Table detection: Datasets & Models
Table detection locates tables within document images and PDFs so their structure can be extracted downstream. It draws bounding boxes around tabular regions to separate them from paragraphs, headers, and figures.
Table detection
This detection workflow locates tabular regions within document images, taking scanned pages, PDFs, invoices, forms, and reports as input. Trained on a table dataset, it returns bounding boxes around each table so downstream steps can extract structure while ignoring paragraphs, headers, and figures.
Try it out
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Why automate table detection
Manually locating tables across thousands of pages is slow, error-prone, and doesn't scale when documents arrive in varied formats and quality. Rule-based parsers break on rotated scans, borderless tables, and multi-column layouts, forcing constant maintenance. A detection model trained on a representative table dataset handles these variations and reliably feeds clean regions into OCR and structure-extraction stages. For teams processing financial statements, medical records, or regulatory filings, this turns a tedious data-entry bottleneck into an automated step.
Key benefits
Automated table detection cuts the manual effort of flagging tabular regions and improves the accuracy of downstream extraction by giving OCR precise areas to focus on. It runs on the document pipelines and batch-processing infrastructure you already have, and scales from a handful of pages to millions without added headcount. Training on RF-DETR gives you strong detection accuracy on dense, varied document layouts.
How to use it
- Upload an image: Drop any photo into the widget above to try table detection for free, right on this page.
- Preview instant results: The model detects table, table row, table cell, table header, text block 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.
- 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.
- 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.
Recommended model for table detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for table detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Table Detection project to Roboflow Agent.