About this Journal Submit a Manuscript Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 834127, 10 pages
http://dx.doi.org/10.5402/2012/834127
Research Article

On the Brittleness of Handwritten Digit Recognition Models

Seewald Solutions, Leitermayergasse 33, 1180 Vienna, Austria

Received 19 July 2011; Accepted 7 September 2011

Academic Editor: A. Torsello

Copyright © 2012 Alexander K. Seewald. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. K. Seewald, “Digits—a dataset for handwritten digit recognition,” Österreichisches Forschungsinstitut für Artificial Intelligence TR-2005-27, Tec. Rep., Wien, 2005.
  2. P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR '03), 2003.
  3. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
  4. M. O'Neill, Neural Network for Recognition of Handwritten Digits, Code Project, 2006.
  5. L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, “Handwritten digit recognition: benchmarking of state-of-the-art techniques,” Pattern Recognition, vol. 36, no. 10, pp. 2271–2285, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Liu and H. Fujisawa, “Classification and learning for character recognition: comparison of methods and remaining problems,” in Proceedings of the International Workshop on Neural Networks and Learning in Document Analysis and Recognition, Seoul, Korea, 2005.
  7. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning, Springer, Heidelberg, Germany, 2003.
  8. J. H. Hull, “A database for handwritten text recognition research,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 550–554, 1994. View at Publisher · View at Google Scholar
  9. H. W. Ian and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2005.
  10. D. Keysers, W. Macherey, H. Ney, and J. Dahmen, “Adaptation in statistical pattern recognition using tangent vectors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 269–274, 2004.
  11. J. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, Eds., MIT Press, Cambridge, Mass, USA, 1998.