AI Handwriting OCR

Difficult handwritten information required manual interpretation. Forms arrived as scans, and someone had to read each one and type what it said into the system.

Extract the fields automatically, be honest about what the model isn't sure of, and keep a person involved only where it matters.

An AI-powered OCR workflow that extracts structured information. Documents enter an n8n workflow, pass through OCR and an LLM extraction step with structured outputs, get validated against field rules, and land in the destination system. Low-confidence fields are routed to a review queue instead of being silently accepted.

• Intake workflow in n8n with document normalization
• OCR pass followed by LLM extraction constrained to a fixed schema
• Validation rules per field, with confidence thresholds that decide what goes to review
• A review queue for the uncertain cases and a clean path into the destination system

• n8n
• OCR
• AI / LLM
• APIs
• Structured Outputs
• Workflow Orchestration

Roughly 90% of fields on the tested document set were extracted correctly with no human involvement. The rest went to a review queue rather than being retyped from scratch.

Structured outputs and validation rules mattered more than prompt wording. Deciding what happens when the model is unsure is most of the design.