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Best practices for convolutional neural networks applied to visual document analysis
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 flexible architecture is suitable formany visual document problems. This simpleconvolutional neural network does not require complexmethods, such as momentum, weight decay, structure-dependentlearning rates, averaging layers, tangent prop,or even finely-tuning the architecture. The end result is avery simple yet general architecture which can yieldstate-of-the-art performance for document analysis. Weillustrate our claims on the MNIST set of English digitimages.
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Metadata
- Authors
- Simard, P. Y., Steinkraus, D., & Platt, J. C.
- Venue
- Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2005-04-12
- Volume
- 1
- Pages
- 958-963
- ISSN
- 1520-5363 (ISSN-L)
- OpenAlex
- W2156163116
- Cited by
- 2,741
- References
- 9
- FWCI
- 22.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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