Automatic handwriting recognition is of academic and commercial interest. Current
algorithms already excel at learning to recognize handwritten digits. Post offices use
them to sort letters; banks use them to read personal checks. Some predict that in the near future
billions of handheld devices such as cell phones will have handwriting recognition capabilities.
In recent decades neural networks
have been overshadowed
by the very useful but principally less general and less powerful support vector machines
as well as other more specialized machine learning methods.
Our new state-of-the-art results
herald a rennaissance of neural networks.
Neither our fast deep nets nor our recurrent nets (also deep by nature)
are limited to handwriting. They yield best known results on
many
visual and other pattern recognition tasks.
Selected Publications
[11]
D. C. Ciresan, J. Schmidhuber.
Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification. Preprint
arXiv:1309.0261, 1 Sep 2013.
[10]
D. C. Ciresan, U. Meier, J. Schmidhuber.
Multi-column Deep Neural Networks for Image Classification.
IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012.
PDF.
ArXiv Preprint
arXiv:1202.2745v1 [cs.CV], Feb 2012.
[9]
U. Meier, D. C. Ciresan, L. M. Gambardella, J. Schmidhuber.
Better Digit Recognition with a Committee of Simple Neural Nets.
11th International Conference on Document Analysis and Recognition (ICDAR 2011),
Beijing, China, 2011. PDF.
[8]
D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber.
Convolutional Neural Network Committees For Handwritten Character Classification.
11th International Conference on Document Analysis and Recognition (ICDAR 2011),
Beijing, China, 2011. PDF.
[7a]
D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber.
Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs.
ArXiv Preprint
arXiv:1103.4487v1 [cs.LG], 23 Mar 2011.
[7]
D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber.
Flexible, High Performance Convolutional Neural Networks for Image Classification.
International Joint Conference on Artificial Intelligence (IJCAI-2011, Barcelona), 2011.
ArXiv preprint, 1 Feb 2011.
[6]
D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber.
Deep Big Simple Neural Nets For Handwritten Digit Recognition.
Neural Computation 22(12): 3207-3220, 2010.
ArXiv Preprint.
[5]
A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber.
A Novel Connectionist System for Improved Unconstrained
Handwriting Recognition.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 31, no. 5, 2009. PDF.
[4]
A. Graves, J. Schmidhuber.
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.
Advances in Neural Information Processing Systems 22, NIPS'22, p 545-552,
Vancouver, MIT Press, 2009.
PDF.
[3]
A. Graves, S. Fernandez, M. Liwicki, H. Bunke, J. Schmidhuber.
Unconstrained online handwriting recognition with recurrent neural networks.
Advances in Neural Information Processing Systems 21, NIPS'21,
p 577-584, 2008, MIT Press,
Cambridge, MA, 2008.
PDF.
[2]
M. Liwicki, A. Graves, H. Bunke, J. Schmidhuber. A novel approach
to on-line handwriting recognition based on bidirectional
Long Short-Term Memory networks. 9th International Conference
on Document Analysis and Recognition, 2007.
PDF.
[1]
S. Hochreiter and J. Schmidhuber.
Long Short-Term Memory.
Neural Computation, 9(8):1735-1780, 1997.
PDF.
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