When OCR text is poor, rebuild the index entirely to restore document accuracy

OCR trouble can feel like a roadblock that shows up right when you need to move fast. When the text in documents is garbled, misspelled, or simply unreadable, finding what you need becomes a slog. In a project management setting—where timing, accuracy, and clear communication matter—this isn’t just frustrating; it can derail timelines and muddy decisions. If you’re wrestling with bad OCR in Relativity, here’s a practical way forward that keeps the focus on reliability and long-term usability: rebuild the index from the ground up.

Why OCR quality really matters in project work

Think of OCR as the bridge between raw paper or image files and searchable knowledge. If that bridge is riddled with potholes, searches miss the right terms, filters misbehave, and redactions or metadata can feel out of sync with the content. In a project environment, that leads to miscommunication, duplicated work, and audits that take longer than they should. A clean, trustworthy index is like having a precise map when you’re steering a large initiative. It helps teams locate the latest approvals, track changes, and prove compliance when questions arise.

Let’s map out the options you might encounter

When OCR text isn’t reliable, you’ll hear four common approaches:

• A. Make manual corrections only.

• B. Run an incremental build of the index.

• C. Rebuild the index entirely.

• D. Archive the OCRed documents.

The right move is C: rebuild the index entirely. Here’s why.

Why a full rebuild beats the other routes

• Manual corrections can fix obvious errors, but they often become a patchwork. If you’ve got hundreds or thousands of pages, you’ll end up with inconsistent corrections scattered across documents. A full rebuild resets the entire text layer, so every document starts from a clean slate, reflecting updated content in a uniform way.

• Incremental builds sound efficient, but they can miss big structural improvements. After you correct OCR on a chunk of documents, those fixes may not propagate cleanly if the index has grown complex or fragmented. A complete rebuild makes sure every change is integrated across the board, which translates to more reliable search results.

• Archiving OCRed documents preserves what’s there, but it doesn’t fix the core problem. The search experience becomes less useful, and the work you do later to locate items or prove status can still be hampered by inconsistent, inaccurate text.

• In project work, accuracy isn’t a luxury; it’s a prerequisite for decisions, approvals, and traceability. A full rebuild gives you a solid foundation for all future work, from daily task updates to formal reviews.

What a full rebuild actually achieves

• Clean integration of corrected text: You’re replacing a jumble with a coherent, unified text layer that aligns with corrected originals.

• Consistency across the document set: No doc stands apart with odd spellings, incorrect words, or missing terms.

• Better searchability: Users can find terms they expect, because the index reflects the corrected content accurately.

• Clear auditability: You can show exactly what was corrected and how the index was rebuilt, which matters for compliance and governance.

A practical, step-by-step approach to the rebuild

If you’re working inside Relativity or a similar platform, here’s a pragmatic way to implement a full index rebuild without turning the project into a rerun nightmare.

1. Assess and plan

• Identify the scope. Which collections or repositories show OCR quality problems? Are there language issues or mixed media? Note the edges where OCR struggled (numbers, tables, handwriting, unusual fonts).

• Check dependencies. Make sure the people and systems that rely on the index know when the rebuild will happen and what to expect post-rebuild (search behavior, saved searches, dashboards).

2. Gather and verify corrected text

• Collect the corrected OCR outputs. If you have corrected text at the document level, assemble it in a centralized place.

• Sanity-check a sample. Pick a subset of documents that cover different types (contracts, emails, scanned forms) to confirm the corrections look right before you reprocess everything.

3. Reprocess and re-OCR (as needed)

• If you can, rerun OCR on the source material using updated settings (language packs, improved recognition for technical terms, layout detection). This step ensures the text layer reflects the latest corrections and captures any new improvements in OCR technology.

• Update metadata where it matters. Ensure that extracted metadata aligns with the updated text so filters and facets stay meaningful.

4. Rebuild the index from scratch

• Start with a clean index rebuild. This step is the core decision that resets the search layer to reflect the corrected content.

• Monitor the process. Large document sets can take time. Have a plan for downtime or a staged approach if the environment supports it. Communicate expected timelines to stakeholders so the change doesn’t feel like a surprise.

5. Validate and verify

• Run representative searches. Test common queries your team uses in daily work. Do results align with what you expect given the corrected text?

• Compare before and after. Look at a few critical terms and verify that results have improved without introducing new false positives.

• Check redactions and sensitivities. Make sure any confidentiality rules stay intact and that redacted sections stay protected in the index.

6. Roll out and document

• Communicate outcomes. Share a short summary of the improvements, any changes in search behavior, and what users should expect when they look up terms.

• Log what changed. Keep an audit trail of the rebuild, including dates, scope, and the OCR corrections applied.

• Plan for future growth. Decide whether new documents will be added incrementally after the rebuild or whether a rolling rebuild schedule makes sense for your team.

Tips to keep the process smooth

• Backup everything. Before you start, snapshot the current index and the document set. If something goes off the rails, you’ll have a safe rollback point.

• Use a staging environment. If your workspace supports it, test the rebuild in a staging area before touching production. It’s a small step that saves big headaches.

• Coordinate with teams. Let reviewers, compliance, and IT know what’s happening. A clear communication plan prevents duplicates and misaligned expectations.

• QA with real-world queries. Think like a user: what would someone actually search for? Make sure those queries yield the expected results post-rebuild.

• Plan for new content. After the rebuild, you’ll likely ingest new documents. Set a lightweight process to OCR and index those items promptly so the index stays current.

A few practical caveats

• Language and layout matter. If your documents come in multiple languages or include dense tables, fine-tuning OCR settings can yield better results. Don’t skip language detection and layout analysis; they can dramatically improve text extraction.

• Data integrity matters. If metadata or doc IDs don’t line up with the corrected text, you’ll chase mismatches. A careful mapping between text and metadata is worth the extra attention.

• It’s not a one-and-done fix. OCR quality can regress with new documents or new types of files. Build in a lightweight review habit to catch drift early.

A quick analogy to keep it grounded

Imagine your document collection is a library, and the index is the catalog that helps people find exactly what they want. If the catalog has misprinted author names, scrambled titles, and broken cross-references, patrons wander, get frustrated, and waste time. Rebuilding the index is like printing a fresh, accurate catalog from clean, verified notes. It’s not glamorous, but it makes the library hum—every search, every inquiry, every reference lands where it should.

Putting it into perspective for project work

In projects, you rely on clarity more than ever. Teams depend on fast, accurate access to agreements, change orders, risk logs, and decisions. The moment OCR quality undermines that access, momentum slows. A full index rebuild isn’t a flashy fix; it’s solid engineering that pays off in smoother collaboration, quicker approvals, and fewer painful questions about what the document actually says.

Final takeaway

OCR problems are common in large document sets, but they don’t have to stall a project. When the text layer is unreliable, rebuilding the index from the ground up offers the most dependable path to consistency, trust, and efficient information retrieval. It makes search results more meaningful, decisions more informed, and audits more straightforward. If you’re facing a batch of poor OCR, think of the rebuild as a restart that cleanly integrates corrected text and resets the entire search experience onto solid footing.

If you’d like, I can tailor these steps to a specific Relativity setup you’re working with—different teams, different document types, or a particular workflow you want to streamline. The core idea remains the same: a fresh index built on accurate text is the best foundation for reliable, speedy access to the information your project relies on.