Vinoth Pandian Sermuga Pandian
RWTH Aachen University, Aachen, NRW, Germany
This paper contributes the first large-scale dataset of 17,979 hand-drawn sketches of 21 UI element categories collected from 967 participants, including UI/UX designers, front-end developers, HCI, and CS grad students, from 10 different countries. We performed a perceptual study with this dataset and found out that UI/UX designers can recognize the UI element sketches with ~96% accuracy. To compare human performance against computational recognition methods, we trained the state-of-the-art DNN-based image classification models to recognize the UI elements sketches. This study revealed that the ResNet-152 model outperforms other classification networks and detects unknown UI element sketches with 91.77% accuracy (chance is 4.76%). We have open-sourced the entire dataset of UI element sketches to the community intending to pave the way for further research in utilizing AI to assist the conversion of lo-fi UI sketches to higher fidelities.
https://doi.org/10.1145/3411764.3445784
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)