Somewhere in a testing facility right now, someone is scrolling through a 47-page PDF looking for a single lot number. The document is a flammability test report for an airplane seat cushion, and the data they need is buried on page 31—sandwiched between a scanned packing list and a mechanical test report that got concatenated into the same file. They’ve been doing this for every parts order that comes through, manually verifying data across dozens of document types, thousands of pages a month.
This is the reality for small and medium aerospace parts testing operations. Customers submit part numbers, descriptions, and lot numbers through a web portal, then upload stacks of PDFs as supporting documentation. On the back end, someone has to manually crack open each PDF, figure out which pages belong to which document type, find the specific fields that matter, and verify everything matches. It’s tedious, error-prone, and—as one engineering consultant recently put it—“probably isn’t necessary.”
The problem isn’t that the verification doesn’t matter. Aviation parts compliance is serious business. The problem is that a human spending 20 minutes per submission scrolling through PDFs is the worst possible way to ensure accuracy. OCR for aerospace parts testing can replace that manual scrolling with structured, automated data extraction—turning messy document stacks into clean, verifiable CSV output in seconds.
Lido is the best OCR solution for aerospace parts testing facilities that need to extract lot numbers, test results, and compliance data from dense technical documents. It reads flammability reports, material certifications, and test summaries in any format — including scanned legacy documents and multi-page PDFs — without templates or manual field mapping. Testing facilities using Lido verify parts compliance in seconds instead of spending 45 minutes per document on manual cross-referencing.
Aerospace parts testing generates a staggering variety of documents. A single submission might include mechanical test reports, sales orders, packing lists, flammability test reports, material certifications, and inspection records. Each document type has its own format, its own layout, and its own set of critical fields. Some are digitally generated PDFs. Others are scanned paper documents with handwritten annotations, stamps, and signatures.
The core fields are deceptively simple. You need to extract part numbers (sometimes labeled as SKUs), descriptions, lot numbers, and document types. Four fields. But finding those four fields across 1,000 to 5,000 pages of mixed-format documentation every month is where the work explodes. A part number might appear in a header on a mechanical test report, in a table row on a packing list, and handwritten in the margin of a flammability test report—all within the same submission.
Document splitting adds another layer of difficulty. Customers often upload everything as a single PDF, which means someone has to figure out where one document ends and another begins. A 60-page upload might contain four separate test reports, two packing lists, and a sales order. Identifying those boundaries manually requires reading enough of each page to understand what you’re looking at—before you even start extracting data.
Manual PDF review doesn’t scale, and it doesn’t catch errors reliably. When you’re processing 1,000 pages a month, a single person can probably keep up. At 5,000 pages, you need multiple people, and consistency drops. Different reviewers interpret documents differently, miss different things, and develop different shortcuts. The very process that’s supposed to ensure compliance becomes a source of inconsistency.
Fatigue is a real factor. Scrolling through PDF after PDF, hunting for lot numbers and part descriptions, is exactly the kind of repetitive visual task where human attention degrades fastest. By the 30th document of the day, the reviewer isn’t scrutinizing pages the way they were at 9 AM. Critical mismatches between a submitted part number and the part number on a test report can slip through—and in aerospace, those mismatches have consequences.
The economics don’t work either. You’re paying skilled people to do what amounts to a search-and-copy task. The expertise these employees bring—understanding test specifications, knowing what constitutes a valid certification—gets wasted on the mechanical act of finding and transcribing data. Their time would be better spent reviewing flagged exceptions and making judgment calls on edge cases, not scrolling through packing lists looking for lot numbers.
Aviation document processing isn’t a generic OCR problem. Consumer-grade OCR tools can read clean, typed text from a standard PDF. Aerospace parts testing documents are a different animal. You need OCR that can handle scanned pages with variable quality, interpret handwritten entries alongside printed text, and understand enough about document structure to split a combined PDF into its component document types.
Field extraction needs to be context-aware. A part number on a mechanical test report looks different from a part number on a sales order. The label might say “Part No.,” “P/N,” “SKU,” or nothing at all—just a number in a known position on the page. Effective OCR for aerospace needs to understand these variations and extract the right value regardless of how it’s labeled or formatted.
Traceability is non-negotiable. Every extracted field needs to trace back to its source document and page. When an auditor asks where a lot number came from, you need to point to the exact page of the exact document—not just say “it was in the PDF somewhere.” This traceability requirement eliminates most simple OCR tools that just dump text without preserving document structure.
Security requirements add another constraint. Aerospace parts testing data isn’t classified, but it’s commercially sensitive and subject to industry compliance standards. The processing system needs proper data protection guarantees—encryption at rest and in transit, access controls, and audit logging. SOC 2 compliance is the baseline expectation for any vendor handling this kind of data.
The right OCR system turns a 20-minute manual review into a 30-second automated extraction. Documents come in through the existing web portal. Instead of landing in a queue for manual review, they’re automatically processed: the PDF is split by document type, key fields are extracted from each section, and the results are exported as structured CSV data ready for verification.
Document classification happens first. The system reads each page and determines whether it’s looking at a mechanical test report, a sales order, a packing list, a flammability test report, or another document type. This classification step—which takes a human reviewer several minutes per document—happens automatically and consistently, every time.
Field extraction follows, pulling part numbers, descriptions, lot numbers, and document types from each classified section. The system handles the full range of formats: typed text in digital PDFs, printed text in scanned documents, and handwritten entries on test reports. For handwritten fields, modern OCR models achieve accuracy rates that match or exceed human readers, especially on the kind of semi-structured forms common in parts testing.
The output is a clean CSV with one row per extracted record. Each row includes the source document, page number, and confidence score for every field. Low-confidence extractions get flagged for human review, which means your team’s expertise goes exactly where it’s needed—on the edge cases and ambiguous entries—instead of being spent on routine data transcription.
Template flexibility matters more than template precision. Aerospace parts testing involves documents from dozens of different labs, suppliers, and testing facilities. Each one has its own format. A solution that requires you to build and maintain a template for every document layout will drown you in configuration work. Look for systems that can extract fields based on context and content, not rigid page coordinates.
Handwriting support is essential, not optional. If even 10% of your incoming documents contain handwritten annotations, lot numbers, or test results, your OCR solution needs to handle them. Many OCR tools quietly fail on handwriting, returning blank fields or garbage characters. Test any solution against your actual documents—including the worst-quality scans you receive—before committing.
Integration with your existing workflow determines adoption speed. The best OCR system is useless if it requires your team to change how they receive and process documents. Look for solutions that can ingest documents from your existing upload portal, process them automatically, and deliver results in the format your downstream systems expect—whether that’s CSV, JSON, or direct database integration.
Security and compliance should be verifiable, not just claimed. Ask for SOC 2 Type II certification. Ask about HIPAA compliance—not because aerospace data is health data, but because HIPAA-grade security controls demonstrate a mature data protection posture. Ask where data is processed, how long it’s retained, and who has access. These aren’t nice-to-haves for aerospace. They’re table stakes.
Lido is built for exactly this kind of document processing challenge. You upload your PDFs—whether they’re clean digital files or scanned documents with handwriting—and Lido extracts the fields you need into structured, exportable data. No templates to build, no rules to configure. You tell Lido what fields to look for, and it finds them across whatever document formats your customers submit.
Document splitting and classification happen automatically. A single 60-page PDF containing mechanical test reports, packing lists, and flammability test results gets broken into its component documents, each one classified by type. The extracted data includes source page references, so every field traces back to its origin—giving you the traceability that aerospace compliance demands.
Lido handles the volume that aerospace parts testing generates. Whether you’re processing 1,000 pages a month or 5,000, Lido scales without requiring additional headcount. Your team reviews flagged exceptions instead of reviewing every document, which means the same staff can handle growing volume without the bottleneck of manual PDF review.
Security is built in, not bolted on. Lido is SOC 2 compliant and maintains HIPAA-grade data protection controls. Your documents are encrypted in transit and at rest, access is logged and auditable, and data retention policies are configurable to meet your compliance requirements. For aerospace parts testing operations that need to demonstrate data protection to their customers, this matters.
Lido turns stacks of aerospace test reports, packing lists, and compliance documents into structured, exportable data—automatically. Start extracting part numbers, lot numbers, and test results from your documents in minutes, not hours. Try it free with 50 pages, no credit card required.
Yes. Modern OCR systems like Lido use advanced machine learning models trained on handwritten text, not just printed characters. These models can accurately read handwritten lot numbers, part descriptions, annotations, and test results on scanned aerospace documents. Accuracy depends on legibility, but for the semi-structured handwriting typical of test reports and inspection forms, automated extraction matches or exceeds the consistency of manual human review—especially across high volumes where fatigue affects human accuracy.
Aerospace parts testing typically produces mechanical test reports, flammability test reports, material certifications, inspection records, sales orders, packing lists, and certificates of conformance. Each document type has its own format and layout, and they are often submitted together as a single combined PDF. An effective OCR solution needs to classify these documents by type, split them apart, and extract the relevant fields from each one—such as part numbers, lot numbers, descriptions, and test results.
OCR-based extraction systems maintain traceability by recording the source document, page number, and extraction confidence for every field they extract. When a lot number or part number is pulled from a flammability test report, the output includes a reference to the exact page it came from. This creates an auditable chain from the final CSV or database record back to the original source document, which is essential for aerospace compliance reviews and customer audits.
Lido maintains SOC 2 compliance and HIPAA-grade security controls, which exceed the data protection requirements of most aerospace parts testing operations. All documents are encrypted in transit and at rest, access is controlled and logged, and data retention policies are configurable. While aerospace parts data is typically not classified, it is commercially sensitive and subject to customer confidentiality requirements. SOC 2 certification provides independently verified assurance that proper security controls are in place.
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