Auto-Icon: an automated code generation tool for icon designs assisting in UI development

Sidong Feng, Suyu Ma, Jinzhong Yu, Chunyang Chen, Ting Ting Zhou, Yankun Zhen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


Approximately 50% of development resources are devoted to UI development tasks [8]. Occupied a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this study, we define 100 icon classes through an iterative open coding for the existing icon design sharing website. Based on a deep learning model and computer vision methods, we propose an approach to automatically convert icon images to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon images, in terms of saving 65.2% developing time.

Original languageEnglish
Title of host publication26th International Conference on Intelligent User Interfaces, IUI 2021
EditorsBart Knijnenburg, John O’Donovan, Paul Teale
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450380171
Publication statusPublished - 2021
EventInternational Conference on Intelligent User Interfaces 2021: Where HCI Meets AI - Online, United States of America
Duration: 14 Apr 202117 Apr 2021
Conference number: 26th (Proceedings)


ConferenceInternational Conference on Intelligent User Interfaces 2021
Abbreviated titleIUI 2021
Country/TerritoryUnited States of America
Internet address


  • code accessibility
  • icon design
  • neural networks

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