OCR quality and document search: lessons from messy archives
Digitization projects fail when teams chase page counts instead of recall—focus on DPI, deskew, and dictionary hints.
Garbage scans, garbage index
Skewed faxes and copier streaks produce gibberish tokens that poison search relevance. Auto-deskew and denoise before OCR when budgets allow.
DPI matters: 300 is a common archival minimum for small print; upscaling blurry phones doesn’t invent detail.
Language packs must match content—mixed English/Hindi needs models tuned for both.
Metadata alongside text
Capture Bates numbers, box IDs, and custodian fields as structured metadata—not buried in paragraphs. Search UIs need facets.
Timestamp ingestion events for legal hold drills.
Redact PII post-OCR using patterns validated by counsel—regex alone hurts.
Testing recall scientifically
Sample queries from actual attorneys or auditors; measure precision@k monthly. Tune stemming and synonyms intentionally.
Log failed queries anonymously—product roadmap gold.
When migrating search engines, run dual indexes briefly to diff results.
Operational costs
OCR vendor pricing explodes at page scale—negotiate burst credits and cache repeated pages.
Cold storage tiers for bitmaps vs text-only derivatives balance budget.
Automate quarantine pipelines for unreadable pages rather than silently dropping them.
Human QA loops
Spot-check random pages per thousand; escalate systematic errors to model vendors.
Train domain experts to flag tables and handwriting separately—different repair paths.
Celebrate teams that document weird glyph failures—patterns become playbooks.
Merge AI connections
Use image-to-text flows for quick spot checks before enterprise OCR batches commit.
Cross-link compress and PDF tools when downstream sharing demands smaller packages.
Feedback on real-world failures helps prioritize model refresh roadmaps.