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Crossing Number Features: From Biometrics to Printed Character Matching

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Document Analysis and Recognition – ICDAR 2021 Workshops (ICDAR 2021)

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Abstract

Nowadays, the security of both digital and hard-copy documents has become a real issue. As a solution, numerous integrity check approaches have been designed. The challenge lies in finding features which are robust to print-and-scan process. In this paper, we propose a new method of printed-and-scanned character matching based on the adaptation of biometrical features. After the binarization and the skeletonization of a character, feature points are extracted by computing crossing numbers. The feature point set can then be smoothed to make it more suitable for template matching. From various experimental results, we have shown that an accuracy of more than \(95\%\) is achieved for print-and-scan resolutions of 300 dpi and 600 dpi. We have also highlighted the feasibility of the proposed method in case of double print-and-scan operation. The comparison with a state-of-the-art method shows that the generalization of proposed matching method is possible while using different fonts.

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Notes

  1. 1.

    The database is available on demand. Contact: iuliia.tkachenko@liris.cnrs.fr.

References

  1. Alhéritière, H., Cloppet, F., Kurtz, C., Ogier, J.M., Vincent, N.: A document straight line based segmentation for complex layout extraction. In: IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1126–1131. IEEE (2017)

    Google Scholar 

  2. Artaud, C., Sidère, N., Doucet, A., Ogier, J.M., Poulain D’Andecy, V.: Find it! Fraud detection contest report. In: International Conference on Pattern Recognition (ICPR), pp. 13–18. IEEE (2018)

    Google Scholar 

  3. Bui, Q.A., Mollard, D., Tabbone, S.: Selecting automatically pre-processing methods to improve OCR performances. In: IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 169–174. IEEE (2017)

    Google Scholar 

  4. Chiang, P.J., et al.: Printer and scanner forensics: models and methods. In: Sencar, H.T., Velastin, S., Nikolaidis, N., Lian, S. (eds.) Intelligent Multimedia Analysis for Security Applications, pp. 145–187. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11756-5_7

    Chapter  Google Scholar 

  5. Eskenazi, S., Bodin, B., Gomez-Krämer, P., Ogier, J.M.: A perceptual image hashing algorithm for hybrid document security. In: IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 741–746. IEEE (2017)

    Google Scholar 

  6. Eskenazi, S., Gomez-Krämer, P., Ogier, J.-M.: When document security brings new challenges to document analysis. In: Garain, U., Shafait, F. (eds.) IWCF 2012/2014. LNCS, vol. 8915, pp. 104–116. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20125-2_10

    Chapter  Google Scholar 

  7. Eskenazi, S., Gomez-Krämer, P., Ogier, J.M.: The Delaunay document layout descriptor. In: Symposium on Document Engineering, pp. 167–175. ACM (2015)

    Google Scholar 

  8. Eskenazi, S., Gomez-Krämer, P., Ogier, J.M.: A study of the factors influencing OCR stability for hybrid security. In: IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 9, pp. 3–8. IEEE (2017)

    Google Scholar 

  9. Ferreira, A., et al.: Data-driven feature characterization techniques for laser printer attribution. IEEE Trans. Inf. Forensics Secur. 12(8), 1860–1873 (2017)

    Google Scholar 

  10. Leborgne, A., Mille, J., Tougne, L.: Noise-resistant digital Euclidean connected skeleton for graph-based shape matching. J. Visual Commun. Image Representation 31, 165–176 (2015)

    Google Scholar 

  11. Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP: Graph. Models Image Process. 56(6), 462–478 (1994)

    Google Scholar 

  12. Lipkina, A., Mestetskiy, L.M.: Grapheme approach to recognizing letters based on medial representation. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pp. 351–358 (2019)

    Google Scholar 

  13. Mikkilineni, A.K., Khanna, N., Delp, E.J.: Texture based attacks on intrinsic signature based printer identification. In: Media Forensics and Security, vol. 7541, p. 75410T. International Society for Optics and Photonics (2010)

    Google Scholar 

  14. Mikkilineni, A.K., Khanna, N., Delp, E.J.: Forensic printer detection using intrinsic signatures. In: Media Watermarking, Security, and Forensics, vol. 7880, p. 78800R. International Society for Optics and Photonics (2011)

    Google Scholar 

  15. Navarro, L.C., Navarro, A.K., Rocha, A., Dahab, R.: Connecting the dots: toward accountable machine-learning printer attribution methods. J. Vis. Commun. Image Representat. 53, 257–272 (2018)

    Google Scholar 

  16. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Google Scholar 

  17. Picard, J.: Digital authentication with copy-detection patterns. In: Electronic Imaging, pp. 176–183. International Society for Optics and Photonics (2004)

    Google Scholar 

  18. Roy, P., Bhattacharya, S., Ghosh, S., Pal, U.: STEFANN: scene text editor using font adaptive neural network. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13228–13237. IEEE/CVF (2020)

    Google Scholar 

  19. Shang, S., Memon, N., Kong, X.: Detecting documents forged by printing and copying. EURASIP J. Adv. Sig. Process. 2014(1), 1–13 (2014)

    Google Scholar 

  20. Solanki, K., Madhow, U., Manjunath, B.S., Chandrasekaran, S., El-Khalil, I.: Print and scan resilient data hiding in images. IEEE Trans. Inf. Forensics Secur. 1(4), 464–478 (2006)

    Google Scholar 

  21. Tan, L., Sun, X.: Robust text hashing for content-based document authentication. Inf. Technol. J. 10(8), 1608–1613 (2011)

    Google Scholar 

  22. Tkachenko, I., Gomez-Krämer, P.: Robustness of character recognition techniques to double print-and-scan process. In: IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 09, pp. 27–32 (2017)

    Google Scholar 

  23. Tkachenko, I., Puech, W., Destruel, C., Strauss, O., Gaudin, J.M., Guichard, C.: Two-level QR code for private message sharing and document authentication. IEEE Trans. Inf. Forensics Secur. 11(3), 571–583 (2016)

    Google Scholar 

  24. Van Renesse, R.L.: Optical document security. Appl. Opt. 13528, 5529–34 (1996)

    Google Scholar 

  25. Villán, R., Voloshynovskiy, S., Koval, O., Deguillaume, F., Pun, T.: Tamper-proofing of electronic and printed text documents via robust hashing and data-hiding. In: Security, Steganography, and Watermarking of Multimedia Contents, vol. 6505, p. 65051T. International Society for Optics and Photonics (2007)

    Google Scholar 

  26. Voloshynovskiy, S., Diephuis, M., Holotyak, T.: Mobile visual object identification: from SIFT-BoF-RANSAC to sketchprint. In: Media Watermarking, Security, and Forensics 2015, vol. 9409, p. 94090Q. International Society for Optics and Photonics (2015)

    Google Scholar 

  27. Wu, L., et al.: Editing text in the wild. In: International Conference on Multimedia, pp. 1500–1508. ACM (2019)

    Google Scholar 

  28. Yang, Q., Huang, J., Lin, W.: SwapText: image based texts transfer in scenes. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14700–14709. IEEE/CVF (2020)

    Google Scholar 

  29. Yu, L., Niu, X., Sun, S.: Print-and-scan model and the watermarking countermeasure. Image Vis. Comput. 23(9), 807–814 (2005)

    Google Scholar 

  30. Zhao, F., Tang, X.: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recogn. 40(4), 1270–1281 (2007)

    Google Scholar 

  31. Zhu, B., Wu, J., Kankanhalli, M.S.: Print signatures for document authentication. In: Conference on Computer and Communications Security, pp. 145–154. ACM (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIRMM, Université de Montpellier, CNRS, Montpellier, France

    Pauline Puteaux

  2. LIRIS, Université Lumière Lyon 2, CNRS, Lyon, France

    Iuliia Tkachenko

Authors
  1. Pauline Puteaux
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  2. Iuliia Tkachenko
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Correspondence to Iuliia Tkachenko .

Editor information

Editors and Affiliations

  1. Boise State University, Boise, ID, USA

    Elisa H. Barney Smith

  2. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Puteaux, P., Tkachenko, I. (2021). Crossing Number Features: From Biometrics to Printed Character Matching. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12916. Springer, Cham. https://doi.org/10.1007/978-3-030-86198-8_31

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  • DOI: https://doi.org/10.1007/978-3-030-86198-8_31

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  • Print ISBN: 978-3-030-86197-1

  • Online ISBN: 978-3-030-86198-8

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