Client

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A USA based AI-driven technology company building document understanding models for large-scale identity verification and information extraction workflows.

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The Challenge

Government-issued identity documents such as Driving Licenses and Vehicle Registration Certificates are information-dense, visually inconsistent, and often vary across states and formats. While OCR can convert images into text, the output is frequently noisy, misaligned, or incomplete.

The client needed to train document intelligence models that could accurately understand and extract structured information from Indian ID cards. This required:

• Reviewing raw ID card images along with OCR-generated text
  
• Correctly extracting and validating more than 15 critical attributes per document
  
• Ensuring consistency across hundreds of thousands of records
  
• Delivering at high speed without compromising accuracy
  

The scale was significant. Over 5 lakh ID documents had to be processed within 10 working days.

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The Solution

Globik AI designed a high-efficiency annotation pipeline combining document expertise with rigorous quality control.

• Dual-Source Review
  Annotators worked with both raw ID card images and OCR text to ensure accurate interpretation of fields that OCR alone could not reliably capture.
  
  
• Attribute-Level Extraction
  Key attributes such as name, date of birth, license number, vehicle number, address, issue date, validity, and authority details were carefully extracted and structured into standardized sheets.
  
  
• Trained Annotation Team
  Annotators were trained specifically on Indian ID formats, layout variations, and common OCR failure patterns.
  
  
• Quality and Scale Management
  Multi-level validation ensured field-level accuracy while parallel workflows enabled delivery of 500,000+ documents within 10 working days.
  

The Result

The client received a clean, structured, and model-ready dataset that enabled:

• Faster training of document AI models for identity and vehicle record understanding
  
• Improved extraction accuracy compared to OCR-only pipelines
  
• Reduced manual verification effort in downstream systems
  
• Reliable performance across diverse ID formats and regional variations
  

Real-World Use Cases

• Identity Verification Systems
  Powering AI models used in KYC and onboarding workflows for banking, insurance, and fintech platforms.
  
  
• Automated Document Processing
  Enabling large enterprises to digitize and structure ID records at scale with minimal human intervention.
  
  
• Compliance and Risk Checks
  Supporting validation of driving and vehicle records for regulatory and operational checks.
  
  
• Digital Public Infrastructure
  Improving accuracy in document-driven systems used by mobility, logistics, and government-linked platforms.
  

Why It Matters

Document AI models do not fail because of algorithms. They fail because of weak training data. By combining human judgment with scale and speed, Globik AI delivered a dataset that teaches models how to correctly read, interpret, and trust real-world identity documents.

This project demonstrates Globik AI’s ability to handle massive document volumes, complex attribute extraction, and tight delivery timelines while maintaining the data quality required for production-grade AI systems.

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Key Highlights

• 500,000+ Indian ID documents processed
  
• Driving License and Vehicle RC coverage
  
• 15+ attributes accurately extracted per document
  
• OCR text reviewed and corrected using raw image context
  
• Delivered within 10 working days
  
• Built for document AI model training and development