Enhanced Non-linear Features for On-line Handwriting Recognition Using Deep Learning

  • Qing Zhang
  • Minhua Wu
  • Zhenbo Luo
  • Youxin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8834)


Conventionally, a deep neural network (DNN) is trained to predict probabilities of class labels. Recently, DNN has shown great success in many pattern recognition tasks such as speech recognition and handwritten digit recognition. To take advantage of its great learning power, we propose and build an on-line handwriting recognition system for French strings, which applies a DNN for non-linear feature transformation before training the character models. When a DNN is predicting class labels, its hidden layer outputs can be regarded as a better representation of the original features extracted from handwriting trajectory data. In this paper, we demonstrate that the proposed system can achieve a relative character error rate (CER) reduction of about 28.5% when being compared to a conventional system without feature transformation. We also notice that the CER could be further reduced by 3.3% relatively when the transformed features are used along with the original features.


Deep neural network (DNN) On-line handwriting recognition Feature transformation Hidden Markov model (HMM) Gaussian mixture model (GMM) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)CrossRefGoogle Scholar
  2. 2.
    Santosh, K., Nattee, C., et al.: A comprehensive survey on on-line handwriting recognition technology and its real application to the nepalese natural handwriting. Kathmandu University Journal of Science, Engineering, and Technology 5(I), 31–55 (2009)Google Scholar
  3. 3.
    Jaeger, S., Manke, S., Reichert, J., Waibel, A.: Online handwriting recognition: The npen++ recognizer. International Journal on Document Analysis and Recognition 3(3), 169–180 (2001)CrossRefGoogle Scholar
  4. 4.
    Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: 2011 IEEE International Conference on Document Analysis and Recognition (ICDAR), pp. 1135–1139 (2011)Google Scholar
  5. 5.
    Liwicki, M., Graves, A., Bunke, H., Schmidhuber, J.: A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proc. 9th Int. Conf. on Document Analysis and Recognition, vol. 1, pp. 367–371 (2007)Google Scholar
  6. 6.
    Seide, F., Li, G., Yu, D.: Conversational speech transcription using context- dependent deep neural networks. In: Interspeech, pp. 437–440 (2011)Google Scholar
  7. 7.
    Deng, L., Yu, D.: DEEP LEARNING: Methods and Applications. NOW Publishers (2014)Google Scholar
  8. 8.
    Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep, big, simple neural nets for handwritten digit recognition. Neural Computation 22(12), 3207–3220 (2010)CrossRefGoogle Scholar
  9. 9.
    Mitchell, M.: An introduction to genetic algorithms. MIT Press (1998)Google Scholar
  10. 10.
    Povey, D., Woodland, P.C.: Minimum phone error and I-smoothing for improved discriminative training. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 105–108 (2002)Google Scholar
  11. 11.
    Forney Jr., G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qing Zhang
    • 1
  • Minhua Wu
    • 1
  • Zhenbo Luo
    • 1
  • Youxin Chen
    • 1
  1. 1.Handwriting GroupSamsung Research Center BeijingBeijingChina

Personalised recommendations