What is Intelligent Document Processing & How It Works?  

Tired of spending time organizing your documents and pulling out the important parts? Try an intelligent document processing (IDP) tool that automatically executes these repetitive tasks for you using AI. 

For extra icing on the cake, IDP processing platforms can also analyze your content and meaning, allowing you to systemize, structure, and keep your important information searchable. 

Read on to learn more about:  

• What intelligent document processing is
• Key steps into how these platforms operate to bolster productivity 
• Reasons to adopt a platform that allows for document processing with AI
• A quick guide to implementing intelligence data processing platforms in your business
• Some leading AI documentation companies
• Why you should pick Lindy.ai as your data processing platform

Let’s now define intelligent document processing and how it can help. 

What is intelligent document processing?

IDP (intelligent document processing) capitalizes on machine learning (ML) and an AI platform’s ability to detect patterns. Then, the optical character recognition (OCR) lets an AI read documents and pictures to extract, interpret, and process data from digital and physical documents.

Document processing platforms can read structured and unstructured data from PDFs, e-books, scanned documents, images, and even handwritten notes. Here are just a few examples of how these platforms can amplify your day-to-day productivity:

• You can prompt your system to extract specific line items from an invoice, such as the date, amount paid, and customer name, and organize these in a spreadsheet or send them to your CRM via an integration. 
• Summarize the key points from a PDF, such as an industry analysis, annual report, or other lengthy documents. 
• Collect work experience, skills, and other information from resumes. 
• Automatically classify complex documents like insurance claims based on client name, policy number, damage assessment, or any other factor you prefer. 

The bottom line about document processing with AI: By automating tedious tasks that you used to do manually, these platforms reduce errors, accelerate workflows, and improve access to valuable business insights — pulling out the information you’ve told it to.

Key technologies powering IDP

Let’s take a look at some of the technology making intelligent document processing solutions possible. Here are some of the main functionalities:

• Optical Character Recognition (OCR): OCR is foundational in IDP systems, enabling converting different types of documents into machine-readable text. This technology extracts text from images and scanned documents, serving as the first step in digitizing paper-based information.

• Natural Language Processing (NLP): NLP allows IDP systems to understand and interpret the text within documents in a way that mimics human reading comprehension. Essentially, it’s how the IDP technology reasons and assesses specific documents. This capability is crucial for extracting the exact information people instructed the AI to extract and understanding context in large volumes of text.

• Machine Learning (ML) & AI Models: ML and AI models leverage statistics algorithms to improve document processing over time. These technologies help classify documents, understand nuanced patterns, and make intelligent decisions based on analyzed content.

• Robotic Process Automation (RPA): RPA integrates with IDP to automate routine tasks such as data entry and document routing. This combination reduces the need for human intervention, accelerating productivity and reducing errors in document-heavy processes.

• Generative AI (LLMs & GPT-based models): Generative AI, particularly models like LLMs and those based on GPT, contribute to IDP by generating summaries, filling in missing information, and even drafting responses based on the document content. This enables more complex document interactions and automations.

How IDP works in 6 steps

Using technologies like natural language processing (NLP), computer vision, and machine learning (ML), IDP systems analyze and extract key details that you require from documents, regardless of format.  

You instruct these systems on classifying, categorizing, and validating extracted data. Let’s take an under-the-hood look at how IDP transforms unstructured data and extracts it to a layout that’s actionable for you:

Step 1: Document ingestion

This is where you “feed” varying documents, such as scanned images, files, or just good-ole copy-and-paste text, into your system. This stage sets the groundwork for further processing and analysis, ensuring all documents are accounted for and ready to be transformed into a structured format.

Step 2: Pre-processing

In this step, raw documents are cleaned up for optimal data extraction. First, you’ll prompt the system to extract key elements like numbers, names, key points, or anything else you need.

To meet your needs, the system will implement techniques like binarization, noise reduction, de-skewing, and de-speckling, which refine document images before AI algorithms analyze them. This helps ensure that text and data are captured without distortion or errors.

Text appearance is also amplified, helping OCR distinguish characters. Noise reduction and de-speckling remove grainy artifacts and stray marks, while de-skewing aligns misaligned text to prevent misinterpretation. 

For example, in automated claims processing, an insurance company can configure pre-processing to extract policy numbers only. This eliminates manual review bottlenecks and speeds up approval times. 

Step 3: Data scanning and classification

With your prompt programmed in its “mind,” and all your document’s data tidied up, artificial intelligence document processing undergoes document classification. This stage ensures that document information is sorted, categorized, and labeled based on content and structure. 

ML, NLP, and computer vision let IDP platforms distinguish between invoices, contracts, resumes, emails, or tax forms without manual intervention.

Classification starts with text and layout analysis. The AI identifies key patterns, keywords, and structural elements, scanning for and categorizing the prompted info. For instance, in invoice extraction, your system will read and classify vendor details, line items, totals, etc. 

Step 4: Data extraction and transfer 

With the data clean, classified, and “understood” by the AI, it’s now extraction time — where the AI, again using NLP and OCR, pulls your requested information from the document and sends it to its final destination. 

During this stage, OCR converts characters into text, while NLP interprets the content, allowing the system to understand and process the extracted information as accurately as possible.

Step 5: Classification and validation

Once data is extracted, ML algorithms take over to classify and validate the information. This step involves sorting and categorizing the data based on predefined criteria and patterns, which aids in understanding the document's context and ensuring the reliability of the extracted data.

Step 6: Data integration into business systems

Your AI documentation platform will route relevant information to the destination you originally prompted it to go. These destinations include databases, CRMs, automation workflows, or other software. Data extraction ensures information flows seamlessly from documents into your software.

Advantages of implementing intelligent document processing

Let’s now look at all the pros that an intelligent document management platform can bring to your business. Most businesses prefer document parsing with AI because it brings the following benefits:

• Accuracy:​ Intelligent documentation processing significantly improves accuracy by eliminating human errors resulting from manual processes, like mistyped numbers, misplaced decimal points, or incorrect classifications. Powered by OCR, NLP, and machine learning algorithms, these systems help ensure that extracted information is precise, structured, and validated before being sent to business software, databases, and other places.

• Productivity: AI-powered document processing significantly boosts productivity by automating repetitive tasks like data entry, classification, and extraction. Instead of manually sorting and inputting information from invoices, contracts, and reports, AI can process thousands of documents in minutes, freeing up your team for higher-value work while eliminating pesky bottlenecks.

• Cost savings: By eliminating the need for human labor-intensive manual data entry and document handling, intelligent document processing platforms reduce operational costs by automating labor-intensive tasks. This lets you reallocate human resources to higher-value work. Additionally, AI minimizes error-related costs, such as incorrect invoice payments, compliance fines, or lost revenue from misfiled contracts.

• Scalability: Unlike traditional methods that require hiring more staff to manage higher demand, document processing with AI can handle 1,000s, of documents simultaneously. 

• Compliance: Manual document handling often leads to misfiled, incomplete, or outdated records, increasing the risk of compliance violations. Intelligent document processing platforms can reduce legal and financial risks by automating data validation, secure storage, and policy enforcement.

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The 7 best intelligent document processing tools in 2025

Looking for an intelligent document processing platform? Look no further than our list of 7: 

1. Lindy: Lindy is a no-code automation tool that lets you build agents for document processing alongside other workflows. For instance, create an agent (or a Lindy) to watch your email for invoices. When it sees one, it can read it, extract the information, and update your CRM and accounts payable. It’s designed for businesses of all sizes. Pricing starts at $49.99/month. 

2. Nanonets: Nanonets is an AI document automation and data extraction platform that handles documents like invoices, receipts, contracts, bills of lading, and tables all at scale. Contact Nanonets directly for custom pricing.  

3. UiPath Document Understanding: UiPath allows you to process various document types, including structured, semi-structured, and even messy, unstructured formats, by utilizing advanced extraction technology. Get in touch with their sales team to activate your 60-day free trial.

4. OpenText Intelligent Capture: This system utilizes Optical Character Recognition (OCR) and machine learning to automate data extraction from various document types, such as paper documents, email attachments, and PDFs. It’s mainly geared toward large organizations, especially those using Salesforce and SAP, as it easily integrates with those systems. Contact OpenText for pricing info.

5. Azure AI Document Intelligence: Azure AI Document Intelligence is designed to help enterprises automate the extraction of text, key-value pairs, tables, and structures from various documents. It fits like a glove with the Microsoft ecosystem, so if you’re a Microsoft user, put this on your shortlist. 
   
   Pricing depends on the number of documents you run through the system, and the type of IDP you employ — either using Microsoft’s prebuilt models or your own customized intelligent document processing automation. 

6. Amazon Textract: Amazon Textract is an advanced machine learning service that accurately identifies and processes structured data, enabling you to extract insights from complex forms, invoices, and tables without manual configuration or updates. Textract is ideal for large finance, healthcare, and the public sector businesses that handle large volumes of document-based data. 
   
   After the free version, pricing is complex and depends on the number of documents fed into the system, your business’s location, use of APIs, and others. 

7. ABBYY: ABBYY is an enterprise IDP solution that features pre-trained models and low-code customization options. It supports over 200 languages and integrates seamlessly into workflows via APIs or connectors. It caters to industries like finance, healthcare, logistics, and legal that require high accuracy and speed in handling large volumes of documents. 
   
   The company doesn’t publish any pricing details, so you’ll need to contact them directly for more info. 

How to choose the right intelligent document processing solution

We recommend you try out a few demos and free versions before handing over your cash. But first, you should always identify the documents you’ll be working with, which items you need extracted from them, and which platforms they need to end up in. This way, you’ll have a better idea of what you need. 

While tinkering with your free version or trial, consider its ease of use and learning curve. This can help you determine how difficult implementation will be. It goes without saying, but ensure the folks on your team who will be using the IDP get to use the free trial as well. 

Keep in mind that while data extraction will save you time, your extractions will still need some human oversight. Always double-check invoice figures and extracted text. While this still takes some time, it’s much less time-consuming than extracting text the old manual way.  

Challenges and considerations in IDP implementation

Before choosing the system that you like best, keep these points in mind: 

• Data privacy and security concerns: You’ll probably be feeding sensitive documents to your IDP system, so you’ll need to ensure that the system you pick implements robust security measures. And, if you’re in healthcare and handle sensitive patient data, using a HIPAA-compliant system is an absolute must. 

• Potential for AI inaccuracies or bias: Just like humans, IDP can sometimes produce inaccuracies or display bias stemming from the data on which they were trained. It's crucial to continuously monitor and refine these models to ensure they deliver accurate outcomes — so, always give your extracted models a once-over. 

• Costs and ROI evaluation: The initial investment in IDP technology can be substantial, making cost and ROI evaluation a vital consideration for businesses. Assess the long-term savings and productivity gained from automating document processing against the upfront and ongoing costs and your current data-extraction methods. 

Frequently asked questions

How does intelligent document processing differ from OCR alone?

IDP extends OCR (optical character recognition) by converting images into text and using technologies like Natural Language Processing (NLP) and Machine Learning (ML), which OCR lacks. This enables IDP to extract, interpret, and categorize data from complex documents, providing actionable insights rather than merely digitized text.

How quickly can businesses expect ROI from IDP solutions?

Depending on the volume of document processing and the efficiency gains from automation, you can often see ROI from IDP solutions within months. This is because IDP reduces manual data entry and error rates, significantly cutting costs and improving operational speed.

Can IDP handle handwritten documents effectively?

IDP systems can effectively handle handwritten documents using advanced OCR and machine learning capabilities tailored for handwriting recognition. However, the accuracy of extraction from handwritten texts can vary based on the clarity of the handwriting and the sophistication of the IDP system used.

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Try Lindy: Your new intelligent document processing tool — and way more

Lindy has loads of features that make intelligent document processing a breeze. And the cool thing is, most of these IDP agents are already made for you — just plug in your documents, clarify your commands, and you’re off to the races. Check out these helpful templates for rapid IDP:

• Attachment handler: Build a Lindy to monitor your inbox for emails with attachments. When you receive one, Lindy summarizes the attachment content for easy reference and understanding and categorizes each document for tidy organization. 

• Chat with PDF: This Lindy extracts summaries, answers, or any specific insights from a PDF, sparing the time spent typically trudging through those lengthy PDFs.  

• Knowledge retrieval: Lindy retrieves specific information from uploaded documents like handbooks, spreadsheets, Word Documents, and more​. Just give Lindy a document, ask a question, or command Lindy to fetch pinpointed information. Kiss those days spent squinting for specific information in long documents goodbye. 

• Case study drafter: Feed Lindy a text version of a recorded interview or conversation, and prompt Lindy to extract key points. This could work for writing a compelling case study that you can feature on your website, CV, or other direct marketing materials. 

Ready to process documents the easy way? Try Lindy for free and create your own AI document processing system.