- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Submit a Manuscript
- Subscription Information
- Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 834127, 10 pages
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.
- A. K. Seewald, “Digits—a dataset for handwritten digit recognition,” Österreichisches Forschungsinstitut für Artificial Intelligence TR-2005-27, Tec. Rep., Wien, 2005.
- 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.
- 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.
- M. O'Neill, Neural Network for Recognition of Handwritten Digits, Code Project, 2006.
- 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.
- 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.
- T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning, Springer, Heidelberg, Germany, 2003.
- 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.
- H. W. Ian and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2005.
- 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.
- 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.