Multilayer neural networks trained with the back-propagation
algorithm constitute the best example of a successful gradientbased
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 characters, with
minimal preprocessing. This paper reviews various methods
applied to handwritten character recognition and compares them
on a standard handwritten digit recognition task. Convolutional
neural networks, which are specifically designed to deal with
the variability of two dimensional (2-D) shapes, are shown to
outperform all other techniques.
Real-life document recognition systems are composed of multiple
modules including field extraction, segmentation, recognition,
and language modeling. A new learning paradigm, called graph
transformer networks (GTN’s), allows such multimodule systems
to be trained globally using gradient-based methods so as to
minimize an overall performance measure.
Two systems for online handwriting recognition are described.
Experiments demonstrate the advantage of global training, and
the flexibility of graph transformer networks.
A graph transformer network for reading a bank check is
also described. It uses convolutional neural network character
recognizers combined with global training techniques to provide
record accuracy on business and personal checks. It is deployed
commercially and reads several million checks per day.
