Comprehensive guide to Large Scale IDP Systems

Extracting any value from this kind of data at scale is incredibly challenging. This is where modern IDP systems help, they turn messy documents into clean and usable data.

However, to see why IDP is such a leap forward, let's first understand the traditional approaches to document processing.

How Document Processing worked before IDP 

The idea of automating document processing isn’t new. For years, businesses have long relied on software to speed up document handling and reduce manual work. One of the earliest solutions built for this was Automated Document Processing (ADP).

‍

Traditional ADP systems rely on fixed rules, predefined templates, and consistent formats to extract data from structured or semi-structured documents. They perform reasonably well when the layout is predictable, like invoices or application forms that follow the same structure every time.

In practice, this means someone manually creates templates for each document type. Extraction rules are written to pull out specific fields like names, dates, or totals. But the moment the layout shifts, even slightly, those rules start to break. Any change means going back in to fix or rebuild the logic.

This makes ADP workable only in tightly controlled environments. It struggles with variability. It can’t deal with documents that are unstructured or inconsistent. And most critically, it doesn’t learn or adapt. Once deployed, it stays static. 

But real-world documents are rarely that neat. Layouts change. Content shifts. And this level of messiness quickly overwhelms systems that rely on rigid rules.

That’s where Intelligent Document Processing (IDP) comes in, a system built to handle the complexity that ADP can’t.

What is Intelligent Document Processing (IDP) ? 

Intelligent Document Processing (IDP) is a method for extracting, classifying, and processing information from documents using techniques such as Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning, and in some cases, Robotic Process Automation (RPA). 

Unlike traditional rule-based systems, IDP is designed to handle documents with inconsistent formats, varied structures, and unstructured content without manual intervention or rigid templates.

It starts with OCR, which converts scanned images and PDFs into machine-readable text. NLP models then interpret the language and context, identifying key fields, sections, or entities. Deep Learning/Machine learning steps in to recognize patterns across different document types and adapt as more data flows through the system. In many setups, RPA is also used to automate the actions that follow—like updating databases, triggering workflows, or sending responses.

Together, these components allow IDP to work in messy, real-world conditions where structure is the exception, not the rule. It scales better, fails less often, and removes the need for constant rule maintenance, making it far more robust than traditional approaches like ADP.

But things get trickier when documents get longer.

Challenges in Large-Scale IDP Systems 

Large documents like contracts, policy manuals, financial reports, legal case files, bring a different level of complexity. It’s not just about pulling out text. It’s about capturing structure, preserving context, and maintaining consistency across hundreds of pages.

Here’s where things start to break.

Complex Layouts

Large documents come with a structure that’s hard to ignore—multi-column layouts, tables, sidebars, footnotes, headers, stamps. OCR alone treats all of this as flat text. That means merged columns, broken tables, and lost context.

Layout-aware models like LayoutLM and DocFormer solve this by combining text with positional and visual features. This helps the model understand that a price in a table isn’t the same as a number in a paragraph, even if they look alike.

‍

In the figure above, the green and red boxes are called “bounding boxes”. Bounding boxes help the system understand not just what’s written, but also where it appears on the page—so the layout and structure are preserved

Donut takes a different route. It skips OCR completely and reads document images directly using a vision transformer. Models like XDoc use layout graphs, treating content blocks as nodes and visual relations as edges to capture document structure. 

Without layout-aware models, the structure breaks down and the content loses meaning.

Cross Page-Context 

In small documents, everything you need is usually on one page. In large ones, that's rarely the case. Entities span pages, tables split across breaks, and references like “see clause 2.1” point to earlier sections.

Medical documents are a good example. A patient's diagnosis might appear early on, while supporting test results are buried deep in the appendix. If a system can’t connect the two, the output loses critical context.

Most basic models process one page at a time, so they miss these links. To solve this, newer approaches use long-context transformers like Longformer and BigBird, which can handle thousands of tokens and preserve context across pages.

A strong IDP pipeline needs this continuity. Without this, large documents just don’t make sense. 

High Variability

Even when documents fall under the same type—like contracts or manuals—their formats can be completely different. One contract might list terms in a table, another in plain text. One manual uses bullet points, another uses numbered steps. The wording, layout, and structure often change across teams or versions.

Fixed-template systems easily break in this setup. Layout-aware and pre-trained models, like LayoutLMv3, handle this better. They learn from patterns across documents rather than depending on fixed positions or templates. Instead of expecting the “price” to always appear in the same place, they learn what price looks like—in context.

This flexibility is key. The system has to generalize beyond the examples it’s seen, because in the real world, documents don't stick to a single format.

Volume, Scalability, and Cost  

Enterprises usually don’t deal with just a few documents. They process tens of thousands of pages every day, sometimes in real time.

This creates three major demands:

• Speed – the system must handle documents quickly, with low latency
• Accuracy – mistakes can be costly, especially in legal or regulated contexts
• Cost – cloud APIs often charge per page, and processing at scale adds up fast

Many use services like AWS Textract, Azure Form Recognizer, or Google Document AI. These offer built-in OCR and layout parsing, so one doesn't need to build models from scratch. But every page processed adds to the bill, and retries or post-processing push costs even higher. A good IDP setup has to deliver on all three fronts, it should be fast, accurate, and affordable, even at scale.

Core Technologies that Power IDP

We’ve already seen how IDP brings together multiple techniques to handle documents the way humans do. Now let’s take a closer look at the core technologies behind it, and how they actually work in an IDP pipeline.

Optical Character Recognition (OCR)

OCR is the foundation for making visual documents machine-readable. It converts scanned PDFs, images, and printed text into structured digital text that can be understood by the next steps in the IDP pipeline. OCR quality determines how well everything else performs. Misread characters, incorrect reading order, or broken layouts introduce noise that only gets amplified later. Getting this layer right is non-negotiable.

But in real-world documents, plain OCR isn’t enough. There are handwritten notes, tables buried in multi-column layouts, and tightly structured forms. To handle this, modern OCR engines bundle in a few key capabilities.

Intelligent Character Recognition (ICR)

ICR is designed for handwriting, think of handwritten forms or doctor’s notes. It deals with stylistic variations, messy handwriting, and faded text by using neural networks to learn from lots of writing styles and scripts.

Here’s what that looks like in practice: this diagram shows a typical ICR use case, a doctor’s handwritten report. Specific areas on the document, like "NS 1-2+" or "CC 2+", are manually written and refer to things like Nuclear Sclerotic and Cortical cataract grades.

The ICR engine zooms in on these regions, reads the handwriting, and interprets it into structured values. For example, it maps “NS 1-2+” to "Nuclear Sclerotic Severity: 1-2+" and “CC 2+” to "Cortical Severity: 2+".

Instead of just reading the text, it turns messy handwriting into clear, labeled information that can be used for things like reports, summaries, or decisions.

Layout Detection

We’ve already seen in the Complex Layouts section how important layout understanding is, especially for documents with columns, tables, and sidebars. OCR systems with layout detection help preserve structure and context, essential for making sense of complex documents.

Zonal OCR

Zonal OCR is built for structure. It extracts texts from predefined regions called ‘zones’ on the document. This is especially useful when the layout is fixed, like tax forms, pay slips, or boarding passes. Instead of reading the whole page, the system goes straight to the fields that matter. 

ZonalOCR does not process the  entire document. Instead, it directly focuses on the specified zones, making it lightweight, and therefore faster and more accurate for structured documents

Cloud platforms like Google Document AI, Azure Form Recognizer, and AWS Textract have in-built zonal extraction capabilities. They return structured outputs, like text blocks, positions, and even table layouts

Natural Language Processing (NLP)

Once OCR pulls out the text, it still has no idea what that text means. That’s where NLP and Language Models come in.

NLP helps the system read documents the way humans do—understanding structure, identifying key elements, and making sense of relationships between words and phrases. This is important in unstructured documents where the same data might be expressed in different ways.

Some core NLP techniques used in IDP :

• Named Entity Recognition (NER):
  Spots real-world entities like names, dates, companies, or amounts. If a line says “Invoice issued by Acme Corp on Jan 5,” NER tags “Acme Corp” as a company and “Jan 5” as a date.
• Part-of-Speech Tagging:Labels each word by its grammatical role—noun, verb, adjective, etc. That helps disambiguate meaning, like “book a flight” vs. “read a book.”
• Dependency Parsing:Maps how words relate. In “The manager approved the request,” it identifies “manager” as the actor and “request” as the target.
• Semantic Role Labeling:Figures out who did what to whom. In “John sent the contract to Sarah,” it knows John is the sender, Sarah the recipient, and the contract the object.

These tools allow IDP systems to extract accurate information, even when the layout changes or the phrasing varies. Without NLP, documents are just a wall of text.

Machine Learning and Deep Learning  

While OCR and NLP handle the basics of getting the text out and understanding what it says, ML lets the system learn from examples and get better over time

Supervised Learning 

Supervised learning is the most common setup. If you’ve got a dataset of invoices where fields like “invoice number,” “total amount,” or “due date” are already tagged, a model can learn to extract those fields from new, unseen invoices (even if the formatting changes). This is important for consistent data extraction across large, diverse datasets.

‍

Large language models (LLMs) can also be fine-tuned on this kind of labeled data to handle even more variation in language and layout. They generalize better across formats, making the extraction more accurate and robust—especially when handling a single type of document.

Unsupervised Learning 

Unsupervised learning is particularly used in IDP, when labeled data is limited. One common use is clustering, grouping together documents that share similar structure or content. This helps organize large collections, and detect new document types. For example, it can separate purchase orders, invoices, and receipts just by observing layout and language patterns. 

It’s also useful within large, multi-page documents. Different pages like—summaries, annexures, lab reports—and unsupervised models can group these page types without any labels. 

Finally, it’s great for spotting anomalies. If a scanned invoice looks very different from the usual pattern—maybe it’s missing a stamp or uses a strange layout—unsupervised models can flag it as an outlier. 

Here, LLMs can help by bringing a deeper understanding of the content itself. Instead of just clustering documents based on how they look, LLMs consider what the text actually says. This makes the patterns they uncover more meaningful.

Deep Learning 

Deep learning takes things a step further. Models like LayoutLM aren’t just reading the words—they also learn how the position of text on the page affects its meaning. They can tell the difference between a date in a header (like “Report Date: March 2023”) and a date mentioned in a paragraph (“The incident occurred in March 2023”). For scanned images or visual inputs, CNNs help pick up features like handwritten notes, stamps, or signatures.

‍

Layout-aware LLMs like DocLLM combine both language understanding and layout structure, helping the system read documents more like a human would. This combination of visual and textual context leads to more reliable extractions.

‍

Together, these techniques give IDP systems the flexibility to scale and adapt in real-world use.

Robotic Process Automation (RPA)

Once the data has been extracted and understood, it still needs to go somewhere. That’s where RPA comes in. It moves the output from the document processing steps into business systems, like CRMs, ERPs, or case management tools—without human intervention.

For example, if a document has a low-confidence field, RPA can flag it for manual review. If everything looks good, it can route the extracted data to the right database, trigger an approval workflow, or send a notification to the relevant team.

The key here is flow. RPA ensures that data doesn’t just sit there. It moves, acts, and completes tasks as part of a larger process.

‍

How to build an Large-Scale IDP System

‍

Building a scalable Intelligent Document Processing (IDP) system isn't just about plugging in some OCR and calling it a day. At scale, performance becomes non-negotiable. The architecture needs to reflect that. That's why a good IDP setup breaks the process down into clear, loosely coupled , modular stages. Each stage solves one problem and passes clean output to the next. This keeps the entire system easier to maintain, optimize, and scale.

Now, Large Language Models (LLMs) are changing how these stages work. Tasks that once needed separate tools or complex rules can now be handled by a single model. It's more accurate, simpler, has fewer components and easier to build. 

In the sections that follow, we’ll look at each component of the IDP pipeline and how LLMs are transforming every step:

Ingestion

It all starts with ingestion. Documents don’t come in from just one place. Some arrive as email attachments, some get uploaded through internal tools, others are pulled in via APIs or dropped into shared folders by third-party platforms.

The first thing the system does is save each file to a central document store—like Amazon S3, Azure Blob Storage, or Google Cloud Storage. This is where the raw files live. Think of it as the inbox of the system, keeping everything safe and traceable.

Once stored, a message is sent to a queue—using tools like Kafka, AWS SQS, or Azure Service Bus. Every file goes in here, often tagged with metadata: details like where the document came from, what kind it is, when it arrived, or which team it belongs to.

That metadata helps the system decide what to do next. A file tagged as “invoice” might go through a different processing path than one marked “contract.” The goal here is simple: capture the input cleanly and reliably, while keeping track of where it came from and what needs to happen next.

How LLMs improve Ingestion

Ingestion hasn’t changed much—but LLMs still play a role. Once metadata is captured, an LLM can help with intelligent routing. Instead of relying on strict rules to classify documents, a model can quickly read a sample and say, “this looks like a vendor contract” or “this is an expense receipt,” even when filenames or templates vary. That reduces the need for separate classification models and makes routing easier.

Integrations like Anthropic’s Claude with Google Workspace allow AI assistants to directly access and interpret documents from platforms such as Gmail and Google Docs.

Preprocessing 

Once documents are in, preprocessing takes over. This is the stage that gets raw input ready for intelligent extraction. If the document is a scanned image or a PDF, the first job is to clean it up , remove smudges, fix rotation, sharpen text, and improve contrast. This isn’t just for cosmetic reasons; OCR engines perform far better on clear, well-aligned input.

But preprocessing isn't limited to cleaning pixels. It also prepares the document logically. For example, large PDFs might need to be split into individual pages or grouped by sections before further analysis. Language detection kicks in here too—using tools like FastText or langdetect—to route documents through the right language-specific models in the later steps.

This step bridges the gap between raw input and structured understanding. It makes sure each document is in the best possible shape before any data is pulled from it. In high-volume systems, small improvements here can have a big impact on accuracy and processing speed down the line.

How LLMs improve Preprocessing

LLMs make preprocessing smarter. They don’t just clean up documents—they can also spot missing sections, blank signatures, or corrupted pages. But the best use case here is handling multiple languages. In older setups, you’d need extra tools to detect the language and route documents to the right models. 

With LLMs, that’s built-in. The same model can read, understand, and adjust on the fly. Open models like BLOOM and Falcon can handle this, but Claude Sonnet 3.5 and GPT-4o are leading the way for complex, real-world documents.

Extraction

Extraction is the stage where the system starts pulling out the actual information you care about. The document might be a scanned invoice, a contract, or an application form—but the goal is always the same: turn that jumble of text into structured data like names, dates, totals, or clauses.

To do this, the system might use simple rules (like "look for the word ‘Total’ and grab the number next to it") or more advanced models that have been trained to recognize patterns across many documents. If the layout matters—like in tables or forms—models like LayoutLM or Donut help by understanding both what the text says and where it appears on the page.

Depending on the use case, this step might involve things like finding key-value pairs (e.g., “Name: John Doe”), spotting tables, or identifying specific sections in a legal contract.

In short: this is where the system reads the document and picks out the pieces that matter.

How LLMs improve Extraction

This is where large language models really shine. Instead of building a new model for every document type, you can just prompt the LLM with what you need:
“Extract the invoice date, total amount, and vendor name.”
It figures out the rest, even when the wording changes or the fields are scattered across the page.

When layout really matters—like in tables or structured forms—layout-aware techniques help the model understand not just what the text says, but where it appears. Layout-aware models like Donut or LayoutLM are still useful, especially when field position is important. But for most cases, LLMs are now good enough to handle all kinds of formats with minimal setup.

‍

Integration 

Once the data is extracted and checked, it needs to go somewhere useful. That could be a database, a CRM like Salesforce, an ERP system, or even a shared storage bucket in JSON or CSV format. The goal is to plug the data into systems where it can actually be used, whether that’s updating records, kicking off approvals, or feeding into dashboards.

To keep everything running smoothly, teams often use tools like message queues (Kafka, SQS) or orchestration platforms (like Airflow or Step Functions) to track what happens next and make sure nothing gets lost. This part ties the whole pipeline back to the business—turning documents into action.

What really makes this kind of architecture work is Modularity. Each step takes in something clear, does its work, and passes along a well-defined output. That makes it easy to improve things over time, making it scalable and maintainable. 

How LLMs improve Integration

Large Language Models are starting to reshape how RPA works. Where traditional RPA relies on hand-coded scripts and rule-based flows, LLMs handle logic based on context. You don’t need to define every possible condition. You just describe what needs to happen, and the model figures it out.

This blurs the old boundary between extraction and RPA. In many setups, there’s no need for a separate RPA layer. The same model that reads the document can decide where the data goes, what task to trigger, or who to notify.

With LLMs, teams can define high-level prompts—“when this form comes in, validate totals, route for approval, notify finance”—and the model handles the rest. It’s simpler, more flexible, and has fewer moving parts.

Does your Business Need IDP ? 

Not every business needs Intelligent Document Processing. But if you're dealing with hundreds of unstructured documents every month, it's worth exploring.

The first red flag is Manual effort. Are teams copying values from PDFs into spreadsheets? Sorting files by hand? These tasks don’t scale when volume increases and often lead to mistakes.

The second is Variability. Invoices that look different each time. Handwritten forms. Scanned documents from mobile phones. If the process breaks whenever a layout changes, that's a sign rules-based systems aren’t enough.

The third is Growth. What works for 100 documents a week won’t work at 10x. Without automation, scaling means hiring more people. IDP absorbs the volume without increasing headcount, making it a more economical solution.

Compliance is another challenge. If documents are scattered across emails and shared drives, it's tough to trace where a value came from or prove who approved what. IDP systems solve this by making everything traceable—so you always know where the data came from, and who handled it

But not every setup needs IDP. If your documents are already digital, follow a consistent format, and come in low volumes, simpler tools or manual handling might be enough.

Some major industries where IDP is used prominently are :

Healthcare

Healthcare depends on paperwork, and mistakes can delay care. Intelligent Document Processing reduces that risk by making document handling faster and more accurate.

Billing documents like claim forms and EOBs arrive in different formats. IDP reads them, pulls out codes, patient info, and insurance details, and sends it straight to billing systems—reducing errors and speeding up payments.

During onboarding, patients submit forms, ID scans, or handwritten insurance cards. IDP extracts the data without needing manual entry, so staff can move faster and focus on care.

For EHR updates, IDP processes clinical notes, lab reports, and referrals, converting them into structured data. This keeps patient records current without extra work.

Insurance approvals often depend on long forms and dense policy documents. IDP extracts diagnoses, referral info, and coverage details, cutting down on delays and back-and-forth communication.

Each step moves faster, with fewer errors and less manual work. In a system where every minute counts, IDP turns scattered documents into clear, usable data.

Legal Case Management

Law firms handle large volumes of documents where accuracy is critical and manual review can’t keep up. Intelligent Document Processing helps manage that load without slowing things down.

In contract review, IDP pulls out key clauses—like payment terms, renewal dates, and liability limits—no matter the contract format. This speeds up reviews and reduces the risk of missing important details.

During litigation, case files can run into thousands of pages. IDP classifies documents automatically and tags names, dates, and entities, making it easy to search and organize case materials.

For compliance, IDP scans regulatory filings and internal documents to flag missing or outdated language. Legal teams can catch issues early and avoid costly oversights.

Onboarding new clients also gets easier. Scanned IDs, intake forms, and engagement letters are digitized and structured into matter management systems—cutting down on admin work. 

Want to build your IDP Pipeline? 

Looking to build your own IDP pipeline? We’ve helped teams across industries process complex documents at scale. With proven workflows and production-ready setups, we can help you design and deploy a solution that fits your use case.

Book a call, let’s streamline your workflow.

‍