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Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis

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27 out of 50 users found this white paper useful


Publisher Microsoft
Publisher Registration N/A
Topics Date added 27 Aug 2003
Downloads 4 Format 88.5KB PDF

Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. The authors propose that a simple "Do-it-yourself" implementation of convolution with a flexible architecture is suitable for many visual document problems.

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