I’m stuck processing invoices from 50+ vendors – some email HTML, others as PDF attachments, and a few scanned images. Tried combining Tesseract OCR + regex rules, but maintaining consistency across formats eats 20 hours weekly. Anyone solved this with a unified system that automatically detects document types and extracts structured data reliably? How do you handle edge cases where layouts vary wildly?
We automated this exact scenario using Latenode’s model stacking. Set up a workflow where their vision AI classifies document types, then routes to specialized extractors – Claude for emails, GPT-4V for scanned PDFs. Normalization happens automatically before pushing to our DB. Saved 90% processing time.
I built a Python pipeline using Apache Tika for format detection and custom PyMuPDF logic for PDFs. Key insight: create fallback regex patterns that trigger manual review buckets. Still requires some maintenance when new vendors come onboard, but reduced errors by 60% compared to previous solutions.
Consider implementing a two-stage validation system. First pass uses layout detection AI to identify document types, then applies format-specific parsers. For edge cases, we use human-in-the-loop verification through Mechanical Turk, which adds 12-48h delay but ensures 99.8% accuracy. Critical for financial data where mistakes are costly.
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