Question 1

What manual work can I automate by connecting Nanonets OCR to other tools?

Accepted Answer

You can automate the extraction of text and data from documents and images, then route that extracted information directly into databases, spreadsheets, or downstream applications without manual re-entry. For example, when documents arrive in email or cloud storage, Nanonets OCR can pull structured data (names, dates, amounts, addresses) and pass it to your CRM, accounting software, or workflow system in real time. This eliminates copy-paste work and reduces errors from manual transcription.

Question 2

How does data flow between Nanonets OCR and other business tools?

Accepted Answer

Nanonets OCR outputs extracted data as structured records that can be sent via API or webhook to connected applications. When you configure an automation, incoming documents trigger extraction, and the results flow into your chosen tool as fields or entries. The exact format and field mapping depend on how you configure the OCR model and your automation platform, so you define which extracted values go to which destinations in your downstream system.

Question 3

What causes Nanonets OCR to miss or misread information?

Accepted Answer

Document quality, image resolution, and unusual layouts can affect accuracy. Poor scans, rotated pages, handwriting, or non-standard templates may produce incomplete or incorrect extractions. Additionally, if your OCR model is not trained or configured for the specific document type you are processing, accuracy will suffer. Before scaling automation, test extraction on sample documents to catch these issues early and adjust your model or document preprocessing if needed.

Question 4

How should I set up Nanonets OCR to ensure automations run reliably?

Accepted Answer

Build and validate an OCR model specifically for your document types before connecting it to production automations. Clearly define which fields you need extracted and test the model against diverse real examples from your document flow. Configure your automation to handle extraction failures gracefully, such as by routing uncertain results for human review or notifying you when confidence scores fall below a threshold. This prevents bad data from reaching downstream systems and gives you visibility into extraction quality over time.

Automate the manual work in Nanonets OCR

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Ready to automate your work in Nanonets OCR?