Identity Document Verification

Proven Track Record of Trusted Remote Identity Verification Projects

Finance Customer onboarding

System integrators eKYC

Finance Customer onboarding

Telco SIM registration

Finance Customer onboarding

Telco SIM registration

System integrators Customer onboarding

Digital security Digital certificates and e-signatures

Finance Banking as a Service

System integrators Digital security

Finance ID data extraction

System integrators Customer onboarding

System integrators SIM registration

System integrators ID data extraction

Digital Security Onboarding as a service

System integrators Insurance registration

System integrators Attendance

In order to get accurate data from a user’s ID during remote onboarding, our AI-powered identity document verification SDK captures, reads, and cross-checks the data from an ID in milliseconds.

To build trust in the onboarding process, our neural networks are also trained to detect distinct document fraud attempts.

Identity Document Verification with Seamless Auto-Capture Component

For users to have a comfortable experience during the remote onboarding process, mobile and web auto-capture components of our Digital Onboarding Toolkit guide them to eliminate image capture flaws and make sure to take a photo of their ID with the best possible quality for OCR.

Document auto-capture significantly reduces image rejection due to low quality or unreadable pictures of IDs
Seamless user experience of the document verification process increases conversion rates
World’s smallest auto-capture SDK

See documentation

AI-Powered OCR & Automated ID Classification

Innovatrics AI-powered OCR technology works accurately and reliably, supporting a wide range of alphabets. Once a picture of an ID is taken, our SDK:

Automatically identifies the identity document type (passports, ID cards, driving licenses, foreigner permanent residence cards), edition, and issuing country
Extracts all required fields of the document and parses them into text
Supports Latin, Cyrillic, Arabic, Thai, traditional Chinese, and Bengali scripts
Extracts the portrait image of the document holder and converts it into a secure proprietary biometric template for biometric verification

See documentation

Innovatrics identity document verification - data extraction in Arabic

Document Authenticity Validation

When onboarding new clients remotely, it is crucial to check if the document has not been forged or altered. The document processing components of our Digital Onboarding Toolkit comprise several technologies to validate the authenticity of the document:

NFC Reading & Verification

Using the Near Field Communication channel (NFC), any identity document with the embedded security chip can be verified, assuring 100% proof of the document’s authenticity.

We have developed a mobile NFC library with all the security algorithms to read the data structures from the standard document types, such as passports and IDs (TD1, TD2, and TD3):

First, our components capture the ID side containing the MRZ zone,
Our on-device OCR technology extracts the unlock code from MRZ enabling the reading of data stored in the security chip,
The data is read in a highly secure and structured way, allowing further processing
Authenticity of the document is verified both for the data (to confirm the data have not been modified) and the chip (to verify the genuineness of the document)

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Document Liveness

Document liveness technology helps to verify a person’s identity document in real time. Our algorithms can detect if the picture of the ID presented during the identity verification process was physically present or taken from a screen.

Regardless if it is a tablet, smartphone, PC, or TV screen, the technology can detect various screen attacks from any device without complicating the user journey.

Innovatrics Identity document verification - authenticity validation

Biometric Verification and Data Consistency Check

To validate the data extracted from the ID by OCR, our identity document verification SDK reads the data from machine-readable zones (MRZ) of the ID to allow data cross-checks, including the check digit validation.

Our face algorithm compares a holder’s photo on the ID with a selfie to biometrically verify that the ID belongs to the same person
Age and gender of a user are validated against the age and gender estimated from the selfie
Validation of the expiry date of the ID is performed
Data in MRZ is cross-checked against the data extracted by OCR
Data in QR code and barcode is cross-checked against the data extracted by OCR
Color profile authenticity of the document is evaluated to detect fraud attempts

See documentation