What is intelligent document processing (ICP)?

Intelligent document processing takes a document from "raw file" to "usable data" in several stages:

Stage 1️⃣, document input: IDP automatically collects documents from different sources and formats (scans, handwritten notes, emails, PDFs, photos, faxes, and more).

Stage 2️⃣, text capture with OCR: optical character recognition (OCR) converts the document content into machine-readable text. At this stage, images may be cleaned up, rotated, or split to improve recognition quality.

Stage 3️⃣, document classification and understanding: IDP uses natural language processing (NLP) to identify the document type and understand its content and context. For example, it uses surrounding words to decide whether "jaguar" refers to an animal or a car, or whether "Sue" is a person's name or a legal action. This lets the system sort each document into the right category.

Stage 4️⃣, data extraction: machine learning models extract key fields (names, dates, amounts, addresses, IDs, and other relevant values) by looking at both the layout (where things are on the page) and the text itself. Over time, these models can learn from corrections and adapt to new templates, formats, and languages.

Finally, the structured data is validated and sent to downstream systems such as ERP, CRM, RPA workflows, or analytics tools.

IDP helps organizations turn messy, document-based processes into structured, automated workflows, but it also comes with some constraints:

• Automates data extraction from PDFs, scans, emails, images, and forms
• Reduces manual data entry, human error, and processing time
• Improves data quality and consistency across systems
• Speeds up document-heavy processes (invoicing, claims, onboarding, KYC, etc.)
• Frees employees to focus on higher-value work instead of repetitive tasks
• Integrates structured data into ERP, CRM, RPA, and analytics tools for better visibility

• Accuracy depends on input quality (poor scans, messy handwriting, complex layouts)
• Requires initial setup, configuration, and model training or tuning
• Often needs human-in-the-loop review for exceptions and edge cases
• Can be complex or costly to implement at scale, especially with legacy systems
• Needs ongoing monitoring and retraining to maintain performance over time

IDP tools combine OCR, AI, and machine learning to read, extract, and structure data from documents automatically. Many platforms offer end-to-end IDP capabilities or include IDP as part of a broader automation suite. Common examples include:

• ABBYY Vantage / FlexiCapture – IDP platform for forms, invoices, contracts, and other complex documents.
• Kofax TotalAgility / Kofax Transformation – IDP and process automation for enterprises handling high document volumes.
• UiPath Document Understanding – IDP components integrated into the UiPath RPA ecosystem.
• Microsoft Power Automate with AI Builder – Uses AI models to extract data from forms and documents inside the Microsoft ecosystem.
• Google Document AI – Cloud-based IDP services for forms, invoices, receipts, and other business documents.
• Amazon Textract – AWS service that extracts text, tables, and key-value pairs from scanned documents.

IDP is used across many industries to automate manual, document-heavy workflows by extracting, structuring, and routing data from forms, PDFs, scans, and emails. Here are common examples:

Healthcare
IDP helps extract and organize information from patient records, lab reports, referrals, and discharge summaries so healthcare providers can maintain accurate, up-to-date records across all touchpoints. Health insurers also use IDP to process and verify claims faster, reducing manual paperwork and errors.

Finance
Financial teams use IDP to automate invoice and expense processing by capturing amounts, dates, vendors, and line items from invoices, receipts, and forms. It also supports payment processing, reconciliations, loan applications, and KYC/AML documentation by pulling key data into core finance systems.

Legal
In the legal sector, IDP is used to analyze and extract clauses, parties, dates, and obligations from contracts and agreements. It also helps process court filings, case documents, and evidence bundles, making it easier for legal teams to search, compare, and build cases based on large document sets.

Logistics
Logistics and supply chain organizations rely on IDP to process bills of lading, shipping manifests, customs forms, transit permits, and delivery notes. Automating data extraction and validation reduces human error and speeds up shipment tracking, clearance, and billing.

Human resources
HR teams use IDP to extract key information from resumes, applications, and onboarding documents, helping them quickly shortlist candidates and populate HR systems. IDP also supports payroll, benefits, and leave management by reading pay stubs, contracts, and internal forms, reducing manual data entry.

For a deeper dive into how IDP supports broader digital transformation, check our related article: