Key Takeaway
By the end of this blueprint you will have an automated document processing pipeline that ingests PDFs and images, extracts text via OCR with layout preservation, classifies documents by type, pulls structured fields using LLM-based extraction with Pydantic schemas, and routes low-confidence results to a human review queue.
Prerequisites
- Python 3.11+ with PyMuPDF or pdf2image for PDF handling
- Tesseract OCR installed, or access to a cloud OCR API (Google Document AI, AWS Textract)
- An LLM API key for classification and extraction (Anthropic or OpenAI)
- PostgreSQL for document metadata and extraction results
- A task queue (Celery, Temporal, or similar) for async processing
Pipeline Architecture
The pipeline follows an ingest-classify-extract-validate pattern. Documents enter through a file watcher or API endpoint, pass through a preprocessing stage for format normalization and OCR, get classified by document type using a fast LLM call, and then flow into type-specific extraction templates powered by structured LLM output. A human-in-the-loop review queue handles low-confidence extractions before data reaches downstream systems.
- 1
Ingest
Accept documents from API upload, email attachment, S3 bucket, or file system watcher. Normalize to a common internal format.
- 2
Preprocess
Convert PDFs to images, run OCR on scanned pages, extract native text from digital PDFs, and detect tables and layout structure.
- 3
Classify
Determine the document type (invoice, contract, report, form) using a fast LLM call or fine-tuned classifier.
- 4
Extract
Apply type-specific extraction schemas using structured LLM output. Each field gets a confidence score.
- 5
Validate
Run business rules (date format, numeric ranges, required fields). Route low-confidence results to human review.
- 6
Output
Write validated extractions to the database, trigger downstream workflows, and archive the source document.
OCR and Text Extraction
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