World Academy of Science, Engineering and Technology
Published September 20, 2008 | Version 13639
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Reduced Dynamic Time Warping for Handwriting Recognition Based on Multidimensional Time Series of a Novel Pen Device

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The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has been applied for time series analysis of five different sensor channels providing pressure, acceleration and tilt data of the pen generated during handwriting on a paper pad. But the standard DTW has processing time and memory space problems which limit its practical use for online handwriting recognition. To face with this problem the DTW has been applied to the sum of the five sensor signals after an adequate down-sampling of the data. Preliminary results have shown that processing time and memory size could significantly be reduced without deterioration of performance in single character and word recognition. Further excellent accuracy in recognition was achieved which is mainly due to the reduced dynamic time warping RDTW technique and a novel pen device BiSP.

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References

  • http://www.bisp-regensburg.de
  • C. Gruber, C. Hook, J. Kempf, G. Scharfenberg, B. Sick, " A Flexible Architecture for Online Signature Verification Based on a Novel Biometric Pen" In Proceedings of the 2006 IEEE Mountain Workshop on Adaptive and Learning Systems (SMCals/06)"; pp. 110-115; Logan, 2006.
  • T. Gruber, C. Gruber, B. Sick, " Online Signature Verification With new Time Series Kernels for Support Vector Machines" D. Zhang, A. K. Jain (Eds.) Advances in Biometrics: International Conference ICB 2006; Lecture Notes in Computer Science 3832, Springer Verlag, Berlin, Heidelberg, New York; pp. 500-508; Hong Kong, 2006
  • Hook C., Kempf J., Scharfenberg, G."New Pen Device for Biometrical 3D Pressure Analysis of Handwritten Characters, Words and Signatures." Proceedings ACM Multimedia Berkeley, USA (2003) 38- 44
  • Hook C., Kempf J., Scharfenberg G, "A Novel Digitizing Pen for the Analysis of Pen Pressure and Inclination in handwriting Biometrics", Biometric Authentication Workshop, Prague 2004, Lecture Notice in Computer Science. Springer 2004.
  • ┼áoule M., Kempf J. "Handwritten Text Analysis through Sound. A New Device for Handwriting Analysis", In Proceedings IWSSIP, Prague, (2003) 254-257
  • ┼áoule, M, "Person Authentification Using Acoustic Handwritten Text", Ph.D. thesis, Pilsen (2007), Czech Republic.
  • M. Dose, C. Gruber, A. Grunz, C. Hook, J. Kempf, G. Scharfenberg, B. Sick, "Towards an Automated Analysis of Neuroleptics- Impact on Human Hand Motor Skills", In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2007)"; pp. 494-501, Honolulu, 2007
  • A. Ünlü, R. Brause, K. Krakow," Handwriting Analysis for Diagnosis and Prognosis of Parkinson-s Disease", Proc.Int. Symp. Biological and Medical Data analysis, LNCS Vol. 4345,Springer Heidelberg 2006,pp.441-450 [10] TakitaT., Hangai S., Kempf J, Hook C., Scharfenberg G., "An Identification of Japanese Numerical Characters on a Biometrical Smart Pen System", In Automatic Identification Advanced Technologies, 2007 IEEE Workshop, June 2007. [11] Tapperet C., Suen C., Wakahara T, "The State of the Art in On-line Handwriting Recognition", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.12, No.8, 1990, pp.787-808. [12] R. Niels, L. Vuurpijl, "Dynamic Time Warping Applied to Tamil Character Recognition". Proceedings of the 8th International Conference on Document Analysis and Recognition, 2005. [13] Marcos Faundez Zanuy, "On-line signature recognition based on VQDTW", ELSEVIER, June, 2006. [14] Kruskall, J.B., Liberman,M., "The Symmetric Time Warping Algorithm: From continuous to discrete". In time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison, pp.125-161,, Addison -Wesley (1983) [15] Eamonn J. Keogh, Michael J. Pazzani, "Derivative Dynamic Time Warping" In Proc. Of the 1st SIAM Int.Conf. on Data Mining (SDM- 2001). [16] V. Vuori, J. Laaksonen, E. Oja, J. Kangas, "Speeding up On-line Recognition of Handwritten Characters by Pruning the Prototype Set" In Proc.Of (ICDAR-01), pp.501-505. [17] Salvador S., Chan P.: "FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space", Intelligent Data Analysis, 2007. [18] H. Sakoe, S. Chiba, "Dynamic programming algorithm optimization for spoken word recognition". IEEE Transaction on Acoustics, Speech and Signal Processing, Vol 26, NO1, pp. 43-49. February 1978. [19] http://www.bromba.com/faq/biofaqe.htm#ROC [20] www.mathworks.com