Online Handwriting Recognition Using Multi Convolution Neural Networks

  • Dũng Việt Phạm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)


This paper presents a library written by C# language for the online handwriting recognition system using UNIPEN-online handwritten training set. The recognition engine based on convolution neural networks and yields recognition rates to 99% to MNIST training set, 97% to UNIPEN’s digit training set (1a), 89% to a collection of 44022 capital letters and digits (1a,1b) and 89% to lower case letters (1c). These networks are combined to create a larger system which can recognize 62 English characters and digits. A proposed handwriting segmentation algorithm is carried out which can extract sentences, words and characters from handwritten text. The characters then are given as the input to the network.


artificial intelligent convolution neural network UNIPEN pattern recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    LeCun, Y., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Simard, P.Y., Steinkraus, D., Platt, J.: Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 958–962. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  4. 4.
    Lauer, F., Suen, C.Y., Bloch, G.: A Trainable Feature Extractor for Handwritten Digit Recognition. Elsevier Science (February 2006)Google Scholar
  5. 5.
    Guyon, I., Schomaker, L., Plamondon, R., Liberman, R., Janet, S.: Unipen project of on-line data exchange and recognizer benchmarks. In: Proceedings of the 12th International Conference on Pattern Recognition, ICPR 1994, Jerusalem, Israel, pp. 29–33. IAPRIEEE (October 1994)Google Scholar
  6. 6.
    Vuurpijl, L., Niels, R., van Erp Nijmegen, M.: Verifying the UNIPEN devsetGoogle Scholar
  7. 7.
    Parizeau, M., Lemieux, A., Gagné, C.: Character Recognition Experiments using Unipen Data. In: Parizeau, et al. (eds.) Proc. of ICDAR 2001, Seatle, September 10-13 (2001)Google Scholar
  8. 8.
    List of publications by Dr. Yann LeCun,
  9. 9.
    O’Neill, M.: Neural Network for Recognition of Handwritten Digits,
  10. 10.
    Dung, P.V.: Neural Network for Recognition of Handwritten Digits in C#,
  11. 11.
    Dung, P.V.: Library for online handwriting recognition system using UNIPEN database,
  12. 12.
    Dung, P.V.: UPV – UNIPEN online handwriting recognition database viewer control,
  13. 13.
    Dung, P.V.: Large pattern recognition system using multi neural networks,
  14. 14.
    Modified NIST ("MNIST") database (11,594 KB total),
  15. 15.
    The UNIPEN Project,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dũng Việt Phạm
    • 1
  1. 1.Computer Network CentreVietnam Maritime UniversityHai Phong cityVietnam

Personalised recommendations