nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Binarization of Degraded Document Images with Generalized Gaussian Distribution

verfasst von : Robert Krupiński, Piotr Lech, Mateusz Tecław, Krzysztof Okarma

Erschienen in: Computational Science – ICCS 2019

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

One of the most crucial steps of preprocessing of document images subjected to further text recognition is their binarization, which influences significantly obtained OCR results. Since for degrades images, particularly historical documents, classical global and local thresholding methods may be inappropriate, a challenging task of their binarization is still up-to-date. In the paper a novel approach to the use of Generalized Gaussian Distribution for this purpose is presented. Assuming the presence of distortions, which may be modelled using the Gaussian noise distribution, in historical document images, a significant similarity of their histograms to those obtained for binary images corrupted by Gaussian noise may be observed. Therefore, extracting the parameters of Generalized Gaussian Distribution, distortions may be modelled and removed, enhancing the quality of input data for further thresholding and text recognition. Due to relatively long processing time, its shortening using the Monte Carlo method is proposed as well. The presented algorithm has been verified using well-known DIBCO datasets leading to very promising binarization results.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor

Nächstes Kapitel Nonlinear Dimensionality Reduction in Texture Classification: Is Manifold Learning Better Than PCA?

Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007). https://doi.org/10.1080/2151237X.2007.10129236CrossRef

Clarke, R.J.: Transform Coding of Images. Academic Press, New York (1985)

Feng, M.L., Tan, Y.P.: Adaptive binarization method for document image analysis. In: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME), vol. 1, pp. 339–342 (2004). https://doi.org/10.1109/ICME.2004.1394198

Krupiński, R.: Approximated fast estimator for the shape parameter of generalized Gaussian distribution for a small sample size. Bull. Polish Acad. Sci. Tech. Sci. 63(2), 405–411 (2015). https://doi.org/10.1515/bpasts-2015-0046MathSciNetCrossRef

Krupiński, R.: Reconstructed quantized coefficients modeled with generalized Gaussian distribution with exponent 1/3. Image Process. Commun. 21(4), 5–12 (2016)CrossRef

Krupiński, R.: Modeling quantized coefficients with generalized Gaussian distribution with exponent 1/m, \(m=2,3,\ldots \). In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds.) ICMMI 2017. AISC, vol. 659, pp. 228–237. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67792-7_23CrossRef

Krupiński, R.: Generating augmented quaternion random variable with Generalized Gaussian Distribution. IEEE Access 6, 34608–34615 (2018). https://doi.org/10.1109/ACCESS.2018.2848202CrossRef

Lavu, S., Choi, H., Baraniuk, R.: Estimation-quantization geometry coding using normal meshes. In: Proceedings of the Data Compression Conference (DCC 2003), p. 362, March 2003. https://doi.org/10.1109/DCC.2003.1194027

Lech, P., Okarma, K.: Optimization of the fast image binarization method based on the Monte Carlo approach. Elektronika Ir Elektrotechnika 20(4), 63–66 (2014). https://doi.org/10.5755/j01.eee.20.4.6887CrossRef

10.

Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004). https://doi.org/10.1109/LSP.2003.821748CrossRef

11.

Mitianoudis, N., Papamarkos, N.: Document image binarization using local features and Gaussian mixture modeling. Image Vis. Comput. 38, 33–51 (2015). https://doi.org/10.1016/j.imavis.2015.04.003CrossRef

12.

Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)

13.

Novey, M., Adali, T., Roy, A.: A complex Generalized Gaussian Distribution - characterization, generation, and estimation. IEEE Trans. Signal Process. 58(3), 1427–1433 (2010). https://doi.org/10.1109/TSP.2009.2036049MathSciNetCrossRefMATH

14.

Ntirogiannis, K., Gatos, B., Pratikakis, I.: Performance evaluation methodology for historical document image binarization. IEEE Trans. Image Process. 22(2), 595–609 (2013). https://doi.org/10.1109/TIP.2012.2219550MathSciNetCrossRefMATH

15.

Okarma, K., Lech, P.: Monte Carlo based algorithm for fast preliminary video analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69384-0_84CrossRef

16.

Okarma, K., Lech, P.: Fast statistical image binarization of colour images for the recognition of the QR codes. Elektronika Ir Elektrotechnika 21(3), 58–61 (2015). https://doi.org/10.5755/j01.eee.21.3.10397CrossRef

17.

Olver, F.W.J.: Asymptotics and Special Functions. Academic Press, New York (1974)MATH

18.

Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076CrossRef

19.

Pascal, F., Bombrun, L., Tourneret, J.Y., Berthoumieu, Y.: Parameter estimation for multivariate Generalized Gaussian Distributions. IEEE Trans. Signal Process. 61(23), 5960–5971 (2013). https://doi.org/10.1109/TSP.2013.2282909MathSciNetCrossRefMATH

20.

Pratikakis, I., Zagori, K., Kaddas, P., Gatos, B.: ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018). In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 489–493, August 2018. https://doi.org/10.1109/ICFHR-2018.2018.00091

21.

Roenko, A.A., Lukin, V.V., Djurović, I., Simeunović, M.: Estimation of parameters for generalized Gaussian distribution. In: 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), pp. 376–379, May 2014. https://doi.org/10.1109/ISCCSP.2014.6877892

22.

Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000). https://doi.org/10.1016/S0031-3203(99)00055-2CrossRef

23.

Saxena, L.P.: Niblack’s binarization method and its modifications to real-time applications: a review. Artif. Intell. Rev. 1–33 (2017). https://doi.org/10.1007/s10462-017-9574-2CrossRef

24.

Tensmeyer, C., Martinez, T.: Document image binarization with fully convolutional neural networks. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, 9–15 November 2017, pp. 99–104. IEEE (2017). https://doi.org/10.1109/ICDAR.2017.25

25.

Wang, C.: Research of image segmentation algorithm based on wavelet transform. In: IEEE International Conference on Computer and Communications (ICCC), pp. 156–160, October 2015. https://doi.org/10.1109/CompComm.2015.7387559

26.

Wang, R., Li, R., Sun, H.: Haze removal based on multiple scattering model with superpixel algorithm. Signal Process. 127, 24–36 (2016). https://doi.org/10.1016/j.sigpro.2016.02.003CrossRef

27.

Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Formal Pattern Anal. Appl. 6(4), 309–326 (2004). https://doi.org/10.1007/s10044-003-0197-7MathSciNetCrossRef

28.

Yu, S., Zhang, A., Li, H.: A review of estimating the shape parameter of generalized Gaussian distribution. J. Comput. Inf. Syst. 21(8), 9055–9064 (2012)

29.

Zhang, Y., Wu, J., Xie, X., Li, L., Shi, G.: Blind image quality assessment with improved natural scene statistics model. Digit. Signal Process. 57, 56–65 (2016). https://doi.org/10.1016/j.dsp.2016.05.012MathSciNetCrossRef

Titel: Binarization of Degraded Document Images with Generalized Gaussian Distribution
verfasst von: Robert Krupiński
Piotr Lech
Mateusz Tecław
Krzysztof Okarma
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2019
Print ISBN: 978-3-030-22749-4

Electronic ISBN: 978-3-030-22750-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-22750-0_14

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner