Intelligent Document Processing (IDP) uses AI to read, classify, extract, and validate data from documents. It combines classification, AI-driven data extraction, automated checks, and human-in-the-loop validation, delivering business-critical data into downstream systems. The goal is clear. IDP turns unstructured and semi-structured documents into reliable structured data that teams can use immediately.
Optical Character Recognition (OCR) is the technology that converts images of text into machine-readable characters. Traditional OCR has been around for decades, and it still works well for simple documents, but struggles with varied layouts, mixed formats, handwriting, and context. As document volume grows and compliance expectations rise, manual entry and template-heavy capture slow teams down, increase error rates, and create audit risk. IDP addresses these limits by understanding document types, extracting the right fields, validating information, and sending accurate data to downstream systems.
Document intake (also referred to as document capture or ingestion) brings files in through scanners, email, watched folders, APIs, or uploads. The system accepts common file types and prepares them for document processing.
The system identifies document type, applies image cleanup, and normalizes layouts. Better image quality improves extraction accuracy, and classification ensures the appropriate extraction model is applied; for example, using an invoice model for invoices or a healthcare form model for patient records.
Machine learning models trained for specific document types capture key fields from structured, semi-structured, and unstructured documents with high accuracy.
Business rules, database lookups, and other validation checks confirm extracted values. Guided review screens handle exceptions and record a complete audit trail.
Clean, validated data moves to line-of-business applications, content platforms, data lakes, and analytics tools through connectors, APIs, or flat-file exports.
OCR extracts machine-readable text but does not classify documents or validate fields on its own. IDP handles document capture, classification, AI-powered extraction, validation, and export, reducing manual effort and improving accuracy across varied document types.
RPA automates user interface actions. It can move data, but it does not replace the document understanding that IDP provides. Many organizations use IDP with RPA so that high-quality data drives automated workflows.
The right IDP solution removes repetitive data entry and exception chasing so teams can focus on higher-value tasks.
An intelligent document processing platform shortens cycle times from intake to decision by streamlining classification, extraction, and validation.
IDP applies business rules and human review where needed so downstream systems receive trusted data.
The right intelligent document processing solution records decisions, corrections, and approvals to support internal controls and external audits.
Many intelligent document processing platforms handle peak volumes without sacrificing accuracy, control, or oversight.
ImageTrust provides intelligent document capture and AI orchestration inside existing web-based workflows. It benchmarks multiple data extraction approaches, selects the best option for each document type–and even each field within a document–and triggers guided human review when exceptions occur for things like low-confidence extraction accuracy. ImageTrust validates extracted values against reference systems, maintains an audit trail, and delivers clean data to content platforms and line-of-business applications through connectors, APIs, and a comprehensive library of native integrations. Teams adopt ImageTrust without disrupting current systems because the interface embeds where users already work.
Intelligent Document Processing (IDP) uses artificial intelligence to turn documents into reliable data. It combines document capture, classification, AI-powered extraction, and validation to feed downstream processes and workflow automation. IDP reduces manual work, improves accuracy, and delivers structured data for business process execution.
IDP accepts files from scanners, email, APIs, and watched folders, then classifies each document and prepares it for data extraction. AI-powered models, including deep learning and natural language techniques, capture the right fields from structured and unstructured documents, then apply validation against business rules and reference systems. Clean data moves to content platforms, data lakes, ERP, and CRM platforms through secure connectors and APIs.
DP solutions handle invoices, statements, purchase orders, bills of lading, medical forms, legal documents, contracts, claims, correspondence, and email. They support structured formats such as fixed forms, semi-structured layouts such as vendor invoices, and unstructured data such as free-text letters and legal contracts. The system adapts extraction logic to each document class, so document workflows stay consistent even when layouts vary.
OCR extracts machine-readable text but does not classify documents or validate fields. IDP adds document classification, AI-powered data extraction, validation, and export so teams receive structured, trusted data. Robotic Process Automation (RPA) automates clicks and keystrokes in applications. Many organizations pair IDP with RPA, so accurate data from IDP drives workflow automation, improves response times, and reduces rework.
IDP shortens cycle times, improves response times, and reduces backlogs by eliminating manual processing. It increases accuracy with validation rules and guided human review, which prevents downstream errors. Customers experience faster decisions, clearer status updates, and fewer requests for missing information, which improves overall customer experience and service quality.
Core IDP relies on discriminative AI models, including computer vision, natural language processing, and other machine learning methods that extract and validate fields. This is known as Document AI or Document Intelligence. While Document AI extracts data because it knows where to look, generative AI contextually understands a document’s meaning, enabling it to support document summarization, automated redaction suggestions, sentiment analysis, and complex knowledge lookups. Teams use generative AI with control points and audit trails so outcomes stay consistent with policy and compliance.
Modern IDP platforms expose REST APIs, webhooks, and prebuilt connectors for content management (ECM), ERP, EHR, and CRM platforms. They publish normalized, validated data in formats such as JSON, CSV, or XML, then update records, attach source documents, and trigger workflow automation. Governance controls, permissions, and logging maintain security across systems.
Validation checks confirm totals, dates, vendor IDs, and reference values before data moves downstream. Human-in-the-loop provides guided review for exceptions, captures user corrections, and trains future AI models. This feedback loop improves precision over time, reduces repeat errors, and keeps document workflows compliant and auditable.
Successful teams start with a focused use case that has clear volume, measurable savings, and well-defined outputs. They configure intake, classification, extraction, and data validation, then integrate with the target systems. After go-live, they expand to adjacent documents, reuse models, and apply the same controls to maintain quality at scale.
ImageTrust provides intelligent capture and AI orchestration that embed in web-based workflows. It benchmarks multiple AI models, selects the best approach per document type–or even per field within a document–applies data validation rules, and presents an industry-leading human-in-the-loop review experience for exceptions, where the data validation screen looks identical to the original document. ImageTrust integrates with content management systems, government records systems, EHR, ERP, and CRM platforms through connectors and APIs (native or custom), which deliver clean data to business process applications and keep an auditable trail from intake to decision.
ImageTrust makes intelligent document processing practical. Benchmark multiple AI technologies, embed intelligent capture in the systems your staff already use, and keep every correction and approval in an auditable trail. It’s a straightforward path to stronger compliance, faster decisions, and measurable ROI.
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