Advertisement

A model for the gray-intensity distribution of historical handwritten documents and its application for binarization

  • Marte A. Ramírez-Ortegón
  • Lilia L. Ramírez-RamírezEmail author
  • Ines Ben Messaoud
  • Volker Märgner
  • Erik Cuevas
  • Raúl Rojas
Original Paper

Abstract

In this article, our goal is to describe mathematically and experimentally the gray-intensity distributions of the fore- and background of handwritten historical documents. We propose a local pixel model to explain the observed asymmetrical gray-intensity histograms of the fore- and background. Our pixel model states that, locally, the gray-intensity histogram is the mixture of gray-intensity distributions of three pixel classes. Following our model, we empirically describe the smoothness of the background for different types of images. We show that our model has potential application in binarization. Assuming that the parameters of the gray-intensity distributions are correctly estimated, we show that thresholding methods based on mixtures of lognormal distributions outperform thresholding methods based on mixtures of normal distributions. Our model is supported with experimental tests that are conducted with extracted images from DIBCO 2009 and H-DIBCO 2010 benchmarks. We also report results for all four DIBCO benchmarks.

Keywords

Binarization Thresholding Normal Historical documents Handwritten  DIBCO 

Notes

Acknowledgments

We would like to thanks to the Asociación Mexicana de Cultura A.C. We are so grateful to the editor and all the reviewers for their constructive and meticulous comments.

Supplementary material

10032_2013_212_MOESM1_ESM.xlsx (127 kb)
Supplementary material 1 (xlsx 127 KB)
10032_2013_212_MOESM2_ESM.xlsx (138 kb)
Supplementary material 2 (xlsx 138 KB)
10032_2013_212_MOESM3_ESM.xlsx (973 kb)
Supplementary material 3 (xlsx 972 KB)
10032_2013_212_MOESM4_ESM.xlsx (2.4 mb)
Supplementary material 4 (xlsx 2479 KB)
10032_2013_212_MOESM5_ESM.xlsx (2 mb)
Supplementary material 5 (xlsx 2083 KB)
10032_2013_212_MOESM6_ESM.xlsx (2.1 mb)
Supplementary material 6 (xlsx 2163 KB)
10032_2013_212_MOESM7_ESM.xlsx (224 kb)
Supplementary material 7 (xlsx 224 KB)
10032_2013_212_MOESM8_ESM.rar (15.4 mb)
Supplementary material 8 (rar 15773 KB)

References

  1. 1.
    Badekas, E., Papamarkos, N.: Estimation of appropriate parameter values for document binarization techniques. Int. J. Robotics Autom. 24(1), 66–78 (2009)Google Scholar
  2. 2.
    Bar-Yosef, I., Mokeichev, A., Kedem, K., Dinstein, I., Ehrlich, U.: Adaptive shape prior for recognition and variational segmentation of degraded historical characters. Pattern Recognit. 42(12), 3348–3354 (2009). New Frontiers in Handwriting RecognitionGoogle Scholar
  3. 3.
    Barney Smith, E.H.: An analysis of binarization ground truthing. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS ’10, pp. 27–34. ACM, New York, NY, USA (2010)Google Scholar
  4. 4.
    Bataineh, B., Abdullah, S.N.H.S., Omar, K.: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recognit. Lett. 32(14), 1805–1813 (2011)CrossRefGoogle Scholar
  5. 5.
    Bazi, Y., Bruzzone, L., Melgani, F.: Image thresholding based on the EM algorithm and the generalized gaussian distribution. Pattern Recognit. 40(2), 619–634 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Ben Messaoud, I., El Abed, H., Amiri, H., Märgner, V.: New method for the selection of binarization parameters based on noise features of historical documents. In: Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data, pp. 1:1–1:8. ACM, New York, NY, USA (2011)Google Scholar
  7. 7.
    Brink, A., Smit, J., Bulacu, M., Schomaker, L.: Writer identification using directional ink-trace width measurements. Pattern Recognit. 45(1), 162–171 (2012)CrossRefGoogle Scholar
  8. 8.
    Çelik, T.: Bayesian change detection based on spatial sampling and gaussian mixture model. Pattern Recognit. Lett. 32(12), 1635–1642 (2011)CrossRefGoogle Scholar
  9. 9.
    Chen, Q., Sun, Q., Ann Heng, P., Xia, D.: A double-threshold image binarization method based on edge detector. Pattern Recognit. 41(4), 1254–1267 (2008)CrossRefGoogle Scholar
  10. 10.
    Chou, C.H., Lin, W.H., Chang, F.: A binarization method with learning-built rules for document images produced by cameras. Pattern Recognit. 43(4), 1518–1530 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Chow, C., Kaneko, T.: Boundary detection and volume determination of the left ventricle from a cineangiogram. Comput. Biol. Med. 3(1), 13–16, IN1-IN2, 17–26 (1973). Cardiology and BloodGoogle Scholar
  12. 12.
    Elguebaly, T., Bouguila, N.: Bayesian learning of finite generalized gaussian mixture models on images. Signal Process. 91(4), 801–820 (2011)CrossRefzbMATHGoogle Scholar
  13. 13.
    Fan, S.K.S., Lin, Y.: A fast estimation method for the generalized gaussian mixture distribution on complex images. Comput. Vis. Image Underst. 113(7), 839–853 (2009)CrossRefGoogle Scholar
  14. 14.
    Fan, S.K.S., Lin, Y., Wu, C.C.: Image thresholding using a novel estimation method in generalized gaussian distribution mixture modeling. Neurocomputing 72(1–3), 500–512 (2008). Machine Learning for Signal Processing (MLSP 2006) / Life System Modelling, Simulation, and Bio-inspired Computing (LSMS 2007)Google Scholar
  15. 15.
    Gatos, B., Ntirogiannis, K., Pratikakis, I.: ICDAR 2009 document image binarization contest (DIBCO 2009). In: Tenth International Conference on Document Analysis and Recognition, pp. 1375–1382 (2009)Google Scholar
  16. 16.
    Gatos, B., Ntirogiannis, K., Pratikakis, I.: DIBCO 2009: document image binarization contest. Int. J. Document Anal. Recognit. 14, 35–44 (2011)CrossRefGoogle Scholar
  17. 17.
    Gatos, B., Pratikakis, I., Perantonis, S.: Adaptive degraded document image binarization. Pattern Recognit. 39(3), 317–327 (2006)Google Scholar
  18. 18.
    Gatos, B., Stamatopoulos, N., Louloudis, G.: ICDAR 2009 handwriting segmentation contest. Int. J. Document Anal. Recognit. 14, 25–33 (2011)CrossRefGoogle Scholar
  19. 19.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Englewood Cliffs, NJ (2007)Google Scholar
  20. 20.
    Hedjam, R., Moghaddam, R.F., Cheriet, M.: A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images. Pattern Recognit. 44(9), 2184–2196 (2011)CrossRefGoogle Scholar
  21. 21.
    Howe, N.R.: Document binarization with automatic parameter tuning. Int. J. Document Anal. Recognit. 16, 247–258 (2013)Google Scholar
  22. 22.
    Huang, Z.K., Chau, K.W.: A new image thresholding method based on Gaussian mixture model. Appl. Math. Comput. 205, 899–907 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)CrossRefGoogle Scholar
  24. 24.
    Khosravi, H., Kabir, E.: A blackboard approach towards integrated Farsi OCR system. Int. J. Document Anal. Recognit. 12(1), 21–32 (2009)CrossRefGoogle Scholar
  25. 25.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19(1), 41–47 (1985)CrossRefGoogle Scholar
  26. 26.
    Kuk, J.G., Cho, N.I., Lee, K.M.: MAP-MRF approach for binarization of degraded document image. In: Proceedings of the 15th International Conference on Image Processing, pp. 2612–2615 (2008)Google Scholar
  27. 27.
    Lázaro, J., Martín, J.L., Arias, J., Astarloa, A., Cuadrado, C.: Neuro semantic thresholding using OCR software for high precision OCR applications. Image Vis. Comput. 28, 571–578 (2010)CrossRefGoogle Scholar
  28. 28.
    Lee, H., Verma, B.: Binary segmentation algorithm for english cursive handwriting recognition. Pattern Recognit. 45(4), 1306–1317 (2012)CrossRefGoogle Scholar
  29. 29.
    Lelore, T., Bouchara, F.: FAIR: a fast algorithm for document image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 2039–2048 (2013)CrossRefGoogle Scholar
  30. 30.
    Louloudis, G.E., Gatos, B.G., Pratikakis, I., Halatsis, C.: Text line detection in handwritten documents. Pattern Recognit. 41, 3758–3772 (2008)CrossRefzbMATHGoogle Scholar
  31. 31.
    Lu, S., Su, B., Tan, C.L.: Document image binarization using background estimation and stroke edges. Int. J. Document Anal. Recognit. 13, 303–314 (2010)CrossRefGoogle Scholar
  32. 32.
    Lyon, R.F.: A brief history of pixel. In: IS &T/SPIE Symposium on Electronic, Imaging, pp. 15–19 (2006)Google Scholar
  33. 33.
    Moghaddam, R.F., Cheriet, M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recognit. 43(6), 2186–2198 (2010)CrossRefzbMATHGoogle Scholar
  34. 34.
    Moghaddam, R.F., Cheriet, M.: A variational approach to degraded document enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1347–1361 (2010)CrossRefGoogle Scholar
  35. 35.
    Moghaddam, R.F., Cheriet, M.: Beyond pixels and regions: a non-local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images. Pattern Recognit. 44(2), 363–374 (2011)CrossRefGoogle Scholar
  36. 36.
    Moghaddam, R.F., Cheriet, M.: AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recognit. 46(6), 2419–2431 (2012)CrossRefGoogle Scholar
  37. 37.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Birkeroed (1985)Google Scholar
  38. 38.
    Nikolaou, N., Makridis, M., Gatos, B., Stamatopoulos, N., Papamarkos, N.: Segmentation of historical machine-printed documents using adaptive run length smoothing and skeleton segmentation paths. Image Vis. Comput. 28, 590–604 (2010)CrossRefGoogle Scholar
  39. 39.
    Otsu, N.: A threshold selection method from grey-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar
  40. 40.
    Pai, Y.T., Chang, Y.F., Ruan, S.J.: Adaptive thresholding algorithm: efficient computation technique based on intelligent block detection for degraded document images. Pattern Recognit. 43(9), 3177–3187 (2010)CrossRefzbMATHGoogle Scholar
  41. 41.
    Papavassiliou, V., Stafylakis, T., Katsouros, V., Carayannis, G.: Handwritten document image segmentation into text lines and words. Pattern Recognit. 43(1), 369–377 (2010)CrossRefzbMATHGoogle Scholar
  42. 42.
    Pratikakis, I., Gatos, B., Ntirogiannis, K.: H-DIBCO 2010—handwritten document image binarization competition. In: International Conference on Frontiers in Handwriting Recognition, pp. 727–732. IEEE Computer Society, Los Alamitos, CA, USA (2010)Google Scholar
  43. 43.
    Pratikakis, I., Gatos, B., Ntirogiannis, K.: ICDAR 2011 document image binarization contest (DIBCO 2011). In: 2011 International Conference on Document Analysis and Recognition, pp. 1506–1510. IEEE (2011)Google Scholar
  44. 44.
    Pratikakis, I., Gatos, B., Ntirogiannis, K.: ICFHR 2012 competition on handwritten document image binarization (H-DIBCO 2012). In: 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 813–818 (2012)Google Scholar
  45. 45.
    Ramírez-Ortegón, M., Tapia, E., Block, M., Rojas, R.: Quantile linear algorithm for robust binarization of digitalized letters. In: Ninth International Conference on Document Analysis and Recognition, vol. 2, pp. 1158–1162 (2007)Google Scholar
  46. 46.
    Ramírez-Ortegón, M.A., Rojas, R.: Transition thresholds for binarization of historical documents. In: 20th International Conference on Pattern Recognition, pp. 2362–2365. IEEE Computer Society (2010)Google Scholar
  47. 47.
    Ramírez-Ortegón, M.A., Tapia, E., Ramírez-Ramírez, L.L., Rojas, R., Cuevas, E.: Transition pixel: a concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognit. 43, 1233–1243 (2010)CrossRefzbMATHGoogle Scholar
  48. 48.
    Ramírez-Ortegón, M.A., Tapia, E., Rojas, R., Cuevas, E.: Transition thresholds and transition operators for binarization and edge detection. Pattern Recognit. 43(10), 3243–3254 (2010)CrossRefzbMATHGoogle Scholar
  49. 49.
    Rivest-Hénault, D., Farrahi Moghaddam, R., Cheriet, M.: A local linear level set method for the binarization of degraded historical document images. Int. J. Document Anal. Recognit. 15, 101–124 (2012)CrossRefGoogle Scholar
  50. 50.
    Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225–236 (2000)CrossRefGoogle Scholar
  51. 51.
    Shi, J., Ray, N., Zhang, H.: Shape based local thresholding for binarization of document images. Pattern Recognit. Lett. 33(1), 24–32 (2012)CrossRefGoogle Scholar
  52. 52.
    Smith, A.R.: A pixel is not a little square, a pixel is not a little square, a pixel is not a little square! (and a voxel is not a little cube). Tech. rep, Microsoft (1995)Google Scholar
  53. 53.
    Su, B., Lu, S., Tan, C.L.: Binarization of historical document images using the local maximum and minimum. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 159–166. ACM (2010) Google Scholar
  54. 54.
    Tonazzini, A.: Color space transformations for analysis and enhancement of ancient degraded manuscripts. Pattern Recognit. Image Anal. 20, 404–417 (2010)CrossRefGoogle Scholar
  55. 55.
    Valizadeh, M., Kabir, E.: Binarization of degraded document image based on feature space partitioning and classification. Int. J. Document Anal. Recognit. 15(1), 57–69 (2012)CrossRefGoogle Scholar
  56. 56.
    Valizadeh, M., Kabir, E.: An adaptive water flow model for binarization of degraded document images. Int. J. Document Anal. Recognit. 16(2), 165–176 (2013)CrossRefGoogle Scholar
  57. 57.
    Verma, B., Lee, H.: Segment confidence-based binary segmentation (SCBS) for cursive handwritten words. Expert Syst. Appl. 38(9), 11,167–11,175 (2011)CrossRefGoogle Scholar
  58. 58.
    Vonikakis, V., Andreadis, I., Papamarkos, N.: Robust document binarization with OFF center-surround cells. Pattern Anal. Appl. 14, 219–234 (2011)CrossRefMathSciNetGoogle Scholar
  59. 59.
    Wen, J., Fang, B., Chen, J., Tang, Y., Chen, H.: Fragmented edge structure coding for chinese writer identification. Neurocomputing 86(1), 45–51 (2012)CrossRefGoogle Scholar
  60. 60.
    Wolf, L., Littman, R., Mayer, N., German, T., Dershowitz, N., Shweka, R., Choueka, Y.: Identifying join candidates in the Cairo Genizah. Int. J. Comput. Vis. 94, 1–18 (2010)Google Scholar
  61. 61.
    Xue, J., Zhang, Y., Lin, X.: Rayleigh-distribution based minimum error thresholding for SAR images. J. Electron. (China) 16, 336–342 (1999)CrossRefGoogle Scholar
  62. 62.
    Xue, J.H., Titterington, D.M.: t-tests, F-tests and Otsu’s methods for image thresholding. IEEE Trans. Image Process. 20(8), 2392–2396 (2011)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marte A. Ramírez-Ortegón
    • 1
  • Lilia L. Ramírez-Ramírez
    • 2
    Email author
  • Ines Ben Messaoud
    • 3
  • Volker Märgner
    • 4
  • Erik Cuevas
    • 5
  • Raúl Rojas
    • 6
  1. 1.División Académica de Ciencias BásicasUJATCunduacánMexico
  2. 2.Instituto Tecnológico Autónomo de México (ITAM)MexicoMexico
  3. 3.Laboratoire des Systèmes et Traitement de SignalLSTS Ecole Nationale dIngénieurs de Tunis, ENIT TunisTunisTunisia
  4. 4.Institüt für NachrichtentechnikTechnische Universität BraunschweigBraunschweigGermany
  5. 5.Departamento de Ciencias ComputacionalesUniversidad de GuadalajaraGuadalajaraMexico
  6. 6.Institüt für InformatikFreie Universität BerlinBerlinGermany

Personalised recommendations