OCR output is most valuable when it becomes clean, reviewable, and reusable business data.
This guide shows how teams move from raw documents to structured records in Numora.
Step 1: Capture Source Files
Collect source files from email, user uploads, or shared storage.
Numora works best when files are:
- High-contrast and readable.
- Correctly oriented.
- Grouped by business context (for example, invoices vs. receipts).
Step 2: Extract and Normalize
Run extraction to capture text and key fields.
After extraction, normalize:
- Date formats.
- Currency and numeric precision.
- Supplier and customer naming conventions.
Normalization reduces downstream mapping errors.
Step 3: Human-in-the-Loop Review
Before publishing records, reviewers confirm fields with low confidence or business impact.
Recommended checks:
- Invoice number uniqueness.
- Amount and tax totals.
- Counterparty names.
- Document date and due date.
Step 4: Publish to Downstream Systems
Once confirmed, send the data where it is needed:
- Internal dashboards.
- Accounting systems.
- Automation workflows and notifications.
Step 5: Track Quality Over Time
Create a lightweight quality loop:
- Sample reviewed documents weekly.
- Track correction rate by document type.
- Update extraction rules and reviewer guidance.
A stable quality process is what turns OCR from a feature into a dependable operation.