  This paper shows how Long Short-term Memory recurrent neural networks can be
used to generate complex sequences with long-range structure, simply by
predicting one data point at a time. The approach is demonstrated for text
(where the data are discrete) and online handwriting (where the data are
real-valued). It is then extended to handwriting synthesis by allowing the
network to condition its predictions on a text sequence. The resulting system
is able to generate highly realistic cursive handwriting in a wide variety of
styles.
