Style Transfer and Extraction for the Handwritten Letters Using Deep Learning
This work addresses style manipulation in handwritten letters, which is incremental as it builds on existing methods with specific gains.
The paper tackled the problem of learning, transferring, and extracting handwriting styles using a deep conditioned autoencoder on the IRON-OFF dataset, achieving improvements over state-of-the-art methods in style extraction and transfer experiments.
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.