Document Extraction
Guides, tutorials, and real-world workflows for composable document and image processing.
Best Document Extraction APIs in 2026
A developer's guide to document extraction APIs — from OCR engines to structured extraction platforms. What each tool does, where it fits, and what it costs.
Document Extraction vs Reducto: Two Approaches to Structured Extraction
Reducto and Iteration Layer both extract structured data from documents. Here's how their approaches differ — and when each one fits.
Document Extraction vs Nanonets: Schema-Based or Model-Based?
Nanonets requires training models per document type. Iteration Layer extracts structured data from any document using a schema — no training, no model management.
Document Extraction vs Mistral OCR: Structured Data or Just Markdown?
Mistral OCR converts documents to markdown. Iteration Layer extracts structured JSON with typed fields and confidence scores. Here's why that difference matters.
Document Extraction vs LlamaParse: Structured Data or RAG Preprocessing?
LlamaParse converts documents into LLM-ready chunks. Iteration Layer extracts typed, structured fields with confidence scores. Different tools for different jobs.
Document Extraction vs Google Document AI: Schema-Based or Processor-Based?
Google Document AI requires creating processors, managing versions, and training custom models. Iteration Layer extracts structured data with a schema — no setup needed.
Document Extraction vs Azure AI Document Intelligence: Schema-First or Model-First?
Azure Document Intelligence requires pre-built models or training data. Iteration Layer extracts structured data from any document using a schema — no training needed.
Document Extraction vs AWS Textract: One API Call vs Five
Textract splits document processing across five separate APIs, each billed independently. Iteration Layer does it in one call with typed fields and confidence scores.
Parse Documents Inside Claude and Cursor with MCP — No Code Required
Use MCP to parse invoices, contracts, and receipts directly from Claude Desktop or Cursor. No code, no server, no pipeline.
Regex and Templates Break. Here's What to Use Instead for Document Parsing
Regex breaks on layout changes. Templates break on new formats. Schema-based AI extraction handles both.
Under the Hood: How Our Parser Plans Multi-Field Extractions Intelligently
The parser builds a dependency graph, groups fields by effort, and paginates arrays. A look at the extraction planning system.
Trust, but Verify: How Confidence Scores Make AI Extraction Production-Ready
Every extracted field comes with a confidence score. Use them to build human-in-the-loop review flows.