Scanned records extraction
Verified OCR and structured-field extraction from aircraft records images
This review converts scanned aircraft records into verified structured fields without losing source trace. EE uses OCR and AI-assisted extraction to identify document type, aircraft or component, dates, part and serial data, signatures, release references, and status fields. Specialists review low-confidence and decision-critical data. The output is a structured data set with source links, confidence status, and an exception queue.
When this review is needed
- A counterparty, auditor, designee, or reviewer may challenge the package during scanned archive needs structured data.
- The file contains duplicates, scans, or inherited status lists.
- The team wants every exception tied to a source record.
- Open items must be separated from false extraction flags.
The problem
Scanned records are readable to humans but often unusable for search, migration, or analytics. OCR can misread serial numbers, split dates, miss handwritten entries, or attach a field to the wrong page.
What gets reviewed
- Extract scanned logbook images using the source file and note the evidence path.
- Trace handwritten task card pages using the source file and note the evidence path.
- Confirm certificate page images using the source file and note the evidence path.
- Flag OCR confidence output using the source file and note the evidence path.
- Package verified structured fields using the source file and note the evidence path.
Scope this review
Tell us the asset, the event, and the evidence in scope, and we will outline a focused first engagement.
Send a representative, redacted record set and we will scope the review.
What gets validated
- For scanned logbook images, the check passes only when the source and summary agree.
- Unmatched documents are tested both ways: claim to source and source to claim.
- Specialist review decides whether each mismatch is clerical, missing evidence, or a substantive gap.
- The final register must show the unresolved owner for every open item.
Evidence normally required
- Scanned logbook images
- Handwritten task card pages
- Certificate page images
- OCR confidence output
- Verified structured fields
Common discrepancies
- Handwriting misread into plausible but wrong numbers.
- Stamps and signatures missed entirely.
- Multi-column logbook layouts scrambled during extraction.
What is at stake
Bad extraction can create wrong status, weak search results, and migration errors. The problem compounds when extracted fields are loaded into a system without retained source links.
How the work runs
Define extraction fields
Set the record types, fields, source links, and confidence thresholds for the project.
Extract from scans
Run OCR and AI-assisted classification against the scanned source records.
Review exceptions
Check low-confidence, handwritten, conflicting, and decision-critical fields.
Deliver structured data
Return verified fields, source links, rejected values, and review queues.
What the buyer receives
- scanned extraction discrepancy register
- source-linked evidence map
- risk-ranked closure plan
- missing-record request list
Who uses the output
- records manager use the register to decide which exceptions affect the event.
- records data lead use the evidence map to request or close source records.
- Aircraft records teams leaders use the summary to brief the next approval, release, or deal meeting.
How the work fits into the transaction or program
This belongs before records migration, digitization acceptance, search-index build, or gap detection. It does not replace the scanned source record. It creates structured data that reviewers can trace back to the original page.
Start with a single asset
Confirm the status list matches the underlying evidence.
Regulatory limits
This review does not replace required maintenance, inspection, airworthiness review, or authority action. It documents evidence gaps and consistency problems for the accountable team.
What this review does not cover
- Final acceptance signing
- Airworthiness certification
- Commercial valuation opinion
- Operator procedure approval
Specific to this review
- Each extracted field keeps a source page, confidence status, and review state.
- Decision-critical fields receive stronger review than search-only fields.
- AI helps extract and classify data, but specialists verify ambiguous or high-impact fields.
- OCR output is treated as a draft until reviewed or accepted under a defined threshold.
- The output supports indexing, migration, and later records review.
Sources
Federal Aviation Administration. FAA acceptance criteria for electronic recordkeeping systems and electronic signatures.
Federal Aviation Administration. FAA guidance on making and keeping maintenance records and acceptable recordkeeping practices.
Frequently asked questions
Can extracted fields replace the scanned records?
No. The extracted data is a working layer. The scanned source remains the record.
Which fields need human review?
Fields that affect status, trace, release, due dates, serial identity, or acceptance need review before use.
Relevant glossary terms
Related pages
Where this fits
Talk to an engineer who has done this work
We will walk through your current state, the records or evidence involved, and a scoped first engagement.
Talk through the aircraft, records, evidence, deadline, and next useful step.