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.
We're not able to analyze this paper right now due to high demand.
Please check back later (sorry!).
Generate a detailed summary of this paper with a premium account.
We ran into a problem analyzing this paper.