  The recent success of large vision language models shows great potential in
driving the agent system operating on user interfaces. However, we argue that
the power multimodal models like GPT-4V as a general agent on multiple
operating systems across different applications is largely underestimated due
to the lack of a robust screen parsing technique capable of: 1) reliably
identifying interactable icons within the user interface, and 2) understanding
the semantics of various elements in a screenshot and accurately associate the
intended action with the corresponding region on the screen. To fill these
gaps, we introduce \textsc{OmniParser}, a comprehensive method for parsing user
interface screenshots into structured elements, which significantly enhances
the ability of GPT-4V to generate actions that can be accurately grounded in
the corresponding regions of the interface. We first curated an interactable
icon detection dataset using popular webpages and an icon description dataset.
These datasets were utilized to fine-tune specialized models: a detection model
to parse interactable regions on the screen and a caption model to extract the
functional semantics of the detected elements. \textsc{OmniParser}
significantly improves GPT-4V's performance on ScreenSpot benchmark. And on
Mind2Web and AITW benchmark, \textsc{OmniParser} with screenshot only input
outperforms the GPT-4V baselines requiring additional information outside of
screenshot.
