Signature detection: Datasets & Models
Locate handwritten signatures within scanned documents, contracts, and forms using object detection. Returns bounding boxes around each signature so downstream systems can crop, verify, or route them.
Signature detection
This signature detection workflow runs an object detection model over document images to find and localize handwritten signatures, returning bounding box coordinates for each one it detects. It accepts scanned pages, contracts, PDFs converted to images, or photographed forms as input, and a signature dataset trained for this task helps separate real signatures from printed text, stamps, and initials.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate signature detection
Manually reviewing thousands of documents to confirm whether they were signed, or to crop signatures for verification, is slow, error-prone, and does not scale across high-volume Document AI and OCR pipelines. A vision model automates this triage step, flagging unsigned documents, extracting signature regions for downstream comparison, and feeding clean crops into OCR or authentication systems. This removes a tedious human bottleneck in contract processing, loan onboarding, and compliance workflows while improving consistency.
Key benefits
Automated signature detection integrates directly into existing document ingestion pipelines, processing scanned files at scale without adding manual review headcount. Accurate bounding boxes let you crop, verify, and audit signatures automatically, and the RF-DETR architecture delivers strong detection performance on dense, cluttered document layouts. You can fine-tune on your own signature dataset to adapt to specific forms, languages, and scan quality.
How to use it
- Upload an image: Drop any photo into the widget above to try signature detection for free, right on this page.
- Preview instant results: The model detects signature, initials, stamp, printed text, checkbox 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 signature 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 signature detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Signature Detection project to Roboflow Agent.