CASE STUDY — 02
OCR at Scale: Digitizing Billion+ Documents
Achieving 99.99999% accuracy using custom multimodal AI OCR models to process documents at scale in public sector vertical across 12 languages.
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Achieving 99.99999% accuracy using custom multimodal AI OCR models to process documents at scale in public sector vertical across 12 languages.
Request a demo
Public sector organizations face massive document backlogs spanning decades, with critical information locked in paper formats across multiple languages. Traditional OCR solutions struggle with accuracy, multilingual support, and the scale required for government digitization initiatives. The cost of manual data entry and verification was becoming unsustainable, while citizens demanded faster, digital-first services.
We deployed custom multimodal AI OCR models trained specifically for public sector documents, combining computer vision, natural language processing, and context-aware validation to achieve unprecedented accuracy across diverse document types and languages.
Advanced computer vision models that understand document structure, context, and handwriting across 12 regional languages.
Context-aware verification engine that cross-references extracted data with databases and business rules.
Cloud-native architecture designed to process millions of documents daily with enterprise-grade reliability.
RESTful APIs and webhooks for easy integration with existing document management and workflow systems.
Reduction in processing time from months to days
Annual cost savings on manual data entry
Faster document retrieval
Digital-first service delivery achieved
Join leading organizations that trust UTTER.AI OCR to digitize their critical documents with unmatched accuracy and scale. See how our custom AI models can work for your use case.
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