OCR vs Native PDF Extraction: What's the Difference?
Two documents can look identical on screen and behave completely differently in extraction. The difference is whether the PDF carries a text layer — and it determines the accuracy ceiling of everything downstream.
Native extraction: reading what's already there
A digitally-generated statement embeds every character with its exact position. Extraction reads this directly — no interpretation, no recognition errors. A 7 is a 7. The remaining work is purely structural: figuring out rows and columns.
OCR: interpreting a picture
A scan or photo contains only pixels. OCR infers characters from shapes, which is where errors creep in: 0/O, 1/l/I, 5/S, 8/B. On dense numeric tables like statements, small misreads matter enormously — a misread digit silently changes an amount.
Quality depends heavily on input: resolution, lighting, skew and compression artifacts all degrade recognition.
- Scan at 300 DPI where possible
- Photograph straight-on, in even light, with headers in frame
- Always prefer the bank's original PDF when it can still be downloaded
Why validation matters more for OCR
Because OCR errors are silent, downstream validation is essential. Running-balance reconciliation is the key safeguard: a misread amount breaks the balance chain and gets flagged with reduced confidence instead of flowing into your books unnoticed.