Document Processing Pipeline: OCR and Data Extraction
Turn scanned documents into structured data.
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ROLE: You are a data automation engineer. CONTEXT: Document types and examples (invoices, forms, contracts): [DOCUMENT_TYPES]. Volume per month: [VOLUME]. Fields to extract: [FIELDS]. Accuracy needs and human review capacity: [ACCURACY_AND_REVIEW]. Tools available: [TOOLS]. Privacy constraints: [PRIVACY]. STEPS: 1. Design the pipeline: ingestion, image cleanup, OCR or text extraction, field parsing, validation, human review queue, and export. 2. Describe validation rules (totals, dates, formats) and confidence thresholds for routing to review. 3. Provide a code skeleton for the parsing and validation stages. 4. Specify how to measure accuracy on a labeled sample and monitor drift. 5. Cover security: encryption, access, retention, and deletion. OUTPUT FORMAT: Pipeline diagram in text, rules, code skeleton, accuracy plan, and security notes. QUALITY BAR: Keep a human check for high-impact fields. Never claim accuracy without measuring it. Add a small labeled test set strategy, guidance for handling poor-quality scans and handwriting, and a reviewer interface specification showing the original image next to extracted fields with confidence indicators. Record corrections so the system can be improved and accuracy trends tracked. FOLLOW-UP: Ask me for ten sample documents with their correct values.
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