Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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Passport Verification Free trial
By:
Latest Version:
1.1.2
As part of trevo's KYC service, this model is built for passport information extraction and further verification with face segment output.
Product Overview
With trevo's Passport Data Page Detection API, you can now have automated Know Your Customer (KYC) capabilities at your fingertips. trevo's Passport Verification API uses neural networks to verify the face image and personal particulars on the passport page and can extract these personal particulars to automate cumbersome form-filling processes. With the additional face location output, you could save the face photo from the passport image easily, and use it for further verification, for example, verifying the passport image to customer's live selfie for KYC purpose. This robust API can be integrated to web or mobile applications easily and perform verification checks at scale.
Key Data
Version
By
Type
Model Package
Highlights
Deep Learning: our API is powered by our proprietary deep learning engine to detect face existence and our accuracy undergoes continuous improvement as we keep training our model with more data input;
Smooth User Experience: easy-to-use API which can be integrated to any web application like chatbots or onboarding screens for instant access to our service;
Trusted by Leading Brands: Trevo's digital identity products are used by leading hospitality brands all over the world such as the Sands China Group and Amara Hotel.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.22/inference
running on any instance
Model Batch Transform$0.22/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.056/host/hr
running on ml.t2.medium
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
About Free trial
Try this product for 14 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
Additional infrastructure cost, taxes or fees may apply.
Usage Information
Model input and output details
Input
Summary
Provide a form-data or application/json file to the endpoint. Parameters you can tune:
- image or image_bytes: Field for your image, either as a file or base64-encoded bytestring. An error will be thrown if neither is provided.
- auto_rotate: Optional. Set to True if uploading your passport at 90/180/270 degrees. A suggested rotation degree will be returned.
- skip_face_detect: Optional. Set to True to skip face detection for the passport. Refer to the sample notebook for more details.
Limitations for input type
In general, the requirements are:
1. The image size cannot be too small to ensure that passport mrz character is clear;
2. Passport must be the largest object in the image;
3. The image is taken with a clear background with no text in the background.
Input MIME type
application/jsonSample input data
Output
Summary
Output Summary:
- face_segment: Base64 encoded face segment in the passport.
- result:
- Type and MRZ type, Country, Nationality
- Name, Date of Birth, Sex
- Expiration Date, Document Number, Personal Number, Expiry Status
- validation_score:
- Composite, Date of Birth, Expiration Date, Number, Personal Number scores
- Overall validity and score
- raw_text: Extracted MRZ text
- rotate_angle: Suggested rotation angle
- success: Scan success status
- error_message: Error details.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Passport Verification
Please contact frosty.trevo@gmail.com for technical questions.
AWS Infrastructure
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Learn MoreRefund Policy
This product is not refundable.
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