A pretrained check scan quality classifier that sorts an image into one of 10 categories — the quality of the scanned document. Use the check scan quality API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 10 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": "https://example.com/photo.jpg"}'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": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Acceptable Quality",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 check scan quality categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file 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 function can be integrated into scanning services to automatically evaluate the quality of scanned images. By identifying false images, businesses can ensure that only high-quality scans are processed and delivered to clients, reducing the risk of errors in documentation.
In the context of document management systems, the function can classify scanned images to identify low-quality or false scans. This allows organizations to streamline their processing workflow by filtering out unusable images and focusing on high-quality content for data extraction.
When scanning large volumes of documents for archiving, this function can help assess the quality of scans in real-time. By automatically flagging false images, businesses can enhance their archiving processes and ensure that only usable materials are stored, saving both time and storage resources.
In industries where compliance is crucial, such as healthcare and finance, this function can assist in verifying the quality of scanned documents. By identifying false images, businesses can uphold regulatory standards and ensure that all documentation meets necessary criteria for accuracy and legibility.
Customer service teams can utilize the false image classification function to verify scanned documents submitted by clients. By automatically filtering out low-quality submissions, the team can expedite the review process and provide faster resolutions to customer inquiries.
In printing environments, this function can be employed to assess the quality of scanned images before they are sent to the print queue. Identifying false images ensures that only high-quality prints are produced, minimizing waste and improving overall print accuracy.
Businesses can leverage the false image classification function to collect data on scanning errors and improve the underlying scanning and image capture technologies. By understanding the types of false images generated, organizations can refine their systems to reduce occurrence and enhance image quality over time.
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 images 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 check scan quality 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.