Document Extraction Pipelines — From PDFs to Structured Data
PDFs are not text files
Scanned PDFs need OCR; digital PDFs need layout parsing (tables, columns, headers). Treating either as plain text destroys the structure extraction depends on. Start with document-type detection and route each type to its own pipeline.
Extraction with validation
LLM extraction plus schema validation plus confidence thresholds: high-confidence fields auto-accept, medium goes to human review queues, low rejects with reasons. The review queue is the product — design it first, not last. Every correction becomes training signal.
Indian-document specifics
GST invoices (e-invoice QR schemas), Aadhaar/PAN redaction requirements, and multi-language (Hindi, Gujarati, Tamil) OCR accuracy vary wildly by vendor — benchmark on your actual documents, not vendor demos. DPDP redaction runs before storage, always.
Cost reality
Per-document costs of ₹2–₹8 at volume, dominated by page count and model choice. Batch async processing cuts 40% versus real-time for non-urgent flows.
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Written by AI Engineer
Specialized engineering teams at Omnetra focus on writing high-performance code, ensuring API security, and optimizing layouts for client success.