Recognition, 18, 2, pp. 169-182, (2015)

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