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Convolution Nets - Brigham Young University

Gradient-Based Learning Appliedto Document RecognitionYANN LECUN,MEMBER, IEEE,L EON BOTTOU, YOSHUA BENGIO,ANDPATRICK HAFFNERI nvited PaperMultilayer neural networks trained with the back-propagationalgorithm constitute the best example of a successful gradient-based learning technique. Given an appropriate networkarchitecture, gradient-based learning algorithms can be usedto synthesize a complex decision surface that can classifyhigh-dimensional patterns, such as handwritten characters, withminimal preprocessing. This paper reviews various methodsapplied to handwritten character recognition and compares themon a standard handwritten digit recognition task. Convolutionalneural networks, which are specifically designed to deal withthe variability of two dimensional (2-D) shapes, are shown tooutperform all other document recognition systems are composed of multiplemodules including field extraction, segmentation, recognition,and language modeling.

Gradient-Based Learning Applied to Document Recognition YANN LECUN, MEMBER, IEEE, LEON BOTTOU, YOSHUA BENGIO,´ AND PATRICK HAFFNER Invited Paper Multilayer neural networks trained with the back-propagation algorithm constitute the best example of …

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