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Offline Writer Identification Using Convolutional Neural Network Activation Features

  • Vincent Christlein
  • David Bernecker
  • Andreas Maier
  • Elli Angelopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of \(\mathrm {mAP}\) on the challenging bilingual ICDAR dataset.

Notes

Acknowledgments

This work has been supported by the German Federal Ministry of Education and Research (BMBF), grant-nr. 01UG1236a. The contents of this publication are the sole responsibility of the authors.

References

  1. 1.
    Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Unsupervised and Transfer Learning, Challenges in Machine Learning, vol. 7, pp. 19–41. Bellevue, Jun 2011Google Scholar
  2. 2.
    Bluche, T., Ney, H., Kermorvant, C.: Feature extraction with convolutional neural networks for handwritten word recognition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 285–289. Buffalo, August 2013. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6628629
  3. 3.
    Brink, A., Smit, J., Bulacu, M., Schomaker, L.: Writer identification using directional ink-trace width measurements. Pattern Recogn. 45(1), 162–171 (2012). http://linkinghub.elsevier.com/retrieve/pii/S0031320311002810CrossRefGoogle Scholar
  4. 4.
    Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007). http://www.ncbi.nlm.nih.gov/pubmed/17299226CrossRefGoogle Scholar
  5. 5.
    Christlein, V., Bernecker, D., Hönig, F., Angelopoulou, E.: Writer identification and verification using GMM supervectors. In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 998–1005, March 2014Google Scholar
  6. 6.
    Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a Matlab-like environment for machine learning. In: Big Learning, Workshop on Advances in Neural Information Processing Systems 24 (NIPS 2011), Granada, December 2011Google Scholar
  7. 7.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977). http://www.jstor.org/stable/10.2307/2984875MathSciNetzbMATHGoogle Scholar
  8. 8.
    Djeddi, C., Meslati, L.S., Siddiqi, I., Ennaji, A., Abed, H.E., Gattal, A.: Evaluation of texture features for offline arabic writer identification. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 8–12, Tours, April 2014Google Scholar
  9. 9.
    Fiel, S., Sablatnig, R.: Writer identification and writer retrieval using the Fisher vector on visual vocabularies. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 545–549, Washington, D.C., August 2013. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6628679
  10. 10.
    Gilliam, T., Wilson, R., Clark, J.: Scribe identification in medieval English manuscripts. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1880–1883, Istanbul, August 2010. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5597227
  11. 11.
    Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 392–407. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10584-0_26Google Scholar
  12. 12.
    He, S., Schomaker, L.: Delta-n Hinge: rotation-invariant features for writer identification. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 2023–2028, Stockholm, August 2014. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6977065
  13. 13.
    Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep Features for text spotting. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 512–528. Springer, Zurich (2014). http://link.springer.com/chapter/10.1007/978-3-319-10593-2_34Google Scholar
  14. 14.
    Jain, R., Doermann, D.: Writer identification using an alphabet of contour gradient descriptors. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 550–554, Buffalo, August 2013. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6628680
  15. 15.
    Jain, R., Doermann, D.: Combining local features for offline writer identification. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 583–588, Heraklion, September 2014Google Scholar
  16. 16.
    Jégou, H., Zisserman, A.: Triangulation embedding and democratic aggregation for image search. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3310–3317, Columbus, June 2014. http://hal.inria.fr/hal-00977321/
  17. 17.
    Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012). http://www.ncbi.nlm.nih.gov/pubmed/22156101CrossRefGoogle Scholar
  18. 18.
    Kleber, F., Fiel, S., Diem, M., Sablatnig, R.: CVL-DataBase: An off-line database for writer retrieval, writer identification and word spotting. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 560–564, Washington, D.C., August 2013. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6628682
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc., (2012)Google Scholar
  20. 20.
    Louloudis, G., Gatos, B., Stamatopoulos, N., Papandreou, A.: ICDAR 2013 competition on writer identification. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1397–1401, Washington, D.C., August 2013Google Scholar
  21. 21.
    Newell, A.J.A., Griffin, L.D.L.: Writer identification using oriented basic image features and the delta encoding. Pattern Recogn. 47(6), 2255–2265 (2014). http://linkinghub.elsevier.com/retrieve/pii/S0031320313005153, http://www.sciencedirect.com/science/article/pii/S0031320313005153CrossRefGoogle Scholar
  22. 22.
    Peng, X., Wang, L., Qiao, Y., Peng, Q.: Boosting VLAD with supervised dictionary learning and high-order statistics. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 660–674. Springer, Zurich (2014) Google Scholar
  23. 23.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted gaussian mixture models. Digit. Sig. Process. 10(1–3), 19–41 (2000)CrossRefGoogle Scholar
  24. 24.
    Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the Fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013). http://link.springer.com/10.1007/s11263-013-0636-xMathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Schomaker, L., Bulacu, M.: Automatic writer identification using connected-component contours and edge-based features of uppercase western script. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 787–798 (2004). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1288527CrossRefGoogle Scholar
  26. 26.
    Sculley, D.: Web-scale K-means clustering. In: 19th International Conference on World Wide Web, WWW 2010, pp. 1177–1178. ACM, New York, April 2010. http://doi.acm.org/10.1145/1772690.1772862
  27. 27.
    Sermanet, P., Chintala, S., LeCun, Y.: Convolutional neural networks applied to house numbers digit classification. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3288–3291. IEEE, Tsukuba, November 2012. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6460867
  28. 28.
    Siddiqi, I., Vincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn. 43(11), 3853–3865 (2010). http://linkinghub.elsevier.com/retrieve/pii/S0031320310002438CrossRefzbMATHGoogle Scholar
  29. 29.
    Wu, X., Tang, Y., Bu, W.: Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans. Inf. forensics Secur. 9(3), 526–536 (2014). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6716030CrossRefGoogle Scholar
  30. 30.
    Xu, M., Zhou, X., Li, Z., Dai, B., Huang, T.S.: Extended hierarchical Gaussianization for scene classification. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 1837–1840, Hong Kong, September 2010Google Scholar
  31. 31.
    Zhu, Y.Z.Y., Tan, T.T.T., Wang, Y.W.Y.: Biometric personal identification based on handwriting. In: 15th International Conference on Pattern Recognition (ICPR), vol. 2, pp. 2–5, Barcelona, September 2000Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Vincent Christlein
    • 1
  • David Bernecker
    • 1
  • Andreas Maier
    • 1
  • Elli Angelopoulou
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

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