PROC OF THE IEEE NOVEMBER Gradient Based Learning Applied to Document Recognition

Multilayer Neural Networks trained with the backpropa gation algorithm constitute the best example of a successful Gradient Based Learning technique Given an appropriate network architecture Gradient Based Learning algorithms can be used to synthesize a complex decision surface that can classify high dimensional patterns such as handwritten char acters with minimal preprocessing This paper reviews var ious methods applied to handwritten character recognition and compares them on a standard handwritten digit recog nition task Convolutional Neural Networks that are specif ically designed to deal with the variability of D shapes are shown to outperform all other techniques Real life document recognition systems are composed of multiple modules including eld extraction segmenta tion recognition and language modeling A new learning paradigm called Graph Transformer Networks GTN al lows such multi module systems to be trained globally using Gradient Based methods so as to minimize an overall per formance measure Two systems for on line handwriting recognition are de scribed Experiments demonstrate the advantage of global training and the exibility of Graph Transformer Networks A Graph Transformer Network for reading bank check is also described It uses Convolutional Neural Network char acter recognizers combined with global training techniques to provides record accuracy on business and personal checks It is deployed commercially and reads several million checks per day