Used to avoid document projects entirely. Parsing was inconsistent. OCR unreliable. Handwriting impossible. Tables became gibberish. Would quote 3x normal rate just to cover the headache. Then discovered modern document processing in n8n ecosystem. Everything changed. WHAT MODERN NODES HANDLE: Multi-format: PDFs, Word, images, scans, phone photos - same node Table extraction: Structured data with rows and columns intact Handwriting OCR: Reads handwritten forms with good accuracy Multi-page intelligence: Hundreds of pages, context preserved Confidence scoring: Every field gets percentage for routing Multi-language: Different languages, no separate workflows WORKFLOW EVOLUTION: BEFORE (Old OCR): Gmail → Download → Google Drive OCR → Parse messy text → Regex → Clean → Format → Validate 12 nodes. Fragile. 73% accuracy. Maintenance nightmare. AFTER (Modern nodes): Gmail → Parse Document → Extract with Schema → Validate → Post 4 nodes. Robust. 96% accuracy. Zero maintenance. REAL EXAMPLE: Medical intake forms with printed text, handwriting, checkboxes, insurance card photos. Old approach: Multiple attempts, manual fallbacks, constant failures. Gave up after two weeks. Modern nodes: Single extraction pass. Handles everything. Including handwritten medical history. Even reads cards photographed at angles. THE SCHEMA APPROACH: Instead of 100 lines of regex: { "patient_name": "string", "date_of_birth": "date", "insurance_provider": "string", "medical_conditions": ["array"] } Modern nodes extract semantically, not positionally. Same schema works across format variations. CONFIDENCE ROUTING: Every field returns 0-100% confidence. Switch logic: IF >90% THEN post directly, ELSE review queue. High confidence auto-processes. Uncertain gets human verification. CURRENT STATE: 12 production workflows 8,000+ documents monthly 94-97% accuracy 1 hour monthly maintenance total Template library THE LESSON: Right tools change everything. Document workflows went from "fragile and painful" to "reliable and profitable" overnight.

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