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arXiv · 1204.3968

Convolutional Neural Networks Applied to House Numbers Digit Classification

Pierre Sermanet·Soumith Chintala·Yann LeCun
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Abstract

We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.

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Pierre Sermanet, Soumith Chintala, Yann LeCun. 2012-04-18. Convolutional Neural Networks Applied to House Numbers Digit Classification. https://arxiv.org/abs/1204.3968

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