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Style Transfer and Extraction for the Handwritten Letters Using Deep Learning by Mohammed, Omar; Bailly, Gerard; Pellier, Damien is a scholarly article available to read on EtoBox.

What is Style Transfer and Extraction for the Handwritten Letters Using Deep Learning about?

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.

Author

Mohammed, Omar; Bailly, Gerard; Pellier, Damien

Published

2018

Language

EN