Excluding OCRed documents and handling them manually can improve data integrity in Relativity project workflows.

OCR drama in data sources is a surprisingly common snag in modern project workflows. You run into scanned documents, PDFs with images, receipts, and forms that were never meant to be machine-read in the first place. The question often comes up in teams that manage large datasets: what’s an acceptable action for handling the OCRed documents? The straightforward answer is simple, but the reasoning behind it is worth unpacking: exclude the OCRed documents and handle them manually.

Let me explain why this approach makes sense in the real world, where accuracy isn’t just nice to have—it’s essential.

Why OCR can trip you up in the first place

OCR, at its core, is a clever guesser. It tries to convert pictures of text into actual characters. That sounds magical until you hit the edge cases. A few stubborn realities get in the way:

• Image quality matters. A scan that’s jaggy, has shadowing, or is a bit blurry can turn clean letters into junk. When the text isn’t crisp, OCR tends to misread it.

• Fonts and layouts matter. Narrow columns, multi-column pages, or fancy fonts can fool OCR engines. Tables? Don’t get me started—cell borders and merged cells are prime misreaders.

• Document condition. Yellowed pages, folding, water stains, or handwriting quirks increase the risk of errors.

• Language and structure. OCR does okay with standard language but can stumble with unusual terms, abbreviations, or acronyms specific to a field.

All of this means OCRed text carries a built-in error rate that isn’t uniform. Some pages come through reasonably well; others spawn a cascade of misreads. In a project where decisions depend on precise data, letting questionable OCR drive outcomes is a risky move.

What happens if you try to bake OCRed data into automated pipelines

If you include OCRed documents in automated data flows without a safety net, you’re inviting a few predictable headaches:

• Noise that masquerades as truth. Tiny OCR mistakes can become big problems in data queries, searches, and analytics.

• Downstream misinterpretation. If an OCR error changes a numeric value, date, or identifier, it can derail workflows, cause misclassifications, or trigger incorrect routing.

• Rework gravity. It’s much harder to clean up after the fact when bad data has already seeped into dashboards, reports, or automated decisions.

• Trust erodes. When data quality slips, teams lose faith in the pipelines. That slows progress and saps momentum.

In short, you don’t save time by letting OCR errors roam free. You risk wasting more time fixing issues later than you gain by skipping a manual review upfront.

A practical, human-centered workflow you can actually run

Think of OCRed documents as a separate stream that deserves a careful, person-led pass. Here’s a pragmatic way to handle them without stalling the whole project:

1. Flag and classify. As soon as a document appears, tag it as OCRed. Add metadata about the source, page quality, and any obvious OCR red flags (blurriness, unusual fonts, complex layouts). This makes it easy to pull these items into a focused review queue.

2. Prioritize for manual review. Not every OCRed file needs the same level of attention. Create a triage system: high-risk items—like financial statements, contract terms, or identifiers—get top priority; lower-risk pages can be sampled first or archived for later re-check.

3. Assign to skilled reviewers. Put OCRed documents into the hands of data stewards, paralegals, or specialists who know what to look for. They should compare OCR text against the original image, correct errors, and annotate any uncertainties.

4. Verify with a QA loop. After manual correction, run a lightweight verification pass. Check key fields, run controlled searches, and spot-check a few pages for consistency. Maintain an audit trail so you can explain what was changed and why.

5. Decide on re-integration. Once corrections are confirmed, decide if and how the data will re-enter automated workflows. In many cases, you’ll keep the corrected version separate from the original OCR text, until you’re confident in reliability. In some workflows, you might re-run OCR with improved settings on the corrected subset, but only after a solid quality check.

6. Document decisions and rationale. Keep a clear record of why certain items were excluded or marked for manual handling. This isn’t a bureaucracy thing; it’s about making future work predictable and defensible.

7. Build guardrails for the future. Consider setting thresholds for OCR confidence scores. If an item falls below a certain score, it automatically routes to manual review rather than entering regular pipelines. This keeps the automatic flow clean and trustworthy.

A few practical tips to smooth the ride

• Separate data streams. Treat OCRed content as its own lane, even if it means duplicating a few records for a period. It’s better to have two clean streams than one messy mix.

• Use metadata to your advantage. Capture source type, OCR confidence, language, and page complexity. Metadata is the quiet hero that makes later audits painless.

• Sample regularly. If you’re dealing with huge volumes, don’t wait for a full sweep to understand OCR quality. Periodic sampling helps you calibrate the triage thresholds and reviewer workload.

• Leverage human-in-the-loop review. Outsourcing the manual pass isn’t just a cost center; it’s a quality investment. A well-run review reduces downstream risk and improves overall project outcomes.

• Keep it transparent. When stakeholders ask why a document is excluded or why a known OCR error remains, you’ll have a crisp, documented rationale to share.

The softer side of data quality

You might wonder if the extra manual step slows you down. It can, at first. But the elixir here is confidence. You gain a steadier state for your data streams, and that steadiness pays off in faster decisions, fewer reworks, and clearer communication with teammates, clients, and regulators.

Humans bring context machines can’t replicate. A line that looks ordinary to a scanner might carry a critical nuance or a jurisdictional requirement that only a person would spot. OCR is powerful, but it isn’t a substitute for careful human judgment in high-stakes data management. By excluding problematic OCRed documents from automated processing and handling them with care, you protect the integrity of the data you actually rely on.

Real-world reminders you can use

• OCR isn’t a final check. Treat OCR results as a rough draft that needs human proofreading before you trust it for decisions.

• Not all OCR is bad, but some is riskier than others. Prioritize the most sensitive content first.

• The moment a document slips into the manual path, you’ve started a predictable, trackable process. That’s a feature, not a stumble.

• Documentation isn’t optional. It’s the glue that keeps the workflow coherent when teams change or new members join.

A nod to the bigger picture

This isn’t just about one file or one project. It’s about building data workflows that respect quality at every turn. When you design processes that automatically welcome clean data and gracefully route the murky stuff to skilled hands, you create room for better analysis, more reliable results, and fewer last-minute firefights.

If you’re part of a team that handles large data sets, you’ve probably wrestled with trade-offs between speed and accuracy more than once. The takeaway here is straightforward: you don’t gain by rushing uncertain OCR through the system. You gain by a thoughtful, human-driven approach to the tricky cases. Excluding OCRed documents from automated processing and giving them the attention they deserve isn’t a retreat—it’s a smarter way to protect the quality of everything that follows.

A final thought to carry forward

Next time you hit a pile of OCRed pages, pause for a moment and ask yourself: what data would be at risk if I trusted this text as-is? If the answer points to potential misinterpretation or downstream mistakes, that’s your cue to route those items to manual review. The result isn’t silence or slowed progress—it’s cleaner data, clearer decisions, and a project trajectory that you can defend with confidence.

In the end, the approach isn’t about avoiding OCR. It’s about respecting its limitations while choosing the right path for accuracy and reliability. Excluding OCRed documents for manual handling is a practical, responsible choice that keeps data integrity front and center—today, tomorrow, and beyond.