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Gabor filter-based texture for ancient degraded document image binarization

Abstract

Binarization of ancient degraded document images is a very important step for their preservation and digital use. In this paper, a new simple threshold-based method is proposed for binarization of ancient degraded documents. The proposed method is inspired from the most popular threshold-based methods by exploiting texture information features extracted from both the filtered image using the Gabor filter and the original degraded document. Firstly, a preprocessing stage using the Wiener filter is performed on the degraded image for facilitating the binarization. Then, a Gabor filter bank is weighted according to the dominant slant of the document’s image script for estimating the binarization threshold. Finally, a post-processing stage is applied based on morphological operator for reducing some artifacts. For setting optimal parameters, a new protocol is proposed in the design stage by taking into account the degradation type. Exhaustive experiments are achieved using standard DIBCO datasets series reorganized according to the degradation type and the year of contest. Obtained results are compared against various well-known threshold-based methods. On the other hand, a comparison is achieved with the state-of-the-art methods. Promising results and stability are noticed for the proposed technique, specifically for ink bleed-through degradation and low-contrasted documents.

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References

  1. 1.

    Gatos B, Ntirogiannis K, Pratikakis I (2009) ICDAR 2009 document image binarization contest (DIBCO 2009). In: 10th international conference on document analysis and recognition, IEEE 2009, pp 1375–1382

  2. 2.

    Pratikakis I, Gatos B, Ntirogiannis K (2010) H-DIBCO 2010-handwritten document image binarization competition. In: International conference on frontiers in handwriting recognition (ICFHR), IEEE 2010, pp 727–732

  3. 3.

    Pratikakis I, Gatos B, Ntirogiannis K (2011) ICDAR 2011 document image binarization contest (DIBCO 2011), pp 1506–1510

  4. 4.

    Pratikakis I, Gatos B, Ntirogiannis K (2012) ICFHR 2012 competition on handwritten document image binarization (H-DIBCO 2012), pp 817–822

  5. 5.

    Pratikakis I, Gatos B, Ntirogiannis K (2013) ICDAR 2013 document image binarization contest (DIBCO 2013). In: 12th international conference on document analysis and recognition (ICDAR), IEEE 2013, pp 1471–1476

  6. 6.

    Ntirogiannis K, Gatos B, Pratikakis I (2014) ICFHR2014 competition on handwritten document image binarization (H-DIBCO 2014), pp 809–813

  7. 7.

    Messaoud IB, Amiri H, El Abed H, Margner V (2011) New binarization approach based on text block extraction. In: International conference on document analysis and recognition (ICDAR), IEEE 2011, pp 1205–1209

  8. 8.

    Ramírez-Ortegón MA, Märgner V, Cuevas E, Rojas R (2013) An optimization for binarization methods by removing binary artifacts. Pattern Recogn Lett 34:1299–1306

    Article  Google Scholar 

  9. 9.

    Farrahi Moghaddam R, Cheriet M (2012) AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recogn 45:2419–2431

    Article  Google Scholar 

  10. 10.

    Ntirogiannis K, Gatos B, Pratikakis I (2009) A modified adaptive logical level binarization technique for historical document images. In: 10th international conference on document analysis and recognition, ICDAR’09, IEEE 2009, pp. 1171–1175

  11. 11.

    Lu S, Su B, Tan CL (2010) Document image binarization using background estimation and stroke edges. Int J Doc Anal Recognit (IJDAR) 13:303–314

    Article  Google Scholar 

  12. 12.

    Bataineh B, Abdullah SNHS, Omar K (2011) An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recognit Lett 32:1805–1813

    Article  Google Scholar 

  13. 13.

    Khurshid K, Siddiqi I, Faure C, Vincent N (2009) Comparison of Niblack inspired binarization methods for ancient documents. In: IS&T/SPIE electronic imaging, International Society for Optics and Photonics 2009, pp 72470U–72479

  14. 14.

    Niblack W (1986) An introduction to digital image processing, vol 34. Prentice-Hall, Englewood Cliffs

  15. 15.

    Wolf C, Jolion J.-M, Chassaing F (2002) Text localization, enhancement and binarization in multimedia documents. In: Proceedings 16th international conference on pattern recognition, IEEE 2002, pp 1037–1040

  16. 16.

    Natarajan J, Sreedevi I (2017) Enhancement of ancient manuscript images by log based binarization technique. AEU-Int J Electron Commun 75:15–22

    Article  Google Scholar 

  17. 17.

    Li Z, Liu C, Liu G, Cheng Y, Yang X, Zhao C (2010) A novel statistical image thresholding method. AEU-Int J Electron Commun 64:1137–1147

    Article  Google Scholar 

  18. 18.

    Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recognit 33:225–236

    Article  Google Scholar 

  19. 19.

    Sehad A, Chibani Y, Cheriet M (2014) Gabor filters for degraded document image binarization. In: 14th international conference on frontiers in handwriting recognition (ICFHR), IEEE 2014, pp 702–707

  20. 20.

    Sehad A, Chibani Y, Cheriet M, Yaddaden Y (2013) Ancient degraded document image binarization based on texture features. In: 8th international symposium on image and signal processing and analysis (ISPA), IEEE 2013, pp 189–193

  21. 21.

    Sehad A, Chibani Y, Hedjam R, Cheriet M (2015) LBP-based degraded document image binarization. In: International conference on image processing theory, tools and applications (IPTA), IEEE 2015, pp 213–217

  22. 22.

    Djema A, Chibani Y, Sehad A, Zemouri E-T (2015) Blind versus unblind performance evaluation of binarization methods. In: 13th international conference on document analysis and recognition (ICDAR), IEEE 2015, pp 511–515

  23. 23.

    Sezgin M (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–168

    Article  Google Scholar 

  24. 24.

    Wen J, Li S, Sun J (2013) A new binarization method for non-uniform illuminated document images. Pattern Recognit 46:1670–1690

    Article  Google Scholar 

  25. 25.

    Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:23–27

    Google Scholar 

  26. 26.

    Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285

    Article  Google Scholar 

  27. 27.

    Ntirogiannis K, Gatos B, Pratikakis I (2014) A combined approach for the binarization of handwritten document images. Pattern Recognit Lett 35:3–15

    Article  Google Scholar 

  28. 28.

    Su B, Lu S, Tan CL (2013) Robust document image binarization technique for degraded document images. IEEE Trans Image Process 22:1408–1417

    MathSciNet  Article  MATH  Google Scholar 

  29. 29.

    Howe NR (2012) Document binarization with automatic parameter tuning. Int J Doc Anal Recognit (IJDAR) 16:247–258

    Article  Google Scholar 

  30. 30.

    Cheriet M, Farrahi Moghaddam R, Hedjam R (2013) A learning framework for the optimization and automation of document binarization methods. Comput Vis Image Underst 117:269–280

    Article  Google Scholar 

  31. 31.

    Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4:1549–1560

    Article  Google Scholar 

  32. 32.

    Celik T, Tjahjadi T (2010) Unsupervised colour image segmentation using dual-tree complex wavelet transform. Comput Vis Image Underst 114:813–826

    Article  Google Scholar 

  33. 33.

    Meshgini S, Aghagolzadeh A, Seyedarabi H (2013) Face recognition using Gabor-based direct linear discriminant analysis and support vector machine. Comput Electr Eng 39:727–745

    Article  Google Scholar 

  34. 34.

    Shen L, Bai L (2006) A review on Gabor wavelets for face recognition. Pattern Anal Appl 9:273–292

    MathSciNet  Article  Google Scholar 

  35. 35.

    Liu Y, Srihari SN (1997) Document image binarization based on texture features. IEEE Trans Pattern Anal Mach Intell 19:540–544

    Article  Google Scholar 

  36. 36.

    Yimit A, Hagihara Y, Miyoshi T, Hagihara Y (2013) 2-D direction histogram based entropic thresholding. Neurocomputing 120:287–297

    Article  Google Scholar 

  37. 37.

    Zuñiga AG, Florindo JB, Bruno OM (2014) Gabor wavelets combined with volumetric fractal dimension applied to texture analysis. Pattern Recognit Lett 36:135–143

    Article  Google Scholar 

  38. 38.

    Trier OD, Taxt T (1995) Evaluation of binarization methods for document images. IEEE Trans Pattern Anal Mach Intell 17:312–315

    Article  Google Scholar 

  39. 39.

    Mandal S, Biswas S, Das AK, Chanda B (2014) Binarisation of colour map images through extraction of regions. In: International conference on computer vision and graphics, Springer, Berlin, pp 418–427

  40. 40.

    Yazid H, Arof H (2013) Gradient based adaptive thresholding. J Vis Commun Image Represent 24:926–936

    Article  Google Scholar 

  41. 41.

    Gatos B, Pratikakis I, Perantonis SJ (2006) Adaptive degraded document image binarization. Pattern Recognit 39:317–327

    Article  MATH  Google Scholar 

  42. 42.

    Rivest-Hénault D, Farrahi Moghaddam R, Cheriet M (2012) A local linear level set method for the binarization of degraded historical document images. Int J Doc Anal Recognit 15:101–124

    Article  Google Scholar 

  43. 43.

    Su B, Lu S, Tan CL (2010) Binarization of historical document images using the local maximum and minimum. In: Proceedings of the 9th IAPR international workshop on document analysis systems ACM2010, pp 159–166

  44. 44.

    Lu S, Su B, Tan CL (2010) Document image binarization using background estimation and stroke edges. Int J Doc Anal Recognit 13:303–314

    Article  Google Scholar 

  45. 45.

    Nafchi HZ, Moghaddam RF, Cheriet M (2014) Phase-based binarization of ancient document images: model and applications. IEEE Trans Image Process 23:2916–2930

    MathSciNet  Article  MATH  Google Scholar 

  46. 46.

    Lelore T, Bouchara F (2011) Super-resolved binarization of text based on the FAIR algorithm. In: International conference on document analysis and recognition (ICDAR), IEEE 2011, pp 839–843

  47. 47.

    Howe NR (2011) A Laplacian energy for document binarization. In: International conference on document analysis and recognition (ICDAR), IEEE 2011, pp 6–10

  48. 48.

    Nafchi HZ, Moghaddam RF, Cheriet M (2012) Historical document binarization based on phase information of images. In: Asian conference on computer vision, Springer, Berlin, pp 1–12

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Acknowledgements

This work was supported by le Fond National de la Recherche Scientifique et du Développement Technologique, Ministère de l’Enseignement Supérieur et de la Recherche Scientifique, Algiers, Algeria.

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Correspondence to Abdenour Sehad.

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Sehad, A., Chibani, Y., Hedjam, R. et al. Gabor filter-based texture for ancient degraded document image binarization. Pattern Anal Applic 22, 1–22 (2019). https://doi.org/10.1007/s10044-018-0747-7

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Keywords

  • Historical document
  • Binarization
  • Threshold
  • Texture
  • Gabor filter