Document Processor

Problem

Organizations process thousands of documents monthly in mixed formats (PDF, scans, emails, images). Manual classification and data extraction introduces 5-15% error rates, 2-3 day processing delays, and scales linearly with headcount.

Solution

Create a parser pipeline with OCR fallback for scanned documents, LLM-powered field extraction with structured outputs, confidence scoring per field, and automated routing based on document type and compliance needs.

Implementation Steps

1. Define extraction schema per document type

   Map required fields, data types, and validation rules for each document category (invoices, contracts, forms, reports).

   Tip: Define a measurable success metric and review weekly to improve quality and cost.

   # Schema definition for invoice extraction
   invoice_schema = {
       'vendor_name': {'type': 'string', 'required': True},
       'total_amount': {'type': 'number', 'required': True},
       'line_items': {'type': 'array', 'items': {'description': 'string', 'amount': 'number'}}  
   }

2. Build OCR and ingestion pipeline

   Handle scanned files, low-quality images, and handwritten content with OCR. Mistral OCR 3 handles handwriting at $2/1K pages.

   Tip: Pre-process scanned images with deskewing and contrast enhancement before OCR to improve extraction accuracy by 15-20%.

3. Classify document type

   Route each document through a classifier to determine type, required extraction schema, and compliance handling rules.

4. Extract structured fields

   Use LLM with structured output mode to extract fields per the schema. Attach confidence scores to each extracted value.

5. Validate and persist metadata

   Cross-check extracted values against business rules, flag low-confidence fields for human review, and store results in your database.

Recommended combos

FAQs