2. Improve data accuracy

You don’t have to worry about the type of document or the format it uses. IDP platforms adapt to document variations and extract data with higher precision. So, there’s a lower likelihood of errors and you can further improve accuracy through additional validation measures.

Let’s say your team receives bills of lading (BOLs) from multiple carriers. A small typo in a cargo weight or container number could cause issues at customs or during shipping. But IDP scans each BOL, extracts critical data, and flags inconsistencies before they become a problem.

3. Dial down operational costs 

IDP can help you save money by removing excessive manual work and improving data quality. Ryan Carter, founder and CEO of NetSharkx says he has seen enterprise companies reduce operational costs by 30%. 

4. Speeds up compliance and audits 

If you’re in a highly regulated industry or need to comply with certain industry regulations, your documents need to be up to date. An IDP platform can ensure that all required documents are captured, validated, and stored properly, reducing compliance risks.

Let’s say your team is handling tax reports for year-end reporting. Your IDP tool scans these documents for missing information, validates it against other relevant documents, and verifies if everything’s in order. Next thing you know, you’re audit ready.

5. Scales effortlessly with demand

Whether you’re a small business or a large enterprise, document processing needs may increase as your company grows. 

A manual process or basic OCR system worked when you had 100 invoices a month. But when it’s 1000, 10,000 or 100,000? It won’t be sustainable because you’ll have to hire more staff. That’s not the case if you use an IDP solution. The platform simply processes documents as they come—round the clock. 

6. Accelerates decision-making 

When you process documents in real time, your team gets access to critical data instantly. You don’t have to wait for manual data entry or search endless PDFs. Faster data means faster decisions and a more agile business.

Imagine you’re a finance manager and a supplier’s invoice lands on your desk. Before you can approve it, you need to check if:

• The invoice matches the purchase order
• The goods were received and verified
• The correct payment terms were applied

This means you’re chasing down emails, cross-checking spreadsheets, and waiting for different departments to confirm details. This could take days—or even weeks. IDP does that in minutes and you can get on with the rest of your day.

7. Enhances document storage and security

Manual document handling comes with risks. You can lose paper files, delete emails, or accidentally leak sensitive data. But the best IDP platforms come with built-in document storage. You can digitize, encrypt, and securely store documents in one place.

Say you’re in charge of tax compliance of your company. With IDP, you can categorize,  encrypt, and store all tax documents in the cloud. It’s all securely organized and easily accessible.

8. Integration with existing business systems

IDP tools offer integration capabilities that work with the tools you already use. You won’t have to context switch between different tools because the IDP platform integrates with them or offers the option to directly import/export data 

If your company uses SAP ERP to manage supplier payments. Your finance team manually enters invoice details into the system which eats into their day all the time. If you build a workflow which includes a step to push the data into SAP directly, your team just got hours back in their day.

What are the use cases for IDP? 

Here are a few examples of document types you can process with IDP platforms:

1. Invoice processing

Invoice processing is a pain. A small error costs you your margins and messes with vendor relationships. Here’s how you can use Docxster’s IDP to extract data from them:

• Upload your invoices or pull them from relevant sources
• Capture key details like invoice number, supplier details, and payment amounts from scanned or digital invoices
• Cross-checks invoice details with purchase orders (POs) and goods received notes to detect discrepancies
• Use HITL automation to route documents to relevant stakeholders and review discrepancies
• Push the validated data into your tool of choice (using integrations or APIs)

2. Bills of Lading (BOL) processing

If you’re handling freight, warehousing, or shipping, you’re probably dealing with BOLs. Different carriers use different formats so it’s hard to standardize the format.

Here’s how you process BOLs using Docxster’s IDP:

• Upload your BOLs or pull them from relevant sources 
• Use AI to detect and extract critical shipment details
• Flag missing or incomplete BOLs for quick human review
• Push the validated data into your tool of choice (using integrations or APIs)

3. Customs documents processing

Customs paperwork is one of the biggest bottlenecks in global shipping. You can deal with issues like a missing tariff code, incorrect HS classification, or an unchecked declaration box that holds up shipments.

Here’s how you can process them using Docxster’s IDP:

• Upload your customs documents or pull them from relevant sources 
• Extract critical details like consignee details, product descriptions, duty rates, and shipping values 
• Cross-check tariff codes and shipment values to flag compliance risks 
• Set up a workflow step to submit documents automatically to customs brokers and compliance teams

How does Intelligent Document Processing work? 

IDP automates document handling, turning unstructured data into structured, usable insights—and it uses a ton of background technologies to do so.

Let’s look at how IDP works:

Step 1: Ingest documents without human involvement

The first step in IDP is getting your documents into the system. The IDP tool automatically ingests documents from various sources including: 

• Email
• PDFs
• Cloud storage
• ERP systems
• Scanners

You can either upload it manually or set up a workflow to automatically pull specific documents (either using document title or email ID) to bring it into the system.

Step 2: Preprocess the documents to improve quality 

Once the documents are ingested, the IDP tool performs preprocessing tasks before any text extraction can begin. Why? Because these steps ensure that the text in your documents is clearer and easier to read for the OCR system. 

An IDP solution performing necessary preprocessing steps to enhance data quality 

Source 

Here’s an overview of preprocessing tasks that improves the overall quality of your documents: 

• Noise removal: Ever noticed tiny specks, smudges, or dust marks on scanned receipts or contracts? These might seem harmless, but they can throw off the accuracy of your OCR. Noise removal techniques including Gaussian blur and median filtering clean up these distractions so the text stands out clearly.

Here’s an example of how denoising process removes distractions from documents: 

Converting a dirty image into clean image using denoising technique 

Source 

• Binarization: Converts images to black-and-white and simplifies text recognition by improving contrast between characters and the background. Techniques like Otsu’s thresholding or adaptive thresholding are used in this step to differentiate text from non-text regions, reducing OCR errors.
• Contrast & brightness adjustment: If your document is too dark, too bright, or just looks faded, OCR might struggle to pick up the text properly.  This technique adjusts the contrast and brightness using adaptive contrast enhancement and makes your text crisp and readable.

Here’s an example of before and after of brightness adjustments in a document: 

Adjusting the contrast and density of the document to improve text clarity

Source 

• Dewarping & deskewing: If you’ve ever scanned an invoice and ended up with curved or slanted text, you know how frustrating it can be. Deskewing straightens tilted documents, while dewarping flattens out those curved lines so the text stays in its proper place.

Here’s how deskewing process aligns the text in a receipt: 

Before and after images of a receipt being deskewed for better readability

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• Edge & border cropping: Typically, you don’t need extra white spaces or black edges in your documents. That’s why automated cropping techniques trim away the unnecessary parts so your document looks clean and focused.

An invoice cropped focusing only on the key details section

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Step 3: Convert documents into machine-readable text

After preprocessing, OCR first starts with identifying the regions in the document that contain text. Here's how OCR goes about the whole process: 

• Layout analysis: OCR first analyzes the layout of the document. The technology identifies each component such as headings, paragraphs, columns and tables. If you’re processing invoices, the OCR locates the heading “X Invoice”, then individual lines such as dates, account number, and more 
• Segmentation: OCR then identifies the boundaries of the text regions and separates them from non-text elements like images or graphics 
• Pattern recognition: OCR uses pattern recognition and feature recognition algorithms to compare each letter or symbol in the document to a database of predefined patterns or templates. 

Instead of relying on whole-character patterns, it breaks characters down into smaller features (example, lines, curves, and intersections). It then matches them to the databases and extracts the characters that are matched. For example, it will match the shape of the character in the image (like A or B) to a stored model.

An OCR engine isolating and matching the characters to consolidate predictions

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Step 4: Extracts relevant data accurately

After OCR processes the text, the next step is to understand the content. It’ll first classify the type of document (invoice, contract, tax form, etc.,) and extract the relevant data fields (e.g., invoice number, amount, date).

This is where natural language processing (NLP) and computer vision technologies come into play. 

Here’s how NLP models understand the context, structure, and semantics of your documents:

1. Tokenization: Tokenization breaks down text into smaller units called tokens (such as words, sentences, or subwords). This process splits continuous text into words or phrases to understand the meaning.

NLP algorithms splitting a sentence into words and subwords using tokenization technique

Source 

For example, if the invoice has a line, “The invoice for shipment is due in March 2025”, then the tokenized text would be “The”, “invoice”, “for”, and “shipment.”

2. Named Entity Recognition (NER): NER identifies and categorizes named entities in text such as person names, organizations, dates, or locations. The model scans through your invoice to identify specific words or phrases that are relevant entities. For example, when you're processing invoices, the potential entities would be:

• Entity 1: DHL (Organization)
• Entity 2: New York (Location)
• Entity 3: March 15, 2025 (Date)

NLP algorithms extracting named entities from an invoice status inquiry email 

Source 

3. Part of Speech Tagging (POS Tagging): POS tagging assigns grammatical tags to each word in a sentence like noun, verb, adjective. This process determines the grammatical role of each token (word) in a sentence. 

A sentence assigned with grammatical tags for each word

Source

If your invoice has a sentence like, “Invoice must be paid before 26”, then it would categorize invoice as Noun, paid as verb and so on.

4. Sentiment analysis: Sentiment analysis determines the emotional tone of a piece of text (positive, negative, or neutral). The model uses words and context to classify the sentiment of the text.

Complex documents don’t arrange the text in a linear fashion. You’d need to extract data from visual elements as well. This is where computer vision helps. Look for and segment visual components like tables, forms, signatures, images, and logos. And then the convolutional neural networks (CNNs) identify these regions and extract line items and other data accordingly.

For example, when you're processing invoices, IDP systems use  NLP and computer vision to interpret context and extract data. It pulls the key value pairs and line items are extracted without relying on predefined templates:

• Name
• Invoice number
• Dates
• Addresses
• Invoice amounts
• GST number 

An invoice with key-value pairs and line items extracted on the right side

Source

Step 5: Validates the extracted data automatically 

Now, the system checks if the extracted information is correct and validates them against predefined business rules. Some IDP tools also cross-verify the information with external sources such as vendor databases to improve accuracy.

As the last step, the IDP tool also assigns a confidence score so that you can prioritize high-risk errors and flag fields that may need human intervention.

If it’s not fully confident about the extraction, the platform triggers a human-in-the-loop (HITL) validation step. You can manually go in and see if the data is correct and approve/edit it.

Step 6: Export data to other business systems 

Once validated, the extracted data is sent to backend systems (like an ERP or CRM) for further action. Or you can export it yourself and upload it into the relevant platform.

For example, if you want to tally data from a shipping manifest to another customs document, you can do that. Just create a workflow to cross-verify data from both these documents and flag any discrepancies.

Where does IDP fall short for today's businesses? 

Even though IDP has come a long way, many platforms don’t live up to its expectations for the following reasons: 

1. Stuck on predefined templates

Many IDP solutions still rely on predefined templates to pull data. And that’s where things can get tricky. Your IDP system might fail to extract data if there are variations in the structure and format, leaving you with a pile of manual work.

Companies like AIScreen deal with a wide variety of document formats—with almost 25% of them that don’t fit standard templates. 

2. Limited flexibility/no-code workflow customization

A lot of intelligent document processing (IDP) platforms still don’t offer no-code workflow builders. This makes it much harder for you if you’re trying to modify workflows without technical expertise or IT team involvement. You could be waiting days (or weeks) to manually adjust simple things. And that’s a serious bottleneck.

That’s one of the reasons we built a platform that uses drag-and-drop interfaces and condition logic. As a result, you can configure workflows on your own and route documents to relevant stakeholders as needed.

3. Heavy reliance on human validation

Even the best AI systems are far from perfect. Many IDP tools still need a human eye to validate the data—especially if the confidence score is low.

In a recent podcast, John Michelsen, founder of Krista Software said that only 30% of invoices are considered straight-through processable invoice orders. And this happens because the documents have watermarks, new layouts or unusual properties that you can’t prepare for. In this case, you need human validation to make sure everything’s tip top.

4. Limited on-premise AI deployment

Many enterprise organizations prefer using on-premise solutions because it’s more secure and easier to comply with industry regulations. The problem here is two-fold. There’s a misconception that cloud-based IDPs are not “fully secure” and on-premise deployment is generally more time consuming and expensive.

As for the former, that’s not true. 

In short: cloud-based IDP is not a drawback but a boon.

How to choose the right IDP solution for your business? 

Some tools focus on data extraction, others on system integration, and only a few provide end-to-end workflow automation.

So, how do you find the best fit for your business?

The key is understanding your document processes, automation goals, and scalability needs. Let’s break it down:

1. Define your document workflow

Document your current state and goals for the document processing project. Start with the following questions:

• How many types of documents are you dealing with?
• Do your documents contain handwritten data or non-text elements?
• Does your team need to validate and export the data manually?
• How much human oversight do you want to enable?
• Do you tend to use too many tools just for document processing?
• Do you need a system to process, validate, and route documents automatically?

Be specific and realistic about the current state of your business—and where you want to be.

2. Assess the solution’s data extraction capabilities

Data extraction is the heart of IDP. If the system struggles with extraction accuracy, everything else falls apart. Choose a solution that does the following:

• Extract structured & unstructured data from different sources without templates
• Improve extraction accuracy with automated validation 
• Handle handwriting, multi-language text, and complex layouts
• Continuously improve accuracy with AI and ML

3. Assess compatibility and integration with your existing systems

Check whether the IDP tool easily connects with your current systems using APIs or built-in connectors. For example, platforms like your ERP, CRM, or shared drives. This way, you don’t have to worry about proper data flow between different tools.

At the very least, it should have powerful exporting capabilities so that you can export structured data and upload it directly into your platform of choice. Check if it exports in common formats like CSV and JSON.

4. Factor in scalability and flexibility

Your IDP solution should be able to handle increasing document volumes without performance issues Some tools work well for small-scale processing but struggle when dealing with: 

• Large datasets
• Multiple document formats
• New document types 
• Complex document workflows

Also, your IDP of choice should be flexible. You should be able to define unique data fields and adapt to industry-specific requirements without heavy IT intervention.

5. Prioritize a no-code, user-friendly interface

A complex, IT-dependent system can lead to adoption challenges. This is especially true if you’re planning on rolling this out to non-technical teams.

If that’s the case, do the following before finalizing an IDP platform:

• Take a demo or try the product for free in a sandbox environment
• Let multiple stakeholders try the product and build workflows for themselves
• Collect feedback from everyone involved (including IT teams)
• Assess if non-technical users can actually get up to speed quickly

Typically, with no-code tools like Docxster, you should be able to ramp up in a day without IT teams or developers.

6. Review security and compliance certifications

Data protection is absolutely paramount—especially for enterprise organizations. Cross-check if the platform has undergone certifications like:

• ISO 27000
• General Data Protection Regulation (GDPR)
• California Consumer Privacy Act (CCPA)
• Health Insurance Portability and Accountability Act (HIPAA)

If your industry has other regulations, ask your vendor if they comply with those too. Also, ask them how they encrypt their data at both rest and transit to ensure complete privacy.

7. Account for built-in document storage and management

Many IDP tools stop at data extraction or data exports. But what happens to the documents you’ve processed? You can’t store them in another drive folder, hoping to find them when you need them.

Prioritize an IDP that offers a document management capability. For example, we offer Docxster Drive that:

• Lets you store documents in dedicated folders
• Share files/folders with relevant stakeholders only
• Search for and within your documents using AI 

What are the potential roadblocks when implementing a new IDP solution?

Adopting new technologies always comes with its own set of risks and concerns. Here are some common roadblocks you might face—and how to overcome them:

Challenge #1: Keeping security and compliance in check

There’s a good chance you’re handling sensitive documents. As a result, you can’t afford to choose a tool that isn’t secured by industry standards—like GDPR and ISO 27000. However, “security” goes beyond that. You also need to make sure that:

• Your workflows can be created by specific stakeholders
• Your documents access is limited to relevant stakeholders
• Your documents are encrypted at all times 
• You can access detailed audit logs when you need it
• You documents are processed/generated without AI hallucinations or data leaks

We recommend constantly checking if you’re running into such issues and working with your vendors to make sure this doesn’t happen.

Challenge #2: Overcoming employee resistance to change 

If your team is used to doing things a certain way, they might fully embrace a new tool at first. Also, Marco Cevoli, managing director at Qabiria says lack of trust in AI is another factor.

Challenge #3: Handling accuracy issues 

If you’ve signed up for the platform but the accuracy rates stay below 80%, you might not experience the true benefits of automation.

Choose an IDP solution that is trained on large datasets and that learns over time and adapts to different document types. At the very least, conduct a trial phase or onboarding phase with your vendor before you sign up for the platform.

Train the AI model using your own documents so that it can learn what to expect in the long run.

Challenge #4: Maximizing user adoption

Irrespective of how big or small your investment is, if employees don’t use the platform, it’s a wasted investment. There are a few reasons why they might find it hard to use:

• The platform is clunky and hard to navigate
• The platform is difficult to integrate with existing workflows
• Your employees don’t understand how to use the platform

Lawrence Guyot, president of ETTE says:

In addition, get feedback from your employees in the pilot phase to see where they’re getting stuck. You can create training programs around that—and make it easier to adopt the platform.

Challenge #5: Tackling extensive customization 

Some IDP solutions require a lot of configuration and IT team involvement to support your needs. It leads to longer setup times and higher costs. To avoid this, use an IDP platform that’s powered by large language models (LLMs) and generative AI (GenAI).

The future of document processing lies in adaptability—not in structure

For far too long, businesses have been stuck forcing documents into rigid templates. They’re constantly trying to squeeze every invoice, contract, and report into a single format.

But that’s not how it usually works. No matter how hard you try, vendors will always send the document in a format that’s specific to their organization. In some cases, they even handwrite it, which only exacerbates the problem.

You need to consider adopting AI-driven tools for document processing that can:

• Adapt to any document format you throw at it
• Break free from rigid templates you’ve created
• Process documents in minutes instead of days
• Let you create flexible document workflows using your tech stack

It’s time to make the switch to a templateless IDP tool—and experience how your document workflows change.