Fix an extraction error so the model improves on the next document.
No extraction is perfect on unfamiliar layouts. When Nanonets misreads a field, you correct it once and the model learns. The same document type will read more accurately next time, reducing manual review on future batches.
This guide shows you how to fix a single misread field and verify that the correction improves the model's performance on the next set of documents.
Turn the pilot into an evidence-based decision on whether to scale.
Get the extracted data into the systems Registry actually works in.
Prove the accuracy on one document type before scaling to others.
Open the document on the review screen.
Click the field that was read incorrectly.
Compare it against the original image.
Type the correct value.
Save the correction so the model learns from it.
Watch whether that field improves across the next batch.
Suppose Nanonets extracts a field as [incorrect value] when the scanned image clearly shows [correct value]. You open the document on the review screen, click the field, compare it against the original image, type [correct value], and save. The next time you upload a document of the same type, Nanonets may read that field more accurately.
Click the misread field, compare it against the original image, enter the correct value, and save the correction so the model learns from it for future documents of this type.
Pull the key clauses and signatories out of a long document automatically.
Cross-check extracted values against what Registry already holds.