How do I handle low-confidence OCR results?

Direct answer

For teams asking “How do I handle low-confidence OCR results?”, the strongest answer is a repeatable workflow with defined fields, review, validation, and downstream handoff. The goal is to turn scanned PDFs, photos, JPEGs, PNGs, handwritten forms, and image-based documents into structured data the business can trust.

For “How do I handle low-confidence OCR results?”, define the exact fields, rows, review rules, and destination before scaling automation across a larger document batch.

What the workflow should include

For teams trying to handle low-confidence OCR results, a production-ready answer should capture recognized text, required fields, table rows, confidence flags, missing values, and review status while preserving a consistent schema from one document to the next.

For “How do I handle low-confidence OCR results?”, pay special attention to blur, rotation, shadows, handwriting, low-resolution scans, broken rows, and values that OCR cannot read confidently; those edge cases usually determine whether the output is usable downstream.

How Lido helps

For teams trying to handle low-confidence OCR results, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for teams extracting data from scans and image-based documents.

That lets teams answer “How do I handle low-confidence OCR results?” with a reusable process that can be tested, corrected, and connected to reviewable spreadsheets, CSV files, exception queues, or downstream systems.

Example workflow

1. Collect scanned documents from email, shared folders, uploads, or another intake path.
2. Define the target fields or table columns: low-confidence text and extracted values.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to Excel, Google Sheets, CSV, an ERP, or another downstream system and monitor exceptions over time.