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Neural Networks for Handwriting Recognition

  • Marcus Liwicki
  • Alex Graves
  • Horst Bunke
Part of the Studies in Computational Intelligence book series (SCI, volume 386)

Abstract

In this chapter a novel kind of Recurrent Neural Networks (RNNs) is described. Bi- and Multidimensional RNNs combined with Connectionist Temporal Classification allow for a direct recognition of raw stroke data or raw pixel data. In general, recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to assimilate context information, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing, or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This chapter describes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range, bidirectional or multidirectional interdependencies. In experiments on two unconstrained handwriting databases, the new approach achieves word recognition accuracies of 79,7% on on-line data and 74,1% on off-line data, significantly outperforming a state-of-the-art HMM-based system. Promising experimental results on various other datasets from different countries are also presented. A toolkit implementing the networks is freely available for public.

Keywords

Hide Markov Model Recurrent Neural Network Text Line Handwriting Recognition Handwritten Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Seiler, R., Schenkel, M., Eggimann, F.: Off-line cursive handwriting recognition compared with on-line recognition. In: ICPR 1996: Proceedings of the International Conference on Pattern Recognition (ICPR 1996), vol. IV-7472, p. 505. IEEE Computer Society, Washington, DC, USA (1996)Google Scholar
  2. 2.
    Tappert, C., Suen, C., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(8), 787–808 (1990)CrossRefGoogle Scholar
  3. 3.
    Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRefGoogle Scholar
  4. 4.
    Vinciarelli, A.: A survey on off-line cursive script recognition. Pattern Recognition 35(7), 1433–1446 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Bunke, H.: Recognition of cursive roman handwriting - past present and future. In: Proc. 7th Int. Conf. on Document Analysis and Recognition, vol. 1, pp. 448–459 (2003)Google Scholar
  6. 6.
    Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: Unipen project of on-line data exchange and recognizer benchmarks. In: Proc. 12th Int. Conf. on Pattern Recognition, pp. 29–33 (1994)Google Scholar
  7. 7.
    Hu, J., Lim, S., Brown, M.: Writer independent on-line handwriting recognition using an HMM approach. Pattern Recognition 33(1), 133–147 (2000)CrossRefGoogle Scholar
  8. 8.
    Bahlmann, C., Burkhardt, H.: The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. and Mach. Intell. 26(3), 299–310 (2004)CrossRefGoogle Scholar
  9. 9.
    Bahlmann, C., Haasdonk, B., Burkhardt, H.: Online handwriting recognition with support vector machines - a kernel approach. In: Proc. 8th Int. Workshop on Frontiers in Handwriting Recognition, pp. 49–54 (2002)Google Scholar
  10. 10.
    Wilfong, G., Sinden, F., Ruedisueli, L.: On-line recognition of handwritten symbols. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 935–940 (1996)CrossRefGoogle Scholar
  11. 11.
    Sayre, K.M.: Machine recognition of handwritten words: A project report. Pattern Recognition 5(3), 213–228 (1973)CrossRefGoogle Scholar
  12. 12.
    Schomaker, L.: Using stroke- or character-based self-organizing maps in the recognition of on-line, connected cursive script. Pattern Recognition 26(3), 443–450 (1993)CrossRefGoogle Scholar
  13. 13.
    Kavallieratou, E., Fakotakis, N., Kokkinakis, G.: An unconstrained handwriting recognition system. Int. Journal on Document Analysis and Recognition 4(4), 226–242 (2002)CrossRefGoogle Scholar
  14. 14.
    Bercu, S., Lorette, G.: On-line handwritten word recognition: An approach based on hidden Markov models. In: Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition, pp. 385–390 (1993)Google Scholar
  15. 15.
    Starner, T., Makhoul, J., Schwartz, R., Chou, G.: Online cursive handwriting recognition using speech recognition techniques. In: Int. Conf. on Acoustics, Speech and Signal Processing, vol. 5, pp. 125–128 (1994)Google Scholar
  16. 16.
    Hu, J., Brown, M., Turin, W.: HMM based online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 1039–1045 (1996)CrossRefGoogle Scholar
  17. 17.
    Marti, U.-V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Artificial Intelligence 15, 65–90 (2001)CrossRefGoogle Scholar
  18. 18.
    Schenkel, M., Guyon, I., Henderson, D.: On-line cursive script recognition using time delay neural networks and hidden Markov models. Machine Vision and Applications 8, 215–223 (1995)CrossRefGoogle Scholar
  19. 19.
    El-Yacoubi, A., Gilloux, M., Sabourin, R., Suen, C.: An HMM-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 752–760 (1999)CrossRefGoogle Scholar
  20. 20.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  21. 21.
    Bourbakis, N.G.: Handwriting recognition using a reduced character method and neural nets. In: Proc. SPIE Nonlinear Image Processing VI, vol. 2424, pp. 592–601 (1995)Google Scholar
  22. 22.
    Bourlard, H., Morgan, N.: Connnectionist Speech Recognition: A Hybrid Approach. Kluwer Academic Publishers (1994)Google Scholar
  23. 23.
    Bengio, Y.: Markovian models for sequential data. Neural Computing Surveys 2, 129–162 (1999)Google Scholar
  24. 24.
    Brakensiek, A., Kosmala, A., Willett, D., Wang, W., Rigoll, G.: Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data. In: Proc. 5th Int. Conf. on Document Analysis and Recognition, Bangalore, India, pp. 446–449 (1999)Google Scholar
  25. 25.
    Marukatat, S., Artires, T., Dorizzi, B., Gallinari, P.: Sentence recognition through hybrid neuro-markovian modelling. In: Proc. 6th Int. Conf. on Document Analysis and Recognition, pp. 731–735 (2001)Google Scholar
  26. 26.
    Jaeger, S., Manke, S., Reichert, J., Waibel, A.: Online handwriting recognition: the NPen++ recognizer. Int. Journal on Document Analysis and Recognition 3(3), 169–180 (2001)CrossRefGoogle Scholar
  27. 27.
    Caillault, E., Viard-Gaudin, C., Ahmad, A.R.: MS-TDNN with global discriminant trainings. In: Proc. 8th Int. Conf. on Document Analysis and Recognition, pp. 856–861 (2005)Google Scholar
  28. 28.
    Senior, A.W., Fallside, F.: An off-line cursive script recognition system using recurrent error propagation networks. In: International Workshop on Frontiers in Handwriting Recognition, Buffalo, NY, USA, pp. 132–141 (1993)Google Scholar
  29. 29.
    Senior, A.W., Robinson, A.J.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 309–321 (1998)CrossRefGoogle Scholar
  30. 30.
    Schenk, J., Rigoll, G.: Novel hybrid NN/HMM modelling techniques for on-line handwriting recognition. In: Proc. 10th Int. Workshop on Frontiers in Handwriting Recognition, pp. 619–623 (2006)Google Scholar
  31. 31.
    IAM-OnDB an on-line English sentence database acquired from handwritten text on a whiteboard, In: Proc. 8th Int. Conf. on Document Analysis and Recognition, pp. 956–961 (2005)Google Scholar
  32. 32.
    The IAM-database: an English sentence database for offline handwriting recognition. Int. Journal on Document Analysis and Recognition 5, 39–46 (2002)Google Scholar
  33. 33.
    Liwicki, M., Bunke, H.: Handwriting recognition of whiteboard notes – studying the influence of training set size and type. Int. Journal of Pattern Recognition and Artificial Intelligence 21(1), 83–98 (2007)CrossRefGoogle Scholar
  34. 34.
    Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks., Ph.D. thesis, Technical University of Munich (2008)Google Scholar
  35. 35.
    Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Processing 45, 2673–2681 (1997)CrossRefGoogle Scholar
  36. 36.
    Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labeling unsegmented sequence data with recurrent neural networks. In: Proc. Int. Conf. on Machine Learning, pp. 369–376 (2006)Google Scholar
  37. 37.
    Pitman, J.A.: Handwriting recognition: Tablet pc text input. Computer 40(9), 49–54 (2007)CrossRefGoogle Scholar
  38. 38.
    Proc. 10th Int. Conf. on Document Analysis and Recognition (2009)Google Scholar
  39. 39.
    Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009)CrossRefGoogle Scholar
  40. 40.
    Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Computation 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  41. 41.
    Gers, F.: Long Short-Term Memory in Recurrent Neural Networks. Ph.D.thesis, EPFL (2001)Google Scholar
  42. 42.
    Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5-6), 602–610 (2005)CrossRefGoogle Scholar
  43. 43.
    Graves, A., Fernández, S., Schmidhuber, J.: Multidimensional recurrent neural networks. In: Proc. Int. Conf. on Artificial Neural Networks (2007)Google Scholar
  44. 44.
    Baldi, P., Pollastri, G.: The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. J. Mach. Learn. Res. 4, 575–602 (2003)Google Scholar
  45. 45.
    Reisenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. NatureNeuroscience 2(11), 1019–1025 (1999)Google Scholar
  46. 46.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  47. 47.
    Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. Advances in Neural Information Processing Systems 21, 545–552 (2009)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.German Research Center for Artificial IntelligenceKaiserslauternGermany
  2. 2.Institute for Informatics 6Technical University of MunichGarching bei MünchenGermany
  3. 3.Institute for Computer Science and Applied MathematicsBernSwitzerland

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