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What is Intelligent Document Recognition?

Miranda Hartley
June 14, 2024

Introducing Intelligent Document Recognition

Intelligent document recognition is a component of intelligent document processing (IDP) technology. Before extracting and processing data from documents, algorithms must correctly classify them, e.g., as invoices, balance sheets, bank statements, etc.

The deployment of AI algorithms is what makes document recognition ‘intelligent’. Rather than simply matching documents to preconfigured templates, intelligent document recognition can contextualise the nuances in each document. Let’s explore this further.

How does Intelligent Document Recognition Work?

Let’s say you’ve got a receipt and an invoice – two structurally similar yet functionally disparate documents. A human could discriminate between the two in a second, but a poorly calibrated document recognition technology likely would not.

However, if you train AI with a million receipts and invoices, the algorithms will gain a perceptive understanding of the minor differences that distinguish invoices and receipts. Therefore, intelligent document recognition technology will work faster than a human manually completing document classification.

Of course, just as no human can complete data recognition with 100% accuracy, no technology will either. Even the best-trained AI may make a mistake once per ten thousand documents. Exception handling is, therefore, what defines the best-packaged document recognition technology.

The unexpected shouldn't mean hours of troubleshooting either. Intelligent technology can anticipate and correct problems on its own. An effective document recognition solution will flag an error for a human to validate manually. From there, the AI won’t repeat the same mistake.

What are the Benefits of Intelligent Document Recognition Technology?

When combined into a high-performance IDP model, document recognition can offer a host of benefits.

1. Better Accuracy Compared to Manual Data Entry and OCR Technology

Various studies have repeatedly shown that manual data entry yields errors at a rate of 1-3%. Arguably, manual data entry is more accurate than traditional OCR, which tends to generate mass errors and require constant reconfiguration. 

  • Read more about how OCR works here.

For many use cases, such as Know Your Customer (KYC), litigation, etc., accuracy is paramount, even more so than speed or extra functionalities. In other words, clients do not need fast output or extra metrics added – they just want accurate data.

AI excels at data accuracy because its algorithms can automatically validate and clean large datasets, significantly reducing errors. Overall, document recognition delivers far better-quality data than its more traditional alternatives.

2. Reduced Costs

Whether packaged as a monthly subscription or a usage-based model, intelligent document recognition can save operational costs. Paying workers to label and extract data from documents is expensive. Some of our clients have paid analysts £50p/h for document classification and capture — an easily automatable job that drains thousands from the company's bottom line each year.

Correcting errors (initially) will almost always be drastically cheaper than letting them travel downstream. Even errors that occur directly in the dataset can be costly. Case in point – a study by EY showed that each data point from simple HR forms was worth over four U.S. dollars. So, even allowing for a low error rate (~1%), a human data entry clerk could commit thousands of dollars worth of mistakes per year – a liability for any business.

3. Improved Productivity

Another way intelligent document recognition can save costs is by improving productivity. Without the constraints of simple yet essential back-office processes, employees have more time to analyse data, collaborate and devise strategies.

Studies by McKinsey, Forrester and others indicate that automation can increase productivity by around 25-30%, but we’ve found the results to be much more substantial. For example, by using document recognition as part of our IDP solution, DF Capital could expand its team and redirect resources previously devoted to manual data extraction to more value-added activities. The reason? Manually classifying and capturing data from documents is both tedious and time-consuming.

3 Examples of Intelligent Document Recognition Applications

The multiple benefits of intelligent document recognition make it applicable to multiple industries. Here are a few typical use cases:

1. Financial Services (Generally)

Accounts Payable (AP) departments process data from large volumes of vendor invoices. Document recognition can validate the invoice’s document type before capturing the relevant information (e.g., the invoice date, vendor details, line items, etc.).

For example, when our client Enterprise RPA receives emails, our document recognition technology automatically classifies the attached documents, which enables them to be sent to the right repository for manual review. Read the full case study here.

2. Annual Reports

Expanding on document recognition’s role in financial services, another valuable use case is identifying financial statements within annual reports. In seconds, AI algorithms can classify the balance sheet, income statement and cash flow statement from a 200+ page annual report. A tedious job for an analyst - with a quick and easy technological fix.

3. Manufacturing

Supply chain automation can extract data from documents like purchase orders, invoices and bills of lading – enabling regulatory checks and routing to the relevant department for further processing.

Managing the Challenges of Intelligent Document Recognition Software

Like any technology, intelligent document recognition can cause issues in certain contexts. Let’s explore three of them.

1. Establishing the Correct IT Architecture

 You’ll need a scalable and robust IT architecture and secure API integration to implement advanced document recognition technology.

2. Training the Document Recognition Model

If you have a custom document type, you’ll need to supply training examples to the vendor. For instance, if you wanted to extract data from real estate occupancy certificates, you would need some on hand – the more, the better.

As the vendor trains their AI, the quality of the technology’s output will begin to improve. In our experience, it can start around 80% before increasing to 99.99+%.

3. Culture shock

Never underestimate the power of AI scaremongering. Employees may feel shaken and vulnerable when AI suddenly takes over a core aspect of their job. Therefore, you must manage the software’s deployment carefully. For example, you might:

  • Encourage employees to try the demo version.
  • Offer training and support throughout the process.
  • Be open to feedback.


IDP, which includes document recognition, offers a powerful solution for streamlining time-consuming document handling. By automating data extraction and classification, IDP can unlock a range of benefits for businesses (with careful implementation).

Evolution AI offers a multiple award-winning data extraction solution that classifies and captures data from financial documents. 

 Our latest product, Financial Statements AI, classifies financial statements (balance sheets and income statements) from annual and quarterly reports and extracts and computes key metrics.

If you want to learn more, please book a demo or email

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