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A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases

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Abstract

This article describes a methodology to generate a large database of synthetic samples from a small set of original online handwriting specimens. The overall paradigm is based on the Kinematic Theory of rapid human movements and its sigma-lognormal model. The principal contributions of the present study include (i) development of a strategy for sigma-lognormal model-based generation of synthetic samples from real online handwriting samples of arbitrary scripts captured by arbitrary relevant devices and (ii) verification of the structural similarities, including the naturalness of such synthetic prototypes, through various human perception experiments, computer evaluations and statistical hypothesis testing. A database consisting of a large number of online synthetic handwritten word samples is used to train and evaluate the performance of three existing automatic online handwriting recognition systems. Training based on a combined set of original and synthetic samples improves the recognition accuracies on the test set. A combined training set is useful irrespective of the nature of the feature set used (online, offline or combined). Although the proposed method has primarily been developed and applied to the design of an online handwriting sample database of a popular Indian script, Bangla, it can be applied to the generation of large databases of any arbitrary script for example: English, Chinese and Arabic.

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References

  1. Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: UNIPEN project of online data exchange and recognizer benchmarks. In: Proceedings on 12th ICPR, pp. 29–33 (1994)

  2. Forman, G., Cohen, I.: Learning from little: comparison of classifiers given little training. In: Proceedings on 8th European Conference on PKDD, pp. 161–172 (2004)

  3. Lim, J.J., Salakhutdinov, R., Torralba, A.: Transfer learning by borrowing examples for multiclass object detection. In: Proceedings on NIPS, pp. 118–126 (2011)

  4. Fink, M.: Object classification from a single example utilizing class relevance metrics. In: Proceeding on NIPS, pp. 449–456 (2004)

  5. Varga, T., Bunke, H.: Comparing natural and synthetic training data for off-line cursive handwriting recognition. In: Proceeding on IWFHR, pp. 221–225 (2004)

  6. Plamondon, R.: A kinematic theory of rapid human movements. Part I: movement representation and generation. Biol. Cybern. 72(4), 295–307 (1995)

    Article  MATH  Google Scholar 

  7. Plamondon, R.: A kinematic theory of rapid human movements: Part II: movement time and control. Biol. Cybern. 72(4), 309–320 (1995)

    Article  MATH  Google Scholar 

  8. Baird, H.S.: Document image defect models. In: Baird, H.S., et al. (eds.) Structured Document Image Analysis, pp. 546–556. Springer, New York (1992)

    Chapter  Google Scholar 

  9. Nonnemaker, J.: The safe use of synthetic data in classification. Ph.D. thesis, Lehigh University (2008)

  10. Gader, P.D., Khabou, M.A.: Automatic feature generation for handwritten digit recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18(12), 1256–1261 (1996)

    Article  Google Scholar 

  11. Xu, S., Jiang, H., Jin, T., Lau, F.C.M., Pan, Y.: Generation of Chinese calligraphic writings with style imitation. IEEE Intell. Syst. 24(2), 44–53 (2009)

    Article  Google Scholar 

  12. Varga, T., Bunke, H.: Generation of synthetic training data for an HMM-based handwriting recognition system. In: Proceeding on 7th ICDAR, pp. 618–622 (2003)

  13. Mori, M., Suzuki, A., Shio, A., Ohtsuka, S.: Generating new samples from handwritten numerals based on point correspondence. In: Proceeding on 7th IWFHR, pp. 281–290 (2000)

  14. Cano, J., Perez-Cortes, J., Arlandis, J., Llobet, R.: Training set expansion in handwritten character recognition. In: Proceeding on 9th SSPR, LNCS 2396, pp. 548–556 (2002)

  15. Varga, T., Kilchhofer, D., Bunke, H.: Template-based synthetic handwriting generation for the training of recognition systems. In: Proceeding on 12th IGS, pp. 206–211 (2005)

  16. Thomas, A., Rusu, A., Govindaraju, V.: Generation and performance evaluation of synthetic handwritten CAPTCHAs. Pattern Recognit. 42, 3365–3373 (2009)

    Article  MATH  Google Scholar 

  17. Chowriappa, A., Rodrigues, R.N., Kesavadas, T., Govindaraju, V., Bisantz, A.: Generation of handwriting by active shape modeling and global local approximation (GLA) adaptation. In: Proceeding on ICFHR, pp. 206–211 (2010)

  18. Bayoudh, S., Anquetil, E., Miclet, L., Mouchère, H.: Synthetic online handwriting generation by distortions and analogy. In: Proceeding on 13th IGS, pp. 10–13 (2007)

  19. Galbally, J., Fierrez, J., Martinez-Diaz, M., Ortega-Garcia, J.: Improving the enrollment in dynamic signature verification with synthetic samples. In: Proceeding on 10th ICDAR, pp. 1295–1299 (2009)

  20. Lin, Z., Wan, L.: Style-preserving English handwriting synthesis. Pattern Recognit. 40, 2097–2109 (2007)

    Article  MATH  Google Scholar 

  21. Wang, J., Wu, C., Xu, Y.-Q., Shum, H.-Y.: Combining shape and physical models for online cursive handwriting synthesis. Int. J. Doc. Anal. Recognit. 7, 219–227 (2005)

    Article  Google Scholar 

  22. Choi, H., Cho, C.J., Kim, J.H.: Writer dependent online handwriting generation with Bayesian network. In: 9th IWFHR, pp. 130–135 (2004)

  23. Chang, W.D., Shin, J.: A statistical handwriting model for style-preserving and variable character synthesis. Int. J. Doc. Anal. Recognit. 15(1), 1–19 (2012)

    Article  Google Scholar 

  24. Choi, H., Kim, J.H.: Probabilistic synthesis of personal-style handwriting. In: IEICE Transactions on Information and Systems, E92-D:4, pp. 653–661 (2009)

  25. Lee, D.H., Cho, H.-G.: A new synthesizing method for handwriting Korean scripts. Int. J. Pattern Recognit. Artif. Intell. 12(1), 46–61 (1998)

    Google Scholar 

  26. Graves, A.: Generating sequences with recurrent neural networks. arXiv:1308.0850 [cs.NE] (2013)

  27. Rabasse, C., Guest, R., Fairhurst, C.: A new method for the synthesis of signature data with natural variability. IEEE Trans. Syst. Man Cybern. B 38(3), 691–699 (2008)

    Article  Google Scholar 

  28. Popel, D.V.: Signature Analysis, Verification and Synthesis in Pervasive Environment, Synthesis and Analysis in Biometrics, pp. 31–63. World Scientific, Singapore (2007)

  29. Galbally, J., Plamondon, R., Fierrez, J., Ortega-Garcia, J.: Synthetic on-line signature generation, part I: methodology and algorithms. Pattern Recognit. 45(7), 2610–2621 (2012)

    Article  Google Scholar 

  30. Galbally, J., Fierrez, J., Ortega-Garcia, J., Plamondon, R.: Synthetic on-line signature generation, part II: experimental validation. Pattern Recognit. 45(7), 2622–2632 (2012)

    Article  Google Scholar 

  31. Almaksour, A., Anquetil, E., Plamondon, R., O’Reilly, C.: Synthetic handwritten gesture generation using sigma-lognormal model for evolving handwriting classifier. In: Proceeding on 15th IGS, pp. 98–101 (2011)

  32. Grossberg, S., Paine, R.W.: A neural model of corticocerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements. Neural Netw. 13, 999–1046 (2000)

    Article  Google Scholar 

  33. Schomaker, L.R.B.: Simulation and recognition of handwriting movement: a vertical approach to modeling human motor behavior. Ph.D. thesis, Nijmegen University (1991)

  34. Plamondon, R., Guefali, W.: The generation of handwriting with delta lognormal synergies. Biol. Cybern. 78, 119–132 (1998)

    Article  MATH  Google Scholar 

  35. Gangadhar, G., Joseph, D., Chakravarthy, V.S.: An oscillatory neuromotor model of handwriting generation. Int. J. Doc. Anal. Recognit. 10(2), 69–84 (2007)

    Article  Google Scholar 

  36. Edelman, S., Flash, T.: A model of handwriting. Biol. Cybern. 57, 25–36 (1987)

    Article  Google Scholar 

  37. Wada, Y., Kawato, M.: A theory for cursive handwriting based on the minimization principle. Biol. Cybern. 73(1), 3–13 (1995)

    Article  MATH  Google Scholar 

  38. Wada, Y., Kasuga, H., Surnita, K.: An evolutionary approach for the generation of diversiform characters using a handwriting model. In: Proceeding on 16th ICPR, vol. 1, pp. 131–134 (2002)

  39. Ltaief, M., Bezine, H., Alimi, A.M.: A neuro-beta-elliptic model for handwriting generation movements. In: Proceeding of 13th ICFHR, pp. 803–808 (2012)

  40. Bezine, H., Alimi, A.M., Sherkat, N.: Generation and analysis of handwriting script with the beta-elliptic model. In: Proceeding on 9th ICFHR, pp. 515–520 (2004)

  41. Ghosh, D., Shivaprasad, A.P.: An analytical approach for generation of artificial hand-printed character database from given generative models. Pattern Recognit. 32(6), 907–920 (1999)

    Article  Google Scholar 

  42. Woch, A., Plamondon, R., O’Reilly, C.: Kinematic characteristics of successful movement primitives in young and older subjects: a delta-lognormal comparison. Hum. Mov. Sci. 30(1), 1–17 (2011)

    Article  Google Scholar 

  43. Plamondon, R., O’Reilly, C., Ouellet-Plamondon, C.: Strokes against strokes—strokes for strides. Pattern Recognit. 47, 929–944 (2014)

    Article  Google Scholar 

  44. Plamondon, R., Djioua, M.: A multi-level representation paradigm for handwriting stroke generation. Hum. Mov. Sci. 25(4–5), 586–607 (2006)

    Article  Google Scholar 

  45. Djioua, M., Plamondon, R.: An interactive system for the automatic generation of huge handwriting databases from a few specimens. In: Proceeding on 19th ICPR, pp. 1–4 (2008)

  46. O’Reilly, C., Plamondon, R.: Development of a sigma-lognormal representation for online signatures. Pattern Recognit. 42(12), 3324–3337 (2009)

    Article  MATH  Google Scholar 

  47. Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses, 3rd edn. Springer, Berlin (2005). (2nd Printing, 2008)

    MATH  Google Scholar 

  48. Chowdhury, S.D., Bhattacharya, U., Parui, S.K.: Levenshtein distance metric based holistic handwritten word recognition. In: Proceeding on 4th MOCR. ACM (2013)

  49. Mohiuddin, S.k., Bhattacharya, U., Parui, S.K.: Unconstrained Bangla online handwriting recognition based on MLP and SVM. In: Proceeding on J-MOCR-AND. ACM (2011)

  50. Bhattacharya, U., Nigam, A., Rawat, Y.S., Parui, S.K.: An analytic scheme for online handwritten Bangla cursive word recognition. In: Proceeding on 11th ICFHR, pp. 320–325 (2008)

  51. Jaeger, S., Manke, S., Reichert, J., Waibel, A.: Online handwriting recognition: the NPen++ recognizer. Int. J. Doc. Anal. Recognit. 3(3), 169–180 (2001)

    Article  Google Scholar 

  52. Samanta, O., Bhattacharya, U., Parui, S.K.: Smoothing of HMM parameters for efficient recognition of online handwriting. Pattern Recognit. 47(11), 3614–3629 (2014)

    Article  Google Scholar 

  53. http://lipitk.sourceforge.net/resources.htm. Accessed 5 May 2017

  54. El Abed, H., Márgner, V., Kherallah, M., Alimi, A.M.: ICDAR 2009 online Arabic handwriting recognition competition. In: Proceeding on 10th ICFHR, pp. 1388–1392 (2009)

  55. Jin, L., Gao, Y., Liu, G., Li, Y., Ding, K.: Scut-couch2009—a comprehensive online unconstrained Chinese handwriting database and benchmark evaluation. Int. J. Doc. Anal. Recognit. 14(1), 53–64 (2011)

    Article  Google Scholar 

  56. https://www.cs.toronto.edu/graves/handwriting.html. Accessed 5 May 2017

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Acknowledgements

This work was partly supported by the RGPIN-915 Grant from NSERC, Canada, to R. Plamondon.

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Authors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ujjwal Bhattacharya, Souvik Dutta Chowdhury & Swapan K. Parui

  2. École Polytechnique de Montréal, Montréal, Canada

    Réjean Plamondon

  3. Heritage Institute of Technology, Kolkata, India

    Pankaj Goyal

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  1. Ujjwal Bhattacharya
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  2. Réjean Plamondon
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  3. Souvik Dutta Chowdhury
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  4. Pankaj Goyal
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  5. Swapan K. Parui
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Correspondence to Ujjwal Bhattacharya.

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Bhattacharya, U., Plamondon, R., Dutta Chowdhury, S. et al. A sigma-lognormal model-based approach to generating large synthetic online handwriting sample databases. IJDAR 20, 155–171 (2017). https://doi.org/10.1007/s10032-017-0287-5

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  • DOI: https://doi.org/10.1007/s10032-017-0287-5

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