  User Interface (UI) prototyping is a necessary step in the early stages of
application development. Transforming sketches of a Graphical User Interface
(UI) into a coded UI application is an uninspired but time-consuming task
performed by a UI designer. An automated system that can replace human efforts
for straightforward implementation of UI designs will greatly speed up this
procedure. The works that propose such a system primarily focus on using UI
wireframes as input rather than hand-drawn sketches. In this paper, we put
forward a novel approach wherein we employ a Deep Neural Network that is
trained on our custom database of such sketches to detect UI elements in the
input sketch. Detection of objects in sketches is a peculiar visual recognition
task that requires a specific solution that our deep neural network model
attempts to provide. The output from the network is a platform-independent UI
representation object. The UI representation object is a dictionary of
key-value pairs to represent the UI elements recognized along with their
properties. This is further consumed by our UI parser which creates code for
different platforms. The intrinsic platform-independence allows the model to
create a UI prototype for multiple platforms with single training. This
two-step approach without the need for two trained models improves over other
methods giving time-efficient results (average time: 129 ms) with good
accuracy.
