I've been exploring invoice processing and accounting automation workflows recently, and one thing stands out: extracting data from invoices sounds simple until you encounter real-world documents.
Challenges I've seen include:
Different invoice layouts
Poor scan quality
Multi-page invoices
Handwritten notes
Missing or inconsistent fields
Multiple languages and currencies
For developers working with OCR or document AI:
What has been your biggest challenge with invoice extraction?
Are traditional OCR engines enough, or are you using AI/LLM-based approaches?
How do you validate extracted data before sending it into accounting systems?
Interested in hearing about real-world experiences, tools, and lessons learned from production environments.
Offloadly
Helping small business owners reclaim 10+ hours/week by combining smart VAs with AI automation. Tips on ops, delegation, and scaling without
The layout variation problem is the one that never really goes away. You can train a model on thousands of invoice templates and the next client will show up with something completely different.
From the small business side, I've seen teams spend hours manually re-entering invoice data because their OCR tool choked on a slightly rotated scan or a logo that overlapped the line items. The gap between "works in a demo" and "works on the messy PDF my accountant emailed me" is massive.
Curious if anyone has found that multimodal LLMs (feeding the invoice image directly to GPT-4V or similar) are closing that gap faster than traditional OCR pipelines.