WebUI-COCO-868: Annotated Webpage Screenshots for UI Region & Landmark Detection

Short description

868 desktop webpage screenshots annotated with COCO bounding boxes over ARIA-style landmark regions and common UI components (cookie dialogs, popovers, captcha, buttons, etc.)

Dataset details

• Modality: image (webpage screenshots)
• Annotation format: COCO-style JSON (bounding boxes + class IDs, optional semantic attributes)
• Number of images: 868
• Label taxonomy: ARIA landmark–inspired regions + UI-specific roles (see Label set below)
• Languages (in-page text): multilingual, reflecting the source domains (primarily English/Czech/Spanish/French/German/Portuguese)

Supported tasks

This dataset is intended primarily for:

• Object detection of semantic webpage regions and UI components (bounding boxes).
• Downstream uses include web layout parsing, UI understanding, and accessibility-oriented landmark detection.

Repository contents

The dataset is organized into splits with images and per-image JSONs, plus merged COCO files per split:

├── annotations/
│ ├── train_annotations.json
│ ├── val_annotations.json
│ └── test_annotations.json
├── train/
│ ├── *.png
│ └── annotations/
│ └── *.json
├── val/
│ ├── *.png
│ └── annotations/
│ └── *.json
├── test/
│ ├── *.png
│ └── annotations/
│ └── *.json
└── split_info.txt

• train/, val/, test/ contain PNG screenshots named like 123.png.
• train/annotations/, val/annotations/, test/annotations/ contain per-image annotation JSON files named like 123.json.
• annotations/*_annotations.json are the merged COCO annotation files for each split.
• split_info.txt documents how the split was produced (and/or which IDs belong to which split).

Annotation format

Annotations are stored in COCO-style JSON. Each annotation contains:

• bbox (x, y, width, height)
• category_id (class ID)
• (optional) additional semantic attributes, where applicable

Annotators were instructed to label UI components at semantically meaningful granularity:

• Composite widgets (e.g., dialogs) are treated as containers
• Controls (e.g., buttons) are labeled as individual entities

Label set (categories)

All classes included in this dataset:

• main
• navigation
• image
• contentinfo
• region
• generic-button
• banner
• complementary
• preferences-button
• popover
• close-button
• search
• cookie-dialog
• accept-button
• reject-button
• form
• captcha

Dataset creation

Collection methodology

The dataset was constructed to capture the visual and semantic structure of real-world webpages.

Screenshots were collected using a custom automation pipeline built atop nodriver (a wrapper for the Chrome DevTools Protocol), enabling fine-grained control over headless browser instances and pixel-accurate rendering.

Key collection details:

• Each session launched Chromium with Skia rendering to ensure consistent visual fidelity.
• Cookie consent modals and overlays were programmatically dismissed using text-based heuristics and synthetic clicks.
• Domain-specific handlers were used to bypass obstructive UI (e.g., paywalls/popups) and ensure content visibility.
• Full-page capture used a scroll-and-stitch approach:
  • Incremental scrolling with overlapping screenshots
  • Pinned headers/footers detected via pixel similarity to crop/align slices
  • Pages were optionally stitched offline or stored as individual tiles
• Request interception modified headers (e.g., User-Agent, Referer) and handled retry/abort logic to reduce failures on dynamic pages and bot protections.
• Screenshots were saved in timestamped directories per crawl session.

Source domains

Webpages were collected from a curated set of high-traffic international media domains to ensure diverse regional styles and layouts, including:

• Bloomberg
• Centrum
• El Cronista
• Financial Times
• Folha de S.Paulo
• Frankfurter Allgemeine Zeitung (Germany)
• Handelsblatt
• iRozhlas
• La Nación
• La Tribune
• Les Echos
• Lidovky
• Novinky
• Seznam
• Seznam Zprávy
• The Business Times
• The Straits Times
• The Wall Street Journal
• Wikipedia

Quality control

Annotation consistency was validated through:

• Automated heuristics (shape validation, area bounds, basic sanity checks)
• Manual review

Dataset metrics and diversity

Element distribution and imbalance

The dataset exhibits a long-tail class distribution:

• The most common class, region, occurs > 9000 times.
• Rare classes such as captcha and reject-button appear < 25 times.

This reflects real-world UI patterns: structural regions dominate the page, while edge-case elements are sparse.

Elements per image

• Mean labeled elements per image: 33.6
• Some images contain > 100 labeled elements
• Most images include ~7–11 unique UI types, indicating moderate layout diversity

Bounding box shape statistics

Bounding boxes cover a broad range of shapes. Most elements are horizontally elongated (wider than tall), consistent with common UI layouts (headers, toolbars, banners). The aspect ratio distribution is skewed toward wide and very wide elements.

Elements can be grouped by aspect ratio into:

• very tall
• tall
• square-ish
• wide
• very wide

Most elements fall into the very wide category.

Spatial layout patterns

UI elements are not uniformly distributed across the screen:

• concentration along typical layout bands (headers, sidebars, central content)
• clustering patterns consistent with real-world desktop webpage layouts

Intended use

This dataset is intended for research and development in:

• UI element detection on real-world webpages
• webpage semantic region detection (ARIA-style landmarks)
• layout analysis and structured understanding of web interfaces

Limitations

• Long-tail imbalance: rare UI elements (e.g., captcha, reject-button) have few examples.
• Domain coverage: the dataset focuses on a curated set of high-traffic media/information sites and may not generalize to all web genres (e-commerce, web apps, dashboards, etc.).
• Visual-only labels: annotations are based on screenshots. They do not include DOM structure or computed accessibility tree.

Legal and ethical considerations

• Screenshots contain content rendered from third-party websites. Ensure your dataset license and terms are compatible with redistribution of rendered pages and any embedded media.
• The crawling pipeline included logic to dismiss overlays and bypass obstructive UI to capture visible content. Users should consider whether this affects allowed redistribution and use.
• The dataset may include incidental personal data if present on captured pages (e.g., author names, user prompts, etc.). Review and redact if needed.

License

The dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0).

Citation

If you use this dataset, please cite:

@dataset{webui_coco_868,
  title     = {WebUI-COCO-868: Annotated Webpage Screenshots for UI Region and Landmark Detection},
  author    = {Lukáš Jílek},
  year      = {2026},
  url       = {https://huggingface.co/datasets/jileklu/WebUI-COCO-868}
}

## Contact
For questions, issues, or takedown requests, please contact:
Name: Lukáš Jílek
Email: [email protected]
Affiliation: Faculty of Information Technology, Czech Technical University in Prague