Precise date of birth
document authenticity.
Advanced OCR technology extracts exact date of birth from driver's licenses, passports, and national IDs. AI-powered authenticity scoring detects photo manipulation, text tampering, and forgery attempts with configurable fraud thresholds.
Document to verified age
in four steps.
When exact birth dates
matter legally.
Document-AI serves platforms where exact date of birth extraction
How Document-AI
reads identity documents.
Traditional optical character recognition (OCR) treats documents as collections of characters to recognize. This approach fails on identity documents where fonts vary, security patterns interfere with text, and critical data appears in diverse layouts across hundreds of document formats worldwide.
Document-AI uses neural networks trained specifically on government credentials. The model learned from millions of driver's licenses, passports, national IDs, and resident cards — understanding not just character shapes but document structure, expected field locations, date formats, and visual patterns unique to official documents versus amateur forgeries.
When you capture an ID, the neural network first classifies document type: US driver's license, EU passport, UK national ID, etc. This classification activates region-specific extraction logic tuned for that credential format. The system knows California licenses put DOB in the top-right corner with MM/DD/YYYY format, while UK licenses use bottom-left with DD.MM.YYYY format.
Field extraction happens through attention mechanisms — the network focuses on relevant regions, ignoring decorative elements, holograms, and background patterns that confuse traditional OCR. Post-processing validates extracted data: date formats must be logical, expiration dates must be future-dated, age calculations must be mathematically consistent.
If primary extraction fails (damaged document, poor lighting, unusual format), a secondary OCR engine automatically processes the image using alternative algorithms. This fallback system ensures high success rates even on edge cases: worn cards, non-standard credentials from small countries, temporary IDs with simplified formats.
Authenticity scoring
detects document fraud.
Extracting text from documents is straightforward — preventing fraud requires deeper analysis. Document-AI examines six categories of authenticity signals, combining them into a 0-100% score indicating likelihood the document is genuine and unaltered.
Photo replacement detection analyzes edges around portrait regions. Legitimate IDs are produced through professional card printing where photos are embedded during manufacturing. Swapped photos show characteristic artifacts: color mismatches between portrait and card background, unnatural edge sharpness, lighting inconsistencies, shadow patterns incompatible with original photography conditions.
Font consistency checking catches text alterations. When someone edits a birth date using photo manipulation software, they rarely match the exact typeface, weight, spacing, and rendering quality of original text. The system measures character metrics at sub-pixel precision, identifying micro-variations invisible to human review but statistically significant indicators of tampering.
Template verification confirms structural compliance with known formats. Each credential type has official templates — field positions, dimensions, graphical elements, security feature placements. Documents diverging from expected templates get flagged: a "California driver's license" with Florida formatting raises suspicion.
Expiration date logic prevents use of outdated credentials. Texture analysis identifies screen photography attempts — when users photograph a digital image of an ID displayed on another screen, moire patterns, pixel grids, and backlight bleed reveal the deception. Quality assessment flags unusually pristine scans of supposedly worn physical cards or degraded images suggesting photocopies.
These signals are weighted by fraud intelligence: certain manipulation types are more common than others, certain document regions are frequently targeted. Machine learning models trained on verified fraud attempts combine signals into final authenticity scores with configurable thresholds per industry risk profile.
Balance security
against user friction.
Fraud detection is never binary perfect. Stricter thresholds catch more fraud but increase false rejections of legitimate documents. Document-AI lets you configure authenticity score requirements matching your industry's risk tolerance and regulatory obligations.
150+ document types.
56+ countries supported.
Document-AI trained on government credentials worldwide, handling the diversity of formats, languages, security features, and design standards across international identity documents.
Ephemeral processing.
Zero document retention.
Document-AI handles highly sensitive identity credentials. The architecture is designed around data minimization principles: extract only necessary information, delete source materials immediately, retain only verification outcomes.
Explore other
verification methods.
Document-AI provides precise date of birth extraction, but your platform might benefit from different verification approaches depending on regulatory requirements and user experience priorities. Compare all four methods to find your optimal solution.
Need guidance selecting the right verification approach? Our compliance specialists can assess your regulatory requirements, industry vertical, and user demographics to recommend the optimal solution.
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