Straight-through processing (STP) is the automated handling of a document or transaction from receipt to completion without any manual intervention.
What STP means in document processing # A document arrives. Data is extracted from it. The extracted data is validated against expected formats and business rules. The validated output is delivered to a downstream system — an ERP, a database, a workflow tool. All of this happens automatically, with no human touching the document at any point.
OCR (Optical Character Recognition) is software that converts images containing text — scanned documents, photos of pages, image-based PDFs — into machine-readable characters.
How OCR works # An OCR engine does not read text the way you do. It processes an image and works through several stages to identify what characters are present.
Document automation means different things to different people. For some, it means generating contracts automatically from a CRM record. For others, it means extracting data from the 400 invoices that arrive each month and getting it into their accounting system without anyone retyping it.
Document automation is the use of software to handle documents that would otherwise require manual processing — either creating documents from data, or extracting data from incoming documents.
Two categories # Document generation means creating documents automatically from templates and data. Contracts, reports, invoices, letters. You have structured data; you need a formatted document. A template defines the layout; the software fills it in. This is well-understood, widely implemented, and mostly solved.
The obvious cost of manual document processing is staff time. Someone opens the document, reads it, and types values into a system. That’s visible, budgetable, and easy to defend leaving in place because it feels controllable.
The less obvious costs are harder to see on a spreadsheet. Errors that propagate into downstream systems. Documents sitting in a queue while operations wait. A team that can’t scale because headcount has to grow in lockstep with document volume. Most businesses underestimate the total cost because they only count the hours.
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.