A certificate of analysis (CoA) is one of the most information-dense documents in regulated industries. It carries test results, method references, accreditation details, chain-of-custody information, and the laboratory’s sign-off — all in a format designed for human reading, not machine parsing.
Contracts are the hardest document type to extract data from reliably. Invoices have a predictable structure. Lab reports have defined fields. Contracts are natural language documents, and the information you need — key dates, party names, payment terms, renewal clauses, termination conditions — can appear anywhere, phrased in many different ways, across documents that range from two pages to two hundred.
Customs declarations are among the most error-sensitive documents in logistics. A wrong tariff code or an incorrectly extracted commodity value can trigger delays, fines, or hold actions. At the same time, import/export operations process hundreds or thousands of declarations per month, and the manual effort of verifying and entering data from these documents is substantial.
Purchase order extraction appears simple on the surface. A PO has a number, a date, a list of line items, and a total. If your business receives POs only from customers who use a single consistent format, a script handles it fine.
Structured documents have a predictable, machine-readable layout — the same fields in the same positions, every time. Unstructured documents present information in free-form natural language, where the relevant data could be anywhere and phrased in any number of ways.
The distinction matters because it determines your extraction approach. Structured documents can be extracted reliably with rules. Unstructured documents require more sophisticated methods, and reliable extraction is harder to guarantee.
Document classification is the step in an extraction pipeline that identifies what type of document has arrived before any field extraction begins. In a pipeline that handles multiple document types — invoices, purchase orders, lab reports, contracts — classification routes each document to the extraction logic designed for it.
Document validation is the step in an extraction pipeline that checks whether the extracted data is internally consistent, correctly formatted, and plausible — before that data passes to any downstream system.
Extraction produces values. Validation determines whether those values are correct. The two steps are distinct, and skipping validation is the most common reason extraction errors reach production systems undetected.
Layout variation is when the same document type arrives in structurally different formats from different sources — or from the same source at different points in time.
An invoice from Supplier A puts the total in the bottom-right cell of a table. Supplier B puts it in a labelled field on the right-hand side. Supplier C embeds it in a paragraph: “The total amount due is £1,234.00.” All three are invoices. All three require different extraction logic to reliably get the same value.
OCR post-processing is the set of steps applied to raw OCR output to clean, normalise, and correct it before extraction logic runs against it. Raw OCR output is rarely clean enough for reliable field extraction — post-processing is the step that makes it production-usable.
Table extraction from PDFs is the process of identifying tabular structures in a document and converting them into structured, row/column data. It sounds straightforward because tables look structured — but PDF tables have no standard internal representation, and parsing them reliably across varied formats is one of the most technically demanding extraction problems.