OCR specialists for your dedicated team.

Where OCR fits in a long-term product team.

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, such as deskewing, contrast correction and denoising, often improves results more than changing the OCR engine.

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 fields to human review is what makes a document pipeline trustworthy.

What your dedicated team does with OCR.

Accuracy expectations need setting clearly 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.

Common OCR use cases.

• 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 confidence scores put to use.
• Accuracy expectations set clearly What the error rate means for your process, stated before the process depends on it.

Adding OCR skills to your team.

Document extraction is assigned inside automation capacity, with realistic accuracy stated before the surrounding process is designed.

At Azendo, OCR work sits with a dedicated AI automation team. Every OCR specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.