Stamp detection: Datasets & Models

In document AI and OCR pipelines, manually reviewing every page to confirm a required stamp or seal is present is slow, inconsistent, and does not scale across thousands of incoming documents. Automating stamp detection lets teams instantly verify approvals, catch missing or misplaced seals, and separate certified documents from drafts without human sorting. Because stamps often overlap text and vary in color, rotation, and clarity, a trained detection model handles the ambiguity far more reliably than template matching or simple color thresholds. This reduces compliance risk and frees reviewers to focus on genuine exceptions.

Teams gain consistent, high-throughput document verification that runs on existing scanners and capture systems, with detections that plug directly into OCR and approval workflows. RF-DETR provides strong accuracy on cluttered pages where stamps overlap text and vary in style, and the workflow scales from a handful of documents to large batch processing. Detected regions can be cropped for archival, redaction, or further classification.

1. Upload an image: Drop any photo into the widget above to try stamp detection for free, right on this page.
2. Preview instant results: The model detects rubber stamp, official seal, ink stamp, signature, date stamp 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.