📄 Document Verification¶

Extracting Identity from Physical Documents¶

This section covers every aspect of identity document processing in eKYC — from capturing and classifying documents, to OCR extraction, forensic analysis, liveness detection, and security feature validation. Document verification is the complement to face biometrics: while face verification answers "is this the right person?", document verification answers "is this a real, unaltered document?"

Articles in This Section¶

Document Capture & Classification¶

#	Article	What You'll Learn
1	Document Capture & Quality	Auto-capture, quality checks, camera guidance
2	Document Classification	CNN/ViT models to identify document type from 6000+ classes
3	ID Document Types Worldwide	Passports, national IDs, driving licenses — global diversity

OCR & Data Extraction¶

#	Article	What You'll Learn
4	OCR Pipeline for ID Documents	End-to-end: detection → recognition → field mapping
5	Text Detection Models	CRAFT, EAST, DBNet — locating text regions
6	Text Recognition Models	CRNN, TrOCR, PaddleOCR — reading detected text
7	Document Understanding Models	LayoutLMv3, LiLT, Donut — structured extraction
8	MRZ Parsing	Machine Readable Zone on passports and travel documents
9	Barcode & QR Code Reading	PDF417, QR codes on IDs — parsing encoded data

Document Security & Forensics¶

#	Article	What You'll Learn
10	Document Forensics Overview	Detecting tampering, forgery, and alteration
11	Digital Tampering Detection	ELA, noise analysis, copy-move, splicing detection
12	Document Liveness	Detecting screen display, photocopies, printed photos of documents
13	Security Feature Validation	Holograms, UV features, microprint, watermarks
14	NFC Chip Reading	Reading ePassport/eID chips — BAC, PACE, Active Authentication

Advanced Topics¶

#	Article	What You'll Learn
15	Document Data Verification	Cross-checking extracted data against government databases
16	Address Verification	Utility bills, bank statements — proof of address processing
17	Multi-Language OCR	Arabic, Chinese, Devanagari, Cyrillic — script-specific challenges
18	Synthetic Document Detection	Detecting AI-generated fake IDs
19	Document Processing at Scale	GPU batching, async pipelines, handling millions/day
20	Document Verification Vendors	Microblink, Regula, ABBYY, cloud AI services

Document Verification Pipeline¶

graph TD
    A[Camera/Upload] --> B[Auto-Capture<br/>Quality checks]
    B --> C[Classification<br/>What type of document?]
    C --> D[Parallel Processing]

    D --> E[OCR Pipeline<br/>Text detection → recognition → field mapping]
    D --> F[Forensic Analysis<br/>Tampering, copy-move, splicing]
    D --> G[Document Liveness<br/>Screen/photocopy detection]
    D --> H[Security Features<br/>Hologram, MRZ, barcode, NFC]
    D --> I[Face Extraction<br/>Photo on document]

    E --> J[Structured Data<br/>Name, DOB, ID number, address]
    F --> K[Authenticity Score]
    G --> L[Liveness Score]
    H --> M[Security Feature Score]
    I --> N[Face for matching]

    J & K & L & M --> O[Document Decision<br/>Accept / Review / Reject]
    N --> P[Face Matching Pipeline]

    style D fill:#4051B5,color:#fff
    style O fill:#2E7D32,color:#fff

For Document AI Engineers

Start with OCR Pipeline for the core extraction flow, then Document Understanding Models for the latest approaches. Document Forensics and Document Liveness are the security-critical components.