What Is Intelligent Document Processing (IDP)? A Guide

Intelligent document processing (IDP) combines OCR, machine learning, and natural language processing to turn scanned or digital documents into structured, usable data — classifying each document, extracting the fields that matter, validating them, and routing the result into a business system. It goes beyond plain OCR by understanding what the extracted text means, and beyond traditional RPA by coping with documents that never look exactly the same twice. This guide covers how IDP actually works, where it differs from OCR and RPA, what documents it typically processes, and how to evaluate a platform.

What is intelligent document processing?

AWS defines intelligent document processing as automating "the process of manual data entry from paper-based documents or document images to digital form, for integration with other digital business processes." Its own example is an invoice: a supplier emails a PDF, and instead of someone in accounts payable retyping the numbers by hand, an IDP system reads the invoice, pulls out the vendor, amount, and due date, and feeds them straight into the accounting system.

Automation Anywhere describes the same idea from the technology side: IDP "combines optical character recognition (OCR) with artificial intelligence (AI) and machine learning (ML) algorithms to automate the processing of complex documents in variable formats," and unlike plain OCR, it "can also understand the context and meaning of the information." That's the core distinction: OCR gets you the characters on the page; IDP figures out which characters are the invoice number, which are the line items, and whether the total actually adds up.

IDP vs. OCR: where one ends and the other begins

Automation Anywhere's own FAQ answers this directly: "Optical character recognition (OCR) is just one component of IDP. OCR is a technology that recognizes and converts printed or handwritten text into digital form, while intelligent document processing (IDP) involves a more advanced process that not only extracts data but also classifies, validates, and integrates it for use in business processes." OCR answers "what does this text say?" IDP answers "what does this document mean, and what should happen next?"

We cover the OCR layer itself in detail elsewhere — see our guides to the best OCR software and how to extract text from PDFs. What matters here is that OCR, and variants like intelligent character recognition for handwriting, is the input layer of IDP, not the whole system.

IDP vs. RPA: two different jobs that get bundled together

AWS describes robotic process automation (RPA) as "a form of technology that facilitates the building and deployment of software that automates human actions": a user records how they process a document, and the RPA software "then repeats the same steps," eliminating manual work. That holds up well when the input is predictable — the same form, in the same place, every time.

Documents are rarely that cooperative. A supplier changes its invoice template, a scanned form arrives slightly rotated, a contract runs to forty pages of free-form text — and a bot built to replay fixed steps has nothing to fall back on. That's the gap IDP fills: it supplies the structured, validated data that an RPA bot, or increasingly an AI agent, can then act on. Both ABBYY's Vantage platform and UiPath's document understanding product are built explicitly to sit in front of RPA tools — ABBYY lists out-of-the-box integration with Power Automate, Blue Prism, UiPath, and Automation Anywhere, and UiPath frames its own IDP as turning "any content into actionable insights that power your AI agents and automations." IDP and RPA are complementary layers, not competing products.

How intelligent document processing works

Vendors describe the pipeline slightly differently, but Automation Anywhere, ABBYY, and Google's Document AI all break it into the same handful of stages.

Input and pre-processing

Documents arrive from wherever business documents actually show up — email attachments, scanned batches, mobile photos, shared folders, or a direct API call. Before anything else happens, image pre-processing cleans up the source: binarization, noise reduction, de-skewing crooked scans, and de-speckling, so the OCR step that follows has a cleaner image to work from.

OCR and character recognition

Next, OCR and intelligent character recognition (ICR) digitize the printed and handwritten text and identify the document's structure, including tables. Microsoft's Document Intelligence splits this into separate models — a "Read" model for printed and handwritten text, and a "Layout" model for text, tables, and document structure — a useful reminder that recognizing characters and understanding layout are two distinct steps, not one.

Classification

Before a document can be processed correctly, the system has to know what it is. Classification models, trained on both text and image features, sort incoming documents into types — invoice, purchase order, ID, contract — and route each one to the right extraction logic. Google's Document AI groups its own processors the same way, with dedicated "classify" processors (custom classifiers and splitters) kept separate from the ones that extract data.

Extraction and validation

This is where the specific fields come out: dates, amounts, names, line items. Platforms mix pre-trained models for common document types, custom-trained models for specialized ones, and increasingly large language models for free-form or unusual layouts. Microsoft's prebuilt invoice model, for example, returns each field as a typed value — a date, a currency amount, an address — rather than raw text, so a downstream system doesn't have to parse it again. Extracted data is then validated: cross-checked against rules, regular expressions, or existing records to catch errors before they reach a business system.

Human-in-the-loop and continuous learning

Low-confidence extractions or exceptions get routed to a person instead of being silently guessed at. ABBYY describes this as core to how its models improve, noting that its skills get "more accurate over time, with new document variations introduced and statistical data collected during human-in-the-loop review." UiPath frames it as keeping "experts involved and engaged" to "quickly validate extractions to resolve inaccuracies and exceptions." This step is optional in principle but load-bearing in practice: it's what stops small classification or extraction errors from compounding at scale.

Integration and output

Once validated, the data is exported — typically as JSON, CSV, or XML — and passed into whatever system needs it next: an ERP, a CRM, an RPA workflow, or an AI agent that acts on the information. This is the step that actually delivers the automation; everything before it just gets the data into a shape a machine can use.

What documents does IDP actually process?

The technology is document-agnostic, but a handful of categories account for most deployments. Microsoft's Document Intelligence ships prebuilt models for exactly these types, which is a reasonable proxy for where demand concentrates:

By industry, Automation Anywhere lists banking and finance (KYC checks, loan packets, account servicing), healthcare (patient records, insurance claims), insurance (property and casualty claims, policy issuance), logistics (bills of lading, customs declarations, commercial invoices), and HR (resumes and onboarding forms) among the most common use cases. AWS adds legal document review, where NLP is used to analyze a contract's terms and obligations rather than just pull out fields.

Main types of IDP platforms

"IDP platform" covers a few genuinely different products, and the right category depends on whether you have engineering resources, an existing RPA stack, or neither.

Cloud provider document AI APIs

Amazon Textract and Amazon Comprehend, Azure AI Document Intelligence, and Google Document AI expose OCR, classification, and extraction as API calls, with prebuilt models for common document types and tooling to train custom ones. This route suits teams with developers who want to build the extraction step into their own application rather than adopt a separate interface. Pricing here is usage-based (per page or per document processed), not a flat license fee.

Low-code and no-code IDP platforms

ABBYY Vantage is built around pre-trained extraction "skills" for specific document types — the vendor lists pre-trained skills covering more than 150 use cases in its marketplace — designed and adjusted by non-developers through a visual skill designer, with a stated starting accuracy of 90% before any tuning. Platforms in this category get a business team processing documents without writing extraction code, at the cost of less flexibility than a raw API.

IDP built into RPA and hyperautomation suites

Automation Anywhere's Document Automation and UiPath's Intelligent Document Processing ship as modules inside those vendors' broader automation platforms, so extracted data flows directly into the same vendor's bots or AI agents without a separate integration step. This suits organizations that already run one of those platforms for process automation and want document handling to live in the same environment rather than a second vendor relationship.

Pricing across all three categories is quote-based or usage-tiered rather than published as a flat number — check each vendor's own pricing page for current terms.

How to evaluate an IDP platform

• Document coverage. Does it handle your actual mix of structured (forms), semi-structured (invoices), and unstructured (contracts, emails) documents — including handwriting, tables, and barcodes if you need them?
• Prebuilt vs. custom models. If your document types match a prebuilt model (an invoice, a W-2, an ID card), you can go live fast. If they don't, you're training a custom model, which means gathering labeled training data — check how much the vendor requires and who does the labeling.
• Confidence scoring and human-in-the-loop. Ask specifically how the platform flags low-confidence extractions, who reviews them, and whether corrections retrain the model automatically or only fix that one document.
• Entity and language handling. Extracting a field is different from understanding it; platforms that layer named entity recognition on top of extraction can distinguish, say, a company name from a person's name inside the same contract.
• Integration path. Does the output plug into your ERP, CRM, or RPA tool through a documented API and connectors, or does it produce a file you still have to move by hand?
• Governance and compliance. Look for audit trails on who accessed or corrected which document, and — if you handle regulated data — built-in redaction of personally identifiable information.
• Build vs. buy. Automation Anywhere's own comparison is candid about what building an in-house IDP system costs: specialized ML talent, ongoing maintenance, and the risk that a homegrown model falls short on governance and accuracy without the tooling a vendor bundles in. That's a vendor making the case for buying, but the underlying cost categories — talent, maintenance, and governance tooling — are worth pricing out honestly either way.

Frequently asked questions about intelligent document processing

What is intelligent document processing?

Intelligent document processing (IDP) is technology that combines OCR, machine learning, and natural language processing to classify documents, extract specific data fields from them, validate that data, and route it into business systems — automating work that would otherwise require someone to read and retype information by hand.

What is the difference between OCR and intelligent document processing?

OCR converts the text in an image into machine-readable characters — it tells you what's written on the page. IDP is the larger system built around OCR: it classifies the document, extracts the specific fields that matter, validates them, and hands the structured result to another system. As Automation Anywhere puts it, OCR is "just one component of IDP."

What is AWS Intelligent Document Processing and how does it work?

AWS doesn't sell a single product called "IDP" — it offers two services that together cover the job. Amazon Textract extracts printed and handwritten text, layout, and data from documents using machine learning, without manual templates. Amazon Comprehend is a natural language processing service that pulls insights, entities, and sentiment out of that text, and can identify and redact personally identifiable information. Used together, they cover the OCR and language-understanding halves of an IDP pipeline.

What is the best intelligent document processing software?

There isn't a single best platform — the right choice depends on whether your documents match a vendor's prebuilt models, whether you already run an RPA platform you want IDP to plug into, and whether you have engineering resources to work with a raw API. Cloud provider APIs (AWS, Azure, Google), low-code platforms (ABBYY Vantage), and IDP built into RPA suites (Automation Anywhere, UiPath) solve the same underlying problem for different teams and budgets.

Can intelligent document processing handle scanned PDFs and handwriting?

Yes. Standard OCR handles printed text in scanned PDFs, and intelligent character recognition (ICR) — an ML-based variant — is designed specifically to read handwriting, including inconsistent or cursive forms. Pre-processing steps like de-skewing and noise reduction typically run first to clean up a scanned image before either technology reads it.

What are some examples of intelligent document processing in use?

Common examples include automating invoice and purchase order processing in accounts payable, extracting patient information from healthcare intake forms, verifying identity documents during customer onboarding (KYC) in banking, processing insurance claims and policy documents, and pulling shipment details from bills of lading and customs declarations in logistics.

Once documents are flowing as structured data, the harder question is usually what feeds on it next — see our broader look at AI tools for business by use case.