Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Introducing an approach for writer recognition based on the i -vector paradigm

© The Institution of Engineering and Technology
Received 04/03/2016, Accepted 30/11/2016, Revised 21/07/2016, Published 05/12/2016

An off-line text-independent writer verification system that leverages the similarities with the field of speaker recognition by employing analogous techniques for modelling and comparing the features extracted from the input text images is presented. The main contribution of this work is the use of the i -vector paradigm in a writer verification setting. The proposed system is evaluated with images of lines of text from the IAM Handwriting Database, and compared with more traditional approaches. The authors also analyse several algorithms for the detection and extraction of points of interest in the text images, different parameters for the modelling part and different scoring techniques. The obtained results show that the use of i -vectors clearly improves the performance of the system even for configurations where the overhead of the additional calculations is minimal.

Inspec keywords: speaker recognition; text analysis; feature extraction

Other keywords: feature extraction; offline text-independent writer verification system; writer recognition; i-vector paradigm; speaker recognition; input text images; IAM handwriting database; writer verification setting

Subjects: Document processing and analysis techniques; Speech recognition and synthesis; Speech processing techniques

References

    1. 1)
      • 1. Srihari, S.N., Leedham, G.: ‘A survey of computer methods in forensic document examination’. Proceedings of 11th Conference International on Graphonomics Society (IGS2003), 2003, pp. 278–281.
    2. 2)
      • 11. Bay, H., Ess, A., Tuytelaars, T., et al: ‘Speeded-up robust features (SURF)’, Comput. Vis. Image Underst., 2008, 110, (3), pp. 346–359. http://dx.doi.org/10.1016/j.cviu.2007.09.014.
    3. 3)
      • 15. Glembek, O., Burget, L., Matejka, P., et al: ‘Simplification and optimization of i-vector extraction’. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conf. on, May 2011, pp. 4516–4519.
    4. 4)
      • 6. Hannad, Y., Siddiqi, I., Kettani, M.E.Y.E.: ‘Writer identification using texture descriptors of handwritten fragments’, Expert Syst. Appl., 2016, 47, pp. 14–22. http://www.sciencedirect.com/science/article/pii/S0957417415007551.
    5. 5)
      • 19. Marti, U.-V., Bunke, H.: ‘The IAM-database: an English sentence database for offline handwriting recognition’, Int. J. Document Anal. Recogn., 2002, 5, (1), pp. 39–46. doix: 10.1007/s100320200071.
    6. 6)
      • 22. Larcher, A., Lee, K.A., Ma, B., et al: ‘Phonetically-constrained PLDA modeling for text-dependent speaker verification with multiple short utterances’. 2013 IEEE International Conf. on Acoustics, Speech and Signal Processing, May 2013, pp. 7673–7677.
    7. 7)
      • 7. Bulacu, M., Schomaker, L.: ‘Text-independent writer identification and verification using textural and allographic features’, Pattern Anal. Mach. Intell., IEEE Trans., 2007, 29, (4), pp. 701–717. http://dx.doi.org/10.1109/TPAMI.2007.1009.
    8. 8)
      • 3. Said, H., Tan, T., Baker, K.: ‘Personal identification based on handwriting’, Pattern Recogn., 2000, 33, (1), pp. 149–160. http://www.sciencedirect.com/science/article/pii/S0031320399000060.
    9. 9)
      • 17. ‘OpenCV (Open Source Computer Vision library)’, www.opencv.org/accessed/20/12/2016.
    10. 10)
      • 18. Bonastre, J.-F., Wils, F., Meignier, S.: ‘ALIZE, a free toolkit for speaker recognition’. Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP ‘05). IEEE International Conf. on, March 2005, vol. 1, pp. 737–740.
    11. 11)
      • 5. Okawa, M., Yoshida, K.: ‘Text and user generic model for writer verification using combined pen pressure information from ink intensity and indented writing on paper’, IEEE Trans. Human-Mach. Syst., 2015, 45, (3), pp. 339–349.
    12. 12)
      • 24. Leutenegger, S., Chli, M., Siegwart, R.: ‘BRISK: binary Robust invariant scalable keypoints’. Computer Vision (ICCV), 2011 IEEE International Conf. on, November 2011, pp. 2548–2555.
    13. 13)
      • 12. Davis, S., Mermelstein, P.: ‘Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences’, IEEE Trans. Acoust. Speech Signal Process., , 1980, 28, (4), pp. 357–366.
    14. 14)
      • 9. Wu, X., Tang, Y., Bu, W.: ‘Offline text-independent writer identification based on scale invariant feature transform’, IEEE Trans. Inf. Forensics Security, 2014, 9, (3), pp. 526–536.
    15. 15)
      • 25. Alahi, A., Ortiz, R., Vandergheynst, P.: ‘FREAK: fast retina keypoint’. ComputerVision and Pattern Recognition (CVPR), 2012 IEEE Conf. on, June 2012, pp. 510–517.
    16. 16)
      • 16. Kenny, P., Boulianne, G., Ouellet, P., et al: ‘Joint factor analysis versus eigenchannels in speaker recognition’, IEEE Trans. Audio Speech Lang Process., 2007, 15, (4), pp. 1435–1447.
    17. 17)
      • 2. Plamondon, R., Lorette, G.: ‘Automatic signature verification and writer identification–the state of the art’, Pattern Recogn., 1989, 22, (2), pp. 107–131.
    18. 18)
      • 10. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vision, 2004, 60, (2), pp. 91–110. http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94.
    19. 19)
      • 4. Bertolini, D., Oliveira, L.S., Justino, E., et al: ‘Texture-based descriptors for writer identification and verification’, Expert Syst. Appl., 2013, 40, (6), pp. 2069–2080.
    20. 20)
      • 14. Schlapbach, A., Bunke, H.: ‘Off-line writer identification using Gaussian mixture models’. Pattern Recognition, 2006. ICPR 2006. 18th Int. Conf. on, 2006, vol. 3, pp. 992–995.
    21. 21)
      • 23. Rublee, E., Rabaud, V., Konolige, K., et al: ‘ORB: an efficient alternative to SIFT or SURF’. Computer Vision (ICCV), 2011 IEEE International Conf. on, November 2011, pp. 2564–2571.
    22. 22)
      • 8. Siddiqi, I., Vincent, N.: ‘Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features’, Pattern Recogn., 2010, 43, (11), pp. 3853–3865. http://dx.doi.org/10.1016/j.patcog.2010.05.019.
    23. 23)
      • 20. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. of Fourth Alvey Vision Conf., 1988, pp. 147–151.
    24. 24)
      • 13. Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: ‘Speaker verification using adapted Gaussian mixture models’, Digit. Signal Process., 2000, 10, 1-3, pp. 19–41, (http://www.sciencedirect.com/science/article/pii/S1051200499903615).
    25. 25)
      • 21. Shi, J., Tomasi, C.: ‘Good features to track’. Computer Vision and Pattern Recognition, 1994. Proc. CVPR ‘94., 1994 IEEE Computer Society Conf. on, June 1994, pp. 593–600.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0043
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0043
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address