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The IDP Pipeline Explained: From Raw Documents to Real Intelligence

By Vimal Cherangattu | 2025 Jul 04

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5 min

Smart Data Extraction is the core of Intelligent Document Processing (IDP)—turning messy, unstructured documents into clean, labeled data that AI and automation can use. Apryse’s SDK does this with no templates, no cloud lock-in, and high accuracy—making your documents AI-ready, securely and at scale.

Intelligent Document Processing (IDP) represents the next evolution of OCR—advancing the field with AI to transform how enterprises turn unstructured documents into structured, usable data. It’s not just about extracting data from PDFs. It’s about transforming entire document workflows—contracts, invoices, forms, reports—into structured, searchable, and actionable inputs that reduce manual data entry, enable automation, and feed AI and decision systems.

But IDP isn’t one tool or product. It’s a pipeline of capabilities, and knowing what each layer does is critical to building a system that performs reliably and scales with your needs.

In this post, we’ll break down what the modern IDP pipeline looks like, which components are essential, where the market is headed—and where Apryse’s Smart Data Extraction fits into it.

What Is IDP, Really?

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IDP stands for Intelligent Document Processing, and at its core, it refers to using technologies like OCR, machine learning, and natural language processing (NLP) to extract, understand, and process data from documents—digitally, intelligently, and at scale.

This isn’t about just “scanning PDFs.” It’s about feeding downstream systems (like automation tools, databases, and AI models) with structured, labeled data that used to live in PDFs, DOCX, scanned forms, or multi-page reports.

The Market Is Growing—Fast

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According to Grand View Research, the global IDP market was valued at $2.3 billion in 2024 and is projected to grow at a CAGR of 33.1%, reaching $12.35 billion by 2030.

What’s driving this?

  • The rise of domain-specific AI models (Small Language Models or SLMs)
  • The cost of manual data entry and review
  • Growing compliance requirements
  • Enterprise appetite for automation that’s actually reliable

The Core Components of an IDP Pipeline

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Here’s how a modern IDP system breaks down:

1. Document Ingestion & Pre-Processing

This stage prepares documents for extraction and AI by handling:

  • Deskewing
  • Rotation correction
  • Noise reduction
  • Multi-column layout handling
  • Format normalization
  • Page splitting & merging

2. Classification

The system identifies what kind of document it is—invoice, contract, claim form, etc.—often using ML models or rule-based logic. Classification informs what kind of extraction rules should follow.

3. Data Extraction (This is where Smart Data Extraction fits in)

This is the heart of the pipeline: transforming raw, unstructured content into structured, labeled, and context-aware data. It's not just about pulling text—it’s about understanding the structure and semantics of a document.

Smart Data Extraction goes beyond traditional OCR by identifying:

  • Key-value pairs (template-free)
  • Tables, including merged cells
  • Visual form fields on flat PDFs
  • Layout hierarchy—headers, sections, zones
  • Semantic structure using NLP

Instead of dumping raw text, it outputs clean, labeled JSON, XML, Excel, or CSV—data that’s ready for downstream automation, analytics, or AI.

Data is great. Data with context is better. Data embedded in your workflow is best. Smart Data Extraction gives you just that—a foundation of reliable, structured data to feed your AI models, automate regulated workflows, or power document-driven features.

It’s not just extraction. It’s how your documents become AI-ready.

4. Validation & Post-Processing

  • This stage ensures the extracted data is accurate, complete, and consistent—before it flows into downstream systems. It often combines:
  • Rule-based checks (e.g., field formats, totals, or logic rules)
  • Confidence scoring from AI models
  • Manual review via human-in-the-loop workflows for edge cases or exceptions

By blending automation with human oversight, this step ensures high data quality—especially in regulated or high-stakes environments.

5. Integration & Workflow Automation

Finally, the structured data feeds into downstream systems: CRMs, ERPs, RPA bots, search engines, or AI models. In AI pipelines, this data is often used for SLM training, RAG (retrieval-augmented generation), or summary generation.

The Role of Smart Data Extraction in the Document Processing Pipeline

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Apryse Smart Data Extraction handles one of the most critical stages in modern document workflows: turning unstructured files into clean, labeled, and structured data that AI and automation systems can actually use.

Before any extraction happens, it performs advanced preprocessing—correcting skewed pages, detecting orientation, cleaning noise, and handling complex layouts like multi-column or rotated documents. This ensures higher accuracy downstream, especially with scans and messy real-world files.

Once preprocessed, Smart Data Extraction uses YOLO-based layout detection and BERT-powered NLP models to identify structure, extract key-value pairs, parse tables, and understand document hierarchy—all without templates.

It’s fully SDK-based and built to run in secure environments—on-prem, offline, or air-gapped—with structured outputs in JSON, XML, Excel, HTML, CSV, and XFDF/FDF.

Smart Data Extraction isn’t a workflow engine or classifier. It’s the structured data layer that cleanly plugs into AI pipelines, RPA tools, and document-driven products—giving them the quality inputs they need to perform.

Why This Matters for Enterprises

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Enterprises don’t want a black-box solution—they want control, transparency, and security. Smart Data Extraction delivers:

  • Full deployment flexibility (on-prem, hybrid, offline)
  • No cloud lock-in or third-party data exposure
  • Privacy-first architecture—data stays within your environment
  • High accuracy in noisy or unstructured layouts
  • Structured output that AI can actually use

Whether you're automating invoice processing or training a domain-specific AI model, secure and reliable data extraction is non-negotiable.

The Future of IDP Is Modular

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  • The IDP landscape is shifting toward modular, composable stacks—not monoliths. Enterprises are assembling their workflows using best-in-class tools for each layer:
  • A Smart Data Extraction engine to structure the content
  • A document classifier to route files
  • A rules engine or AI validator
  • An RPA tool or integration layer

This modularity gives teams more control, lower costs, and better outcomes. And Apryse is at the center of that transformation.

Ready to build smarter document workflows? Contact Sales or Start Your Free Trial to see Smart Data Extraction in action.

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Vimal Cherangattu

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