Turn identity documents into structured spreadsheet data using AI that reads driver licenses, passports, and ID cards from any country or jurisdiction
Every business that verifies customer or employee identity faces the same manual step: someone looks at a driver license, passport, or ID card and types the information into a system. A bank opens a new account and a teller manually keys the customer's name, address, date of birth, and ID number from their driver license into the core banking system. A property management company onboards a new tenant and an office assistant types the same fields from the tenant's ID into the lease management software. A healthcare practice registers a new patient and a front desk staff member copies insurance card and photo ID details into the electronic health record. Each of these interactions takes 2 to 5 minutes of manual transcription, introduces a risk of keystroke errors in critical fields like ID numbers and dates of birth, and creates a poor first impression during what should be a smooth onboarding experience.
The volume compounds the problem. A car rental counter processes 50 to 100 driver licenses per day. An event venue checks IDs at the door for hundreds of attendees. A staffing agency onboards dozens of new workers per week, each requiring I-9 identity verification. A university registrar processes thousands of student IDs at enrollment time. At these volumes, manual data entry from identity documents becomes a dedicated job function — or more commonly, a task distributed across front-line staff who are also responsible for customer service, creating a tension between thorough data capture and efficient processing.
Lido applies AI-powered OCR to identity documents, extracting every field from driver licenses, passports, and ID cards into structured spreadsheet data. The AI reads the document layout, identifies name, date of birth, address, ID number, expiration date, and document class, and validates the extracted data against format rules and barcode cross-references. Combine ID extraction with forms processing to automate complete onboarding workflows from document collection to data entry. Start with 50 free pages.
Every US state uses a different driver license layout. Unlike standardized documents such as passports, driver licenses vary dramatically by issuing jurisdiction. A California driver license places the name in a different position than a Texas license. New York uses a different font and field arrangement than Florida. Each state also includes different supplementary fields — some include weight, some include eye color, some include organ donor status, and the position of each varies. A template-based extraction system would need 50+ templates just for US driver licenses, plus templates for each state's ID card variant, enhanced driver licenses, and interim documents. AI-powered extraction eliminates the template requirement by identifying fields based on their label text and spatial context rather than fixed coordinates.
Photo quality from phone cameras introduces extraction challenges. Most ID document capture now happens through phone cameras rather than flatbed scanners. A customer photographs their driver license on a desk, a counter, or their hand. The resulting image has perspective distortion from the camera angle, uneven lighting with shadows and highlights, potential glare from the holographic security overlay, and motion blur if the phone moved during capture. Background clutter from the surface the ID is resting on adds noise to the image. The AI must correct for all of these photographic artifacts before extraction can begin — deskewing the perspective, normalizing the lighting, identifying the document boundaries against the background, and removing glare artifacts that obscure text beneath the holographic overlay.
Security features designed to prevent counterfeiting also complicate OCR. Identity documents are deliberately designed with visual complexity to resist forgery: holographic overlays, rainbow printing, microtext, guilloche patterns, and UV-reactive inks. These security features, while essential for document integrity, create visual noise that interferes with text extraction. The holographic overlay on many driver licenses produces bright spots in photos that obscure the text beneath. Rainbow printing causes individual characters to shift color, complicating character recognition. Microtext that appears as a line to the naked eye can confuse the AI's text detection. The extraction engine must distinguish between genuine text content and decorative security elements, reading through the visual complexity to capture the data underneath.
International documents multiply the variation. Organizations that process passports and foreign-issued ID cards face an even broader range of document formats. While the passport MRZ (machine-readable zone) follows the ICAO international standard, the visual fields above it vary by country in language, script, layout, and included fields. ID cards from EU member states follow a different format than those from Asian or African countries. Some documents include Latin transliterations of names; others do not. The AI must detect the document type and issuing country, then apply the appropriate extraction logic for that specific document variant — a recognition challenge that scales to hundreds of document formats worldwide.
The extraction pipeline starts with document detection and preprocessing. The AI identifies the document boundaries in the image, crops away the background, and corrects perspective distortion to produce a flat, front-facing view of the document. For images with glare from holographic overlays, the system applies glare removal algorithms that recover the text beneath the bright spots. For images with motion blur or low resolution, enhancement processing sharpens the text before recognition. The preprocessing stage also determines the document type — driver license, passport, or ID card — and the issuing jurisdiction, which informs the extraction logic in the next stage.
The second stage is field identification and extraction. Using the detected document type as context, the AI locates the standard fields on the document: name fields (first, middle, last), date of birth, address (street, city, state, zip), document number, issue and expiration dates, and supplementary fields specific to the document type. For documents with a barcode (most US driver licenses include a PDF417 barcode on the back), the system reads the barcode data and cross-references it with the visually extracted text. Discrepancies between the barcode data and the visual OCR — which can indicate either an OCR error or a document alteration — are flagged for review. For passports, the MRZ is decoded using the standard ICAO format and compared against the visual fields.
The third stage validates and standardizes the extracted data. Dates are parsed into consistent formats regardless of how they appear on the document. Addresses are standardized to USPS format for US documents. ID numbers are validated against the issuing jurisdiction's format rules — for example, confirming that a California driver license number starts with a letter followed by seven digits. Expiration dates are checked to flag expired documents. The output is a structured data record with each field labeled, validated, and ready for import into CRM, KYC, or identity management systems. Confidence scores accompany each field, and fields falling below the confidence threshold are marked for human verification.
Banking and financial services KYC onboarding. Banks and financial institutions are required by KYC (Know Your Customer) regulations to verify the identity of every new account holder. The customer presents a government-issued photo ID, and the bank must capture the name, date of birth, address, and ID number, then verify this information against watch lists and credit bureau records. Manual entry of ID data takes 3 to 5 minutes per customer and introduces errors that can cause false positives on watch list screening. AI extraction captures the ID data in seconds, feeds it directly into the KYC screening system, and preserves an image of the source document for the compliance audit trail. The entire account opening process becomes faster for the customer while producing more accurate data for the compliance team.
Property management tenant screening. Property management companies collect ID copies from every prospective tenant as part of the application process. A single property manager handling 20 applications per week manually enters name, date of birth, and ID number from each applicant's driver license into the tenant screening system. Errors in the ID number cause screening reports to return for the wrong person or fail entirely, delaying the leasing process. AI extraction from photographed or scanned IDs feeds accurate data directly into the screening workflow, reducing application processing time and eliminating the screening failures caused by data entry errors.
Healthcare patient identity verification. Hospitals and clinics collect patient ID copies at registration to verify insurance eligibility and prevent medical identity fraud. The registration clerk photographs or photocopies the patient's driver license and insurance card, then manually enters the demographic data into the EHR. For emergency departments processing 100 or more patients per day, this manual entry creates registration bottlenecks that delay care. AI extraction from the patient's ID populates the registration fields automatically, and cross-referencing the ID data with the insurance card data catches discrepancies that might indicate eligibility issues before the patient is seen.
Employment I-9 verification and onboarding. Every new hire in the United States must complete Form I-9, which requires the employer to examine identity and work authorization documents. HR departments collect copies of driver licenses, passports, permanent resident cards, and employment authorization documents from new employees and manually enter the document details into the I-9 system. For large employers onboarding dozens of new hires per week, AI extraction from identity documents accelerates the I-9 process from minutes per employee to seconds, while producing the accurate document data and audit trail that E-Verify and ICE audits require.
Upload photos or scans of identity documents and get structured data with every field extracted and validated
Yes. AI-powered OCR reads driver licenses, state ID cards, military IDs, and other government-issued identification documents. The system identifies the document type, locates the standard fields — full name, date of birth, address, ID number, issue date, expiration date, and document class — and extracts each value into structured data. It handles the varied layouts used by different US states and international jurisdictions, including both the human-readable text on the front and the machine-readable zone (MRZ) on the back of compliant documents.
Accuracy depends on image quality but typically exceeds 95% for clearly photographed or scanned documents. The AI uses multiple extraction strategies to maximize accuracy: it reads the visual text fields on the document front, cross-references with the barcode data on the back when available, and validates extracted fields against format rules. When the visual text and barcode data disagree, the system flags the discrepancy for human review rather than silently outputting potentially incorrect data.
Yes. Passports follow the ICAO 9303 international standard, which defines the machine-readable zone (MRZ) format at the bottom of the biographical data page. The AI reads both the MRZ — which contains the name, nationality, passport number, date of birth, sex, and expiration date in a standardized format — and the visual text fields above it. For passports in non-Latin scripts, the MRZ provides a Latin transliteration of the holder's name. The system processes passports from any ICAO-compliant country.
The extraction process handles sensitive personal identifiable information (PII), so security is critical. Documents are processed with encryption in transit and at rest, and source images can be configured for automatic deletion after extraction. For organizations subject to KYC, AML, GDPR, or HIPAA regulations, the system provides audit logs showing when each document was processed, what data was extracted, and who accessed the results. The extracted data can be masked or redacted in the output to limit PII exposure in downstream systems.
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