Copyspace: Where to Write on Images?
This work tackles the problem of automating text placement on images for designers and generative design models, offering an incremental improvement in visual design automation.
This paper addresses the problem of automatically placing text on images to create high-quality visual designs. The authors developed solutions using one and two-stage object detection methodologies trained on expertly labeled data to determine appropriate position, orientation, and style for textual elements.
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.