  An early stage of developing user-facing applications is creating a wireframe
to layout the interface. Once a wireframe has been created it is given to a
developer to implement in code. Developing boiler plate user interface code is
time consuming work but still requires an experienced developer. In this
dissertation we present two approaches which automates this process, one using
classical computer vision techniques, and another using a novel application of
deep semantic segmentation networks. We release a dataset of websites which can
be used to train and evaluate these approaches. Further, we have designed a
novel evaluation framework which allows empirical evaluation by creating
synthetic sketches. Our evaluation illustrates that our deep learning approach
outperforms our classical computer vision approach and we conclude that deep
learning is the most promising direction for future research.
