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RWTH Aachen
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Syn: Synthetic Dataset for Training UI Element Detector From Lo-Fi Sketches

Year 2020

User Interface design is an iterative process that progresses through low-, medium-, and high-fidelity prototypes. A few research projects use deep learning to automate this process by transforming low fidelity (lo-fi) sketches into front-end code. However, these research projects lack a large scale dataset of lo-fi sketches to train detection models. As a solution, we created Syn, a synthetic dataset containing 125,000 lo-fi sketches. These lo-fi sketches were synthetically generated using our UISketch dataset containing 5,917 sketches of 19 UI elements drawn by 350 participants. To realize the usage of Syn, we used it to train a UI element detector, Meta-Morph. It detects UI elements from a lo-fi sketch with 84.9% mAP and 72.7% AR. This work aims to support future research on UI element sketch detection and automating prototype fidelity transformation.

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Presented at

IUI '20, 2020 , Cagliari , IT.

Published in

Proc. 25th Intl. Conf. Intelligent User Interfaces (IUI 20, Cagliari, Italy) Companion , p. 79-80 .

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