Building an OCR API for Financial Documents: Lessons Learned
Over the past few months, I’ve been working on an OCR API focused on bank statements and invoices.
The initial idea came from a simple problem: extracting data from financial documents is still surprisingly manual in many workflows. Different formats, inconsistent layouts, and low-quality PDFs make it harder than it should be.
At first, I tried rule-based parsing, but it quickly became difficult to maintain. Every new document format required adjustments, and edge cases kept growing.
That’s when I started exploring OCR-based approaches combined with structured data extraction.
Some key things I learned along the way:
Document variability is the biggest challenge
Clean output matters more than raw text extraction
Handling edge cases takes more time than expected
API simplicity is important for adoption
The goal is to make it easier for developers to extract transaction data, account details, and other key information without building complex pipelines from scratch.
Still early, but it’s been an interesting space to work in.
If anyone here has worked on document processing or financial data extraction, I’d love to hear how you approached it.
Interesting approach to OCR. Curious — is the extraction running on AI models or traditional OCR? If AI-powered, how are you managing those API costs?
Thanks! We use a hybrid OCR and AI pipeline, applying AI only when needed to balance accuracy and cost.