Table extraction: Datasets & Models

Manually transcribing tabular data from invoices, financial filings, lab reports, and scanned records is slow, error-prone, and does not scale as document volume grows. Generic OCR run across a full page frequently scrambles multi-column data because it has no concept of table boundaries, so extracted values land in the wrong cells. A detection model trained for table extraction isolates each table region first, giving your parser clean, correctly scoped input and dramatically reducing the cleanup work on the extracted output. This is especially valuable in document AI and OCR settings where accuracy on structured data directly affects downstream automation.

Accurate table region detection improves the reliability of every step that follows it in an OCR pipeline, cutting manual correction and speeding up document processing. The workflow runs on standard document image inputs, so it slots into existing ingestion systems and PDF-to-image preprocessing without new capture hardware. Using RF-DETR, the approach scales from a handful of forms to high-volume batch processing while adapting to diverse layouts across industries.

1. Upload an image: Drop any photo into the widget above to try table extraction for free, right on this page.
2. Preview instant results: The model detects table, table row, table column, table cell, header row 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.