  How can we learn, transfer and extract handwriting styles using deep neural
networks? This paper explores these questions using a deep conditioned
autoencoder on the IRON-OFF handwriting data-set. We perform three experiments
that systematically explore the quality of our style extraction procedure.
First, We compare our model to handwriting benchmarks using multidimensional
performance metrics. Second, we explore the quality of style transfer, i.e. how
the model performs on new, unseen writers. In both experiments, we improve the
metrics of state of the art methods by a large margin. Lastly, we analyze the
latent space of our model, and we see that it separates consistently writing
styles.
