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.
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.
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.
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.
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.
OCR and IDP are often used as though they mean the same thing. They don’t. OCR is a component; IDP is a system built around it. Treating them as synonyms causes two predictable mistakes: underbuilding (using OCR alone when you need structured extraction) or overbuilding (licensing an enterprise IDP platform for a use case that a few well-written regex patterns would solve).
Nanonets is a SaaS intelligent document processing platform founded in 2016, aimed primarily at small and mid-sized businesses. Its pitch is quick setup with pre-trained models for invoices, receipts, and purchase orders, and a no-code interface for training custom models on your own documents. For AP automation — getting data out of supplier invoices into an accounting system — it is a reasonable starting point.
Lab reports are among the harder document types for automated extraction. They come from multiple testing laboratories, each with a proprietary format built around their own LIMS software, reporting preferences, and historical conventions. The same parameter — say, nitrate concentration — might appear in a column headed “NO3-N (mg/L)”, “Nitrate as N”, or “NO₃⁻” depending on which lab issued the report. The value might be in a structured table, a semi-structured list, or embedded in narrative text alongside method references and QA annotations. A pipeline that works reliably on one laboratory’s reports needs to be explicitly designed and tested against each additional format. That’s not a limitation of the approach — it’s the nature of the domain.
Extracting vendor name, invoice number, date, line items, and total from a single consistent invoice format is a few lines of pdfplumber. If your company uses one internal invoice template and you control the format, that script will probably hold. The real problem appears the moment you have invoices from 30 different suppliers, each using a different layout, font, table structure, and occasionally a different currency format. That’s when a script becomes a pipeline — or it becomes a maintenance burden.
Logistics runs on paperwork. A single shipment from a manufacturer in Guangzhou to a distributor in Hamburg might require a bill of lading, commercial invoice, packing list, certificate of origin, customs entry, and a dangerous goods declaration — all of which need to be read, keyed into systems, and verified before anything moves.