Copyspace: Where to Write on Images?

TL;DR

This paper addresses the challenge of automatically detecting optimal regions for text placement on images, crucial for high-quality visual design, by developing object detection-based algorithms trained on labeled data.

Contribution

It introduces novel copyspace detection algorithms using one and two stage object detection methods, tailored for aesthetic text placement over images.

Findings

Algorithms effectively identify suitable text regions.

Demonstrated integration with generative design pipelines.

Improved automation in visual content creation.

Abstract

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.