What is Structured Data Extraction?
Structured data extraction is the process of converting unstructured documents into validated, typed data using AI and schema validation.
Structured data extraction is the process of converting unstructured documents (PDFs, images, text files) into validated, typed data using AI and schema validation. The output is typically JSON that conforms to a predefined schema.
The Problem
Documents contain valuable information, but it's locked in unstructured formats:
- PDFs — Text, tables, images mixed together
- Images — Scanned documents, photos, screenshots
- Text files — Unstructured prose, logs, transcripts
Traditional approaches require manual data entry or brittle regex patterns that break when formats change.
The Solution
Modern structured data extraction uses LLMs to:
- Parse documents into processable content
- Extract relevant information based on a schema
- Validate output against the schema
- Retry with error feedback if validation fails
Key Components
| Component | Purpose |
|---|---|
| Document parser | Converts PDFs, images to text/structure |
| Schema | Defines expected output structure (JSON Schema) |
| LLM | Extracts data following the schema |
| Validator | Checks output against schema |
| Retry loop | Fixes errors with LLM feedback |
Use Cases
- Invoice processing — Extract vendor, line items, totals
- Contract analysis — Extract parties, dates, obligations
- Form data entry — Convert scanned forms to structured data
- Research papers — Extract methods, results, citations
Tools for Structured Data Extraction
- Struktur — Open source, autonomous agent-based extraction
- LlamaExtract — Managed cloud service with citations
- Unstract — Open source with visual prompt engineering
- Instructor — Python library for structured LLM outputs