  Texts, widgets, and images on a UI page do not work separately. Instead, they
are partitioned into groups to achieve certain interaction functions or visual
information. Existing studies on UI elements grouping mainly focus on a
specific single UI-related software engineering task, and their groups vary in
appearance and function. In this case, we propose our semantic component groups
that pack adjacent text and non-text elements with similar semantics. In
contrast to those task-oriented grouping methods, our semantic component group
can be adopted for multiple UI-related software tasks, such as retrieving UI
perceptual groups, improving code structure for automatic UI-to-code
generation, and generating accessibility data for screen readers. To recognize
semantic component groups on a UI page, we propose a robust, deep
learning-based vision detector, UISCGD, which extends the SOTA deformable-DETR
by incorporating UI element color representation and a learned prior on group
distribution. The model is trained on our UI screenshots dataset of 1988 mobile
GUIs from more than 200 apps in both iOS and Android platforms. The evaluation
shows that our UISCGD achieves 6.1\% better than the best baseline algorithm
and 5.4 \% better than deformable-DETR in which it is based.
