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Data extraction from unstructured text, including invoices, receipts, medical records, and contracts, is the bread and butter of structured output. The schema defines what “correctly extracted” looks like: which fields must be present, what types they have, and what ranges are valid. This turns extraction from a fuzzy NLP task into a well-defined contract: the output either conforms to the schema or it doesn’t. The tighter your schema constraints, the less post-processing you need. A format: “date” constraint on the invoice date means you get ”2026-02-12” instead of ”Feb 12, 2026” or ”12/02/2026”. A pattern constraint on currency codes means you get ”USD” instead of ”US Dollars”.

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Goal

Extract invoice data from OCR text into a normalized, storage-ready record with validated types and bounded arrays.

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Schema contract

{
  "type": "object",
  "properties": {
    "invoice_id": { "type": "string", "minLength": 1, "maxLength": 40 },
    "vendor": { "type": "string", "minLength": 1, "maxLength": 120 },
    "invoice_date": { "type": "string", "format": "date" },
    "currency": { "type": "string", "pattern": "^[A-Z]{3}$" },
    "total": { "type": "number", "minimum": 0 },
    "line_items": {
      "type": "array",
      "minItems": 1,
      "maxItems": 100,
      "items": {
        "type": "object",
        "properties": {
          "description": { "type": "string", "minLength": 1, "maxLength": 200 },
          "quantity": { "type": "number", "minimum": 0 },
          "unit_price": { "type": "number", "minimum": 0 },
          "line_total": { "type": "number", "minimum": 0 }
        },
        "required": ["description", "quantity", "unit_price", "line_total"],
        "additionalProperties": false
      }
    }
  },
  "required": ["invoice_id", "vendor", "invoice_date", "currency", "total", "line_items"],
  "additionalProperties": false
}

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Example input

Invoice INV-22019
Vendor: Northwind Supplies
Date: 2026-02-12
Currency: USD
Items:
- Battery Pack x2 @ 39.50 = 79.00
- Cable Kit x1 @ 15.00 = 15.00
Total: 94.00

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Example output

{
  "invoice_id": "INV-22019",
  "vendor": "Northwind Supplies",
  "invoice_date": "2026-02-12",
  "currency": "USD",
  "total": 94,
  "line_items": [
    {
      "description": "Battery Pack",
      "quantity": 2,
      "unit_price": 39.5,
      "line_total": 79
    },
    {
      "description": "Cable Kit",
      "quantity": 1,
      "unit_price": 15,
      "line_total": 15
    }
  ]
}

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Implementation tips

  • Narrow fields to business needs. Don’t add a catch-all "raw_text" field. Each field should map to a column in your database or a field in your downstream API. If you don’t need it, don’t extract it.
  • Bound arrays to realistic limits. maxItems: 100 on line items is generous but prevents runaway generation on malformed OCR input. Without it, a noisy scan could produce thousands of phantom line items.
  • Use format and pattern for normalization. format: "date" on invoice_date gives you ISO 8601 dates regardless of how the source text formats them. pattern: "^[A-Z]{3}$" on currency gives you three-letter codes, not spelled-out currency names.
  • Consider per-field confidence. For high-stakes extraction (financial documents, medical records), add a confidence number field next to each extracted value. This lets your application flag low-confidence extractions for human review rather than trusting everything equally.