  The placement of text over an image is an important part of producing
high-quality visual designs. Automating this work by determining appropriate
position, orientation, and style for textual elements requires understanding
the contents of the background image. We refer to the search for aesthetic
parameters of text rendered over images as "copyspace detection", noting that
this task is distinct from foreground-background separation. We have developed
solutions using one and two stage object detection methodologies trained on an
expertly labeled data. This workshop will examine such algorithms for copyspace
detection and demonstrate their application in generative design models and
pipelines such as Einstein Designer.
