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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

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

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    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.

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    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 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/json
    Sample 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/json
    Sample output data

    End User License Agreement

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    Support Information

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    Refund Policy

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