Document layout detection: Datasets & Models

Manually annotating or hard-coding rules for document structure breaks down fast: forms, invoices, research papers, and financial reports all use different layouts, and rule-based parsers fail whenever a table shifts or a column count changes. Automating layout detection with a vision model means the system identifies tables, figures, and text blocks directly from the pixels, regardless of template, so OCR reads content in the correct reading order and tables aren't flattened into garbled text. For Document AI and OCR teams, this removes the brittle preprocessing step that otherwise dominates engineering time and error rates.

Detecting layout regions before extraction dramatically improves downstream OCR accuracy and preserves reading order, table integrity, and figure boundaries. The models run on standard page-image inputs and slot into existing document ingestion pipelines without new capture hardware. Because detection generalizes across templates, a single model scales to millions of pages spanning many document types.

1. Upload an image: Drop any photo into the widget above to try document layout detection for free, right on this page.
2. Preview instant results: The model detects text block, table, figure, title, list within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.