A pretrained boarding pass formats classifier that sorts an image into one of 10 categories — the type of boarding pass format it is. Use the boarding pass formats 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 20 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": "A4",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 boarding pass formats 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 use case involves using the boarding pass formats identifier to automate the verification of boarding passes during online check-in. Airlines can streamline the check-in process by ensuring that only valid boarding pass formats are accepted, reducing fraud and errors associated with manual checks.
Airline mobile applications can integrate this function to validate the boarding pass format at the point of scanning. By providing real-time feedback to users, the app can improve user experience and minimize the frustration often associated with incorrect boarding pass formats.
Airports and airlines can employ this identifier to enforce compliance with security protocols during security screenings. By validating the format of boarding passes, organizations can prevent potential issues with unauthorized or incorrectly formatted documents, enhancing overall security.
By analyzing the types of boarding pass formats being presented by passengers, airlines can gather insights for data analytics purposes. This information can help them identify trends, improve service delivery, and optimize their document management systems based on the most commonly used formats.
The function can be integrated into fraud detection systems to identify irregularities in boarding pass formats. By flagging boarding passes that do not match expected formats, airlines can enhance their fraud prevention strategies and reduce losses associated with ticketing fraud.
Customer support systems can use the boarding pass formats identifier to automate initial queries related to boarding pass issues. This can help triage customer inquiries more effectively, directing passengers with formatting issues to relevant resolutions without the need for live agent intervention.
In a global aviation landscape, this function can help ensure that all boarding passes comply with international formats and standards. By implementing the boarding pass formats identifier, airlines can conduct audits of their boarding pass systems to ensure consistency and compliance with industry norms.
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 boarding pass formats 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.