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.

本文提出了两种途径以自动化用户界面模板代码开发，一种是使用传统的计算机视觉技术，另一种则运用深度语义分割网络的新应用。通过我们设计的新评估框架的评估表明，我们的深度学习方法优于传统的计算机视觉方法，我们得出结论：深度学习是未来研究的最有前途的方向。