OCR.
Optical character recognition extracts text from images and scanned documents. Accuracy depends heavily on input quality, and the realistic output is text with errors rather than a perfect transcription.
Where OCR fits on a long engagement.
Input quality dominates everything else. A clean digital PDF extracts perfectly; a photographed document at an angle in poor light produces errors no engine eliminates. Preprocessing — deskewing, contrast correction, denoising — frequently improves results more than changing OCR engine does.
Confidence scores are the output people ignore and should not. An engine reporting low confidence on a field is telling you something useful, and routing those to human review rather than accepting them silently is what makes a document pipeline trustworthy.
What an assigned team does with OCR.
Accuracy expectations need setting honestly at the start. Ninety-five percent character accuracy sounds excellent and means several errors per page, which for financial data is not acceptable without verification.
Being clear about that before a process is designed around it prevents a predictable disappointment, and it is part of scoping as described in how an assignment runs.
What we use OCR for.
- Preprocessing before recognition Deskew and contrast correction, which often helps more than a different engine.
- Low-confidence fields routed to review The engine's own uncertainty used rather than discarded.
- Accuracy expectations set honestly What the error rate means in practice, stated before a process depends on it.
How OCR capacity is assigned.
Document extraction is assigned inside automation capacity, with realistic accuracy stated before the surrounding process is designed.
Tell us what your roadmap needs OCR for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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