Optical Character Recognition (OCR)

🔹 Definition

Optical Character Recognition (OCR) is a technology that converts images of typed, printed, or handwritten text into machine-readable data. It enables computers and software to automatically extract, digitize, and process textual information from documents such as passports, ID cards, utility bills, bank statements, or business licenses—without the need for manual data entry.

OCR is a key enabler of eKYC, document verification, and automated onboarding processes in regulated industries such as finance, insurance, and compliance.

🔹 Frequently Asked Questions (FAQs)

Q1: How does OCR work in identity verification?

• Captures an image of a document using a scanner or smartphone camera
• Applies pattern recognition and machine learning to identify characters and layout
• Extracts data fields such as name, date of birth, document number, expiration date
• The data is then used for auto-filling forms, cross-checking databases, or screening against watchlists

Q2: What types of documents are commonly processed using OCR?

• Government-issued IDs (e.g., passports, national ID cards)
• Proof of address documents (e.g., utility bills, bank statements)
• Corporate documents (e.g., business registration forms, tax certificates)
• Banking documents (e.g., account statements, cheques)
• Invoices and receipts in financial reconciliation processes

Q3: What are the benefits of OCR in compliance workflows?

• Enables faster onboarding by eliminating manual data entry
• Improves data accuracy and consistency
• Scales document processing for high-volume environments
• Reduces human errors and operational costs
• Supports KYC, KYB, and document-based due diligence

Q4: What are common challenges or limitations of OCR?

• Accuracy may decrease with:
  • Blurry or low-resolution images
  • Handwritten text or non-standard fonts
  • Damaged, folded, or reflective documents
• Multilingual or complex documents may require custom OCR models
• May require manual review or post-processing in high-risk environments

Q5: How is OCR integrated with other technologies?

• Combined with liveness detection and biometric matching in digital onboarding
• Integrated into identity verification SDKs/APIs
• Paired with machine learning (ML) and natural language processing (NLP) for intelligent document classification and fraud detection
• Supports real-time validation and risk scoring systems in AML/CFT compliance