A pretrained if drivers license number is in a scan classifier that sorts text into one of 2 categories. Use the if drivers license number is in a scan API immediately, no training required, then adapt it to your own data when you need more.
Drop in some text and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "The text you want to classify"},
)
print(response.json())Example response
{
"labelName": "Contains Drivers License Number",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if drivers license number is in a scan categories, served on Nyckel's own infrastructure — your text snippet stays on Nyckel.
Send raw text to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
This use case involves scanning documents for driver’s license numbers to identify potential fraudulent activities. By flagging documents that contain such information without proper justification, organizations can mitigate risks associated with identity theft and unauthorized access.
Companies in regulated industries can utilize this classification function to ensure that driver’s license numbers are only present in authorized documents. This automated scanning process can streamline compliance audits and reduce the likelihood of human error in document review.
Financial institutions can implement this functionality to quickly verify customer identities during account opening. By ensuring that a legitimate driver's license number is scanned, they can enhance security measures and accelerate customer onboarding processes.
In insurance companies, this function can be used to identify driver’s license numbers in claims documents. It helps ensure that claims are processed for valid policyholders, reducing the chances of fraudulent claims and assisting in risk assessment.
Law enforcement agencies can employ this text classification in managing various records and reports. By automatically identifying driver’s license numbers in investigations, agencies can enhance their data management efforts and improve case tracking capabilities.
Ride-sharing platforms can use this technology to scan drivers’ submitted documentation to confirm that they hold valid licenses. This function helps maintain safety standards and regulatory compliance for drivers operating within the platform.
City or state safety departments can use this classification tool to analyze scanned documents for driver’s license numbers during traffic stops or inspections. This helps to ensure that drivers on the road are licensed, thereby promoting public safety and reducing illegal driving incidents.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of text samples in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if drivers license number is in a scan classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.