Gradient-Based Learning Applied to Document Recognition

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.

particularly when applied to NN’s, have played an increas-ingly important role in the design of pattern recognition systems. In fact, it could be argued that the availability of learning techniques has been a crucial factor in the recent success of pattern recognition applications such as continuous speech recognition and handwriting recognition.

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