Convolutional Neural Network Committees for Handwritten Character Classification
- 1 September 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1135-1139
- https://doi.org/10.1109/icdar.2011.229
Abstract
In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.Keywords
This publication has 20 references indexed in Scilit:
- A committee of neural networks for traffic sign classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Deep, Big, Simple Neural Nets for Handwritten Digit RecognitionNeural Computation, 2010
- What is the best multi-stage architecture for object recognition?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Convolutional deep belief networks for scalable unsupervised learning of hierarchical representationsPublished by Association for Computing Machinery (ACM) ,2009
- Estimating accurate multi-class probabilities with support vector machinesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Best practices for convolutional neural networks applied to visual document analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Machine and human recognition of segmented characters from handwritten wordsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Optimal linear combination of neural networks for improving classification performanceIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- Optimal Linear Combinations of Neural NetworksNeural Networks, 1997
- Bagging predictorsMachine Learning, 1996