Skip to main content

2020 | OriginalPaper | Buchkapitel

Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model

verfasst von : Robert Krupiński, Piotr Lech, Krzysztof Okarma

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

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

search-config
loading …

Abstract

Image binarization is one of the most relevant preprocessing operations influencing the results of further image analysis conducted for many purposes. During this step a significant loss of information occurs and the use of inappropriate thresholding methods may cause difficulties in further shape analysis or even make it impossible to recognize different shapes of objects or characters. Some of the most typical applications utilizing the analysis of binary images are Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), which may also be applied for unevenly illuminated natural images, as well as for challenging degraded historical document images, considered as typical benchmarking tools for image binarization algorithms.
To face the still valid challenge of relatively fast and simple, but robust binarization of degraded document images, a novel two-step algorithm utilizing initial thresholding, based on the modelling of the simplified image histogram using Gaussian Mixture Model (GMM) and the Monte Carlo method, is proposed in the paper. This approach can be considered as the extension of recently developed image preprocessing method utilizing Generalized Gaussian Distribution (GGD), based on the assumption of its similarity to the histograms of ground truth binary images distorted by Gaussian noise. The processing time of the first step, producing the intermediate images with partially removed background information, may be significantly reduced due to the use of the Monte Carlo method.
The proposed improved approach leads to even better results, not only for well-known DIBCO benchmarking databases, but also for more demanding Bickley Diary dataset, allowing the use of some well-known classical binarization methods, including the global ones, in the second step of the algorithm.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Bernsen, J.: Dynamic thresholding of grey-level images. In: Proceedings 8th International Conference on Pattern Recognition (ICPR), pp. 1251–1255 (1986) Bernsen, J.: Dynamic thresholding of grey-level images. In: Proceedings 8th International Conference on Pattern Recognition (ICPR), pp. 1251–1255 (1986)
4.
Zurück zum Zitat Clarke, R.J.: Transform Coding of Images. Academic press, New York (1985) Clarke, R.J.: Transform Coding of Images. Academic press, New York (1985)
5.
Zurück zum Zitat Deng, F., Wu, Z., Lu, Z., Brown, M.S.: Binarization shop: a user assisted software suite for converting old documents to black-and-white. In: Proceedings of Annual Joint Conference on Digital Libraries, pp. 255–258 (2010) Deng, F., Wu, Z., Lu, Z., Brown, M.S.: Binarization shop: a user assisted software suite for converting old documents to black-and-white. In: Proceedings of Annual Joint Conference on Digital Libraries, pp. 255–258 (2010)
9.
Zurück zum Zitat 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.: Reconstructed quantized coefficients modeled with generalized Gaussian distribution with exponent 1/3. Image Process. Commun. 21(4), 5–12 (2016)CrossRef
10.
Zurück zum Zitat 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.: 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
14.
Zurück zum Zitat Lins, R.D., Bernardino, R.B., de Jesus: D.M.: A quality and time assessment of binarization algorithms. In: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20–25 September 2019, pp. 1444–1450. IEEE (2019). https://doi.org/10.1109/ICDAR.2019.00232 Lins, R.D., Bernardino, R.B., de Jesus: D.M.: A quality and time assessment of binarization algorithms. In: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20–25 September 2019, pp. 1444–1450. IEEE (2019). https://​doi.​org/​10.​1109/​ICDAR.​2019.​00232
18.
Zurück zum Zitat Niblack, W.: An introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986) Niblack, W.: An introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)
23.
Zurück zum Zitat Olver, F.W.J.: Asymptotics and Special Functions. Academic Press, New York (1974)MATH Olver, F.W.J.: Asymptotics and Special Functions. Academic Press, New York (1974)MATH
26.
27.
31.
Zurück zum Zitat Shrivastava, A., Srivastava, D.K.: A review on pixel-based binarization of gray images. In: Satapathy, S.C., Bhatt, Y.C., Joshi, A., Mishra, D.K. (eds.) Proceedings of the International Congress on Information and Communication Technology. AISC, vol. 439, pp. 357–364. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0755-2_38CrossRef Shrivastava, A., Srivastava, D.K.: A review on pixel-based binarization of gray images. In: Satapathy, S.C., Bhatt, Y.C., Joshi, A., Mishra, D.K. (eds.) Proceedings of the International Congress on Information and Communication Technology. AISC, vol. 439, pp. 357–364. Springer, Singapore (2016). https://​doi.​org/​10.​1007/​978-981-10-0755-2_​38CrossRef
32.
Zurück zum Zitat 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 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
37.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model
verfasst von
Robert Krupiński
Piotr Lech
Krzysztof Okarma
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-50426-7_35