Document Understanding
From scan to structured data
The Problem: Traditional OCR gives you raw text with broken formatting, split words, and misread characters. Turning that into structured, validated data requires a second step — and LLMs excel at it.
The Solution: From Pixels to Validated Data
Traditional OCR extracts text from images but loses structure — columns merge, tables break, handwriting garbles. LLM-based document understanding goes further: it reads, corrects, and structures the text in one pass. The model understands that "$I2.99" is actually "$12.99", that "TechParts L LC" is "TechParts LLC", and that the number at the bottom is a total, not just another line item. The key addition: confidence markers — each extracted field is tagged [VERIFIED] or [UNVERIFIED] so downstream systems know what to trust.
Think of it like a paralegal reading contracts for a law firm:
- 1. Raw OCR extraction: Scan the document image and extract all text — expect errors: split words, misread characters, broken layout
- 2. LLM structuring & correction: LLM reads the raw OCR, fixes errors (split words, misread digits), and organizes into logical sections
- 3. Schema-based extraction: Apply a target JSON schema to extract specific fields — company, amounts, dates, line items
- 4. Validation & confidence marking: Tag each field as [VERIFIED] or [UNVERIFIED] — cross-check totals, flag inferred values, catch hallucinations
Where Is This Used?
- Invoice & Receipt Processing: Extract vendor, amounts, line items, tax from scanned invoices — output structured JSON for accounting systems
- Contract Analysis: Parse clauses, dates, parties, obligations from legal documents with confidence markers for uncertain readings
- Medical Records: Extract diagnoses, medications, lab values from handwritten or printed medical forms
- Technical Drawings & Schematics: Read dimensions, labels, part numbers from engineering drawings and circuit diagrams
Fun Fact: OCR-free models like mPLUG-DocOwl2 and Docopilot (CVPR 2025) skip the OCR step entirely — they process the document image directly as visual tokens. This eliminates OCR errors at the source, though they still need grounding prompts to avoid hallucinating content.
Try It Yourself!
See the interactive pipeline below: watch how a messy OCR output transforms into clean, validated JSON through LLM structuring.
Try it yourself
Interactive demo of this technique
Extract structured data from a document scan instead of raw text
TechParts L LC I NV-2024-0847 2024-03-15 Wid get A 50 649.50 Connector B 100 350.00 Cable Set C 25 700.00 Tax 1,869.45
{ "company": "TechParts LLC", // [VERIFIED] "invoice_number": "INV-2024-0847", // [VERIFIED] "date": "2024-03-15", // [VERIFIED] "items": [ {"name": "Widget A", "qty": 50, "unit_price": 12.99, "total": 649.50}, {"name": "Connector B", "qty": 100, "unit_price": 3.50, "total": 350.00}, {"name": "Cable Set C", "qty": 25, "unit_price": 28.00, "total": 700.00} ], "tax": 169.95, // [VERIFIED] "tax_rate": 0.10, // [UNVERIFIED — inferred] "grand_total": 1869.45 // [VERIFIED] }
JSON schema + correction instruction + confidence markers turn raw OCR into production-ready data. The model fixes errors and honestly marks assumptions.
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