articleApr 12, 2005Closed access

Best practices for convolutional neural networks applied to visual document analysis

Microsoft (United States)

Indexed incrossref

Abstract

Neural networks are a powerful technology forclassification of visual inputs arising from documents.However, there is a confusing plethora of different neuralnetwork methods that are used in the literature and inindustry. This paper describes a set of concrete bestpractices that document analysis researchers can use toget good results with neural networks. The mostimportant practice is getting a training set as large aspossible: we expand the training set by adding a newform of distorted data. The next most important practiceis that convolutional neural networks are better suited forvisual document tasks than fully connected networks. Wepropose that a simple do-it-yourself implementation ofconvolution with a…

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Authors

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Topics & keywords

Keywords
  • MNIST database
  • Convolutional neural network
  • Computer science
  • Set (abstract data type)
  • Simple (philosophy)
  • Architecture
  • Artificial neural network
  • Artificial intelligence
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