Any organization's productivity and success rely on effective data and document management. Over the years, document processing has changed from manual handling to storing files digitally. And now, it’s come down to using smart tech like Intelligent Document Processing (IDP) to transform unstructured and semi-structured data into usable information.
Why does this evolution matter for businesses? First and foremost, it unlocks quite a few efficiencies and cost savings. By adopting IDP, companies can reduce manual errors, accelerate decision-making, and put their focus on strategic initiatives that drive growth. In a competitive market, IDP turns everyday documents into a valuable asset.
I am Oleh Komenchuk, an ML Department Lead at Uptech. In this article, I want to shed some light on what IDP is and how it works. You will also learn how IDP leverages AI and machine learning to automate data extraction, classification, workflow automation, and much more. Keep reading!
H2: What Is Intelligent Document Processing
Intelligent document processing (IDP) – also known as Cognitive Document Processing, Intelligent Document Recognition, or Intelligent Document Capture – is the technology that automates the extraction, classification, and analysis of data from documents.
It does much more than perform a simple scan of content. In the modern world, IDP uses artificial intelligence and machine learning to read the details within your documents with clarity.
This solution can handle different data formats – whether it’s structured, semi-structured, or unstructured. For instance, IDP-powered tools can read scanned forms, PDFs, emails, and handwritten notes and pick out essential details from them.
Many business owners call this tool a real game changer. Here are a few examples of what intelligent document processing technology can do:
- Connect with other systems and workflows to simplify document processing.
- Convert paper documents into digital files for secure storage and easy retrieval.
- Retrieve essential data from documents such as invoices, contracts, forms, and receipts.
- Confirm data accuracy and completeness through systematic checks.
- Identify and fix errors in data or formatting.
- Interpret handwritten or printed text on scanned documents and images.
- Organize documents automatically by type, content, or other defined attributes.
- Enhance compliance and risk management by ensuring data remains secure and confidential.
Let’s take an investment app as an example. These applications manage client forms, invoices, and regulatory reports. With IDP in place, the apps extract key data quickly, check its accuracy, and secure every piece of information. The result appears in improved operational flow, enhanced customer satisfaction, and tighter control over compliance.
Intelligent document processing transforms almost any tedious task into a reliable, error-free, and secure system – a tool every business should consider to keep pace in today’s dynamic market.
Sounds great, right? But you may be wondering how all this magic happens. We’ll explain below.
H2: How Intelligent Document Processing Works
Intelligent document processing comprises different technologies and components that work together to automate and improve document management processes.
Intelligent document processing technologies
In the IDP workflow, there are a few key technologies, such as optical character recognition (OCR), natural language processing (NLP), computer vision, machine learning (ML), and artificial intelligence (AI), to automate and enhance the document management process. For better understanding, we’ll consider the entire IDP ecosystem as a modern smart city where every component has its own role.
Let’s take a closer look at each.
Optical character recognition (OCR)
OCR converts images, scanned documents, and PDFs into machine-readable text by identifying printed or handwritten characters. Advanced OCR systems employ deep learning techniques – often using convolutional neural networks – to accurately detect and segment text from complex layouts and varied fonts.
In our analogy, OCR is the sensor network of a smart city. Basically, this technology lays the groundwork for the rest of the processing pipeline by transforming raw visual data into a digital format that can be further analyzed.
Machine learning and artificial intelligence (ML and AI)
Next comes the heart of the smart city – machine learning and artificial intelligence. They function as the central command center. ML and AI process data from OCR and other sources to identify patterns, make informed decisions, and continuously refine the system’s performance.
Thanks to algorithms such as decision trees and support vector machines, as well as more advanced ones like deep neural networks, these technologies learn from historical data and adapt to new inputs. As such, they make sure that the document processing remains both accurate and dynamic.
Here, IDP mainly relies on the following ML-driven technologies:
- Computer vision – to interpret visual data beyond simple text recognition and detect elements such as images, tables, and layout structures within documents. Techniques like deep convolutional neural networks enable the system to classify and understand the visual components, much like a smart city's cameras that monitor and analyze urban activity to optimize traffic flow and safety.
- Natural Language Processing (NLP) – to decipher the language embedded in the text. It parses and interprets human language to extract context, recognize named entities, and summarize content. With algorithms for sentiment analysis and entity recognition, NLP ensures that the information carries meaning – similar to how a city’s communication center interprets and responds to the voices and needs of its citizens.
Robotic process automation (RPA)
RPA is akin to the automated transit and delivery services in our smart city. It handles routine, repetitive tasks by mimicking human actions to move data between systems, trigger workflows, and execute predefined rules.
RPA bridges the gap between data analysis and actionable outcomes: Once the data is digitized and understood, it flows seamlessly into the appropriate business processes. This automation reduces manual labor and minimizes errors so that the city (or business) runs efficiently.
Cloud computing
Last but not least, there’s cloud computing, which provides scalable computing resources, vast storage capacities, and strong security measures to handle the large volumes of data processed by IDP systems.
For organizations, it brings on-demand access, distributed processing power, and integration capabilities across various platforms. In our analogy, cloud computing is the city’s power grid and communication network that delivers the energy and connectivity required to support every other system in the ecosystem.
Together, these technologies create an integrated, end-to-end solution that can transform unstructured data into actionable insights.
Intelligent document processing workflow steps
Now that we have clarified the topic, let us examine the step-by-step flow behind intelligent document processing.
Collection (Document ingestion)
The workflow starts with document collection from various sources. Businesses obtain files from multiple channels, such as:
- Scanned documents and images
- PDFs, Word, and Excel files
- Emails and electronic forms
- Databases and APIs
The system uses a mix of technologies at this point:
- Secure API connectors pull documents from electronic sources
- File monitoring tools scan designated directories for new files
- High-speed scanners convert paper documents into digital images
- Event-driven architectures with message queues ensure that data moves quickly into the processing pipeline.
This stage accepts all data types – structured, semi-structured, and unstructured – and guarantees that every document format, from handwritten notes and invoices to contracts and receipts, enters the system for further treatment.
Document preprocessing
Before the system extracts useful data, the documents must undergo some preparation to boost clarity and quality. This stage comprises several key tasks:
- Deskew – correction of misaligned images so that text lines appear horizontal.
- Noise reduction – elimination of distortions and irrelevant marks that block text recognition.
- Binarization – converting images into a binary (black and white) format that benefits OCR accuracy.
- Cropping – removing unnecessary elements and margins to emphasize essential content.
- OCR preparation – contrast and resolution adjustment to improve text clarity.
- Language detection and conversion – identification of the document language and adjusting text to support multilingual content.
Each task raises document quality and creates a solid foundation for accurate data capture.
Document classification
After the preparation jobs are done, the system categorizes each file according to its type, content, format, or predefined rules.
Rule-based methods and AI/ML models sort the files. The system may assign multiple labels to one document if it fits more than one category. Examples include invoices, contracts, purchase orders, resumes, medical records, and legal documents.
Data capture (extraction)
Once classification ends, the system enters the critical phase of data extraction – conversion of unstructured text into a structured format that meets business requirements. The data capture stage transforms preprocessed and classified documents into usable digital information. The system now uses OCR to convert images of printed text into digital text. OCR reads shapes that form letters and numbers in scanned documents, producing a machine-readable version of the content.
The system also employs Intelligent Character Recognition (ICR) to handle handwriting. ICR builds on OCR by applying artificial intelligence algorithms that interpret various handwriting styles and yield accurate text. In addition, Optical Mark Recognition (OMR) detects marked elements, such as checkboxes or radio buttons on forms, and transfers that information into a digital format.
The system locates key-value pairs within documents. For example, it recognizes a label like “Invoice Number:” and captures the associated value “12345.” The system applies table extraction techniques to identify rows, columns, and individual cells, thereby converting tabular information into a structured dataset.
To refine the captured data further, the system employs NLP techniques, including Named Entity Recognition (NER), to identify and label important elements such as dates, monetary figures, names, and addresses. Machine learning models standardize data formats, correct common errors, and enforce consistency across entries.
Data validation and verification with/without human-in-the-loop
After extraction, the system checks the data for accuracy and consistency. It applies automated rules to verify proper formatting (such as dates, currencies, and IDs) and logical relationships (for example, an invoice total equals the sum of its line items). The system computes a confidence score for each field; if a score falls below a preset threshold, the system flags the document for review.
A human reviewer then inspects and corrects the data, and the system incorporates these corrections into its operations. This dual approach of automation and human review guarantees high data quality.
Data storage, integration, and export
After validation, the system standardizes the data and transfers it into target systems. The system exports data to platforms such as:
- CRM (Customer Relationship Management) platforms
- EHR (Electronic Health Records) systems
- Business intelligence and analytics tools
- Databases and APIs
This integration step makes sure that refined data flows without any errors into systems where it supports operational decisions and drives business processes.
Continuous learning & AI model improvement
The final stage establishes a feedback loop that helps the system improve over time. Human corrections, exception cases, and new data patterns serve as training inputs for OCR, NLP, and other AI models. The models must be retrained and updated periodically to meet evolving requirements. This iterative process enhances recognition accuracy and automation precision as time goes by.
This detailed view of the IDP workflow shows how each phase contributes to turning raw documents into reliable, actionable data.
H2: Intelligent Document Processing Use Cases for Industries
We’ve seen many applications of IDP in different industries. In this section, you will find a few examples.
Loan applications in finance
Financial institutions face a large volume of documents from loan applicants. IDP can be used here to convert paper and digital forms into structured data with accuracy and speed. Here’s how.
The system extracts applicant names, addresses, credit scores, and income details from scanned files and PDFs. It checks each data point against internal records and external databases.
For example, Impakt Claims, a UK-based claims management company, deployed an intelligent document processing system to verify signatures and applicant details in payday loan letters. For this, they opt for the Robocloud RPA Service alongside their IDP solution. As a result, the company experienced lower operational costs, faster risk assessments, and a notable reduction in errors throughout the approval cycles.
Healthcare records
Medical professionals require precise patient data to make critical decisions. IDP transforms diverse medical documents into unified records. The system extracts patient identifiers, diagnosis codes, medication details, and treatment plans from doctor notes, consent forms, and test results and integrates with Electronic Health Records and other critical medical systems. It handles both printed and handwritten information without error. What’s more, IDP solutions ensure compliance with regulations like HIPAA, GDPR, and PHI standards.
Let’s take Amazon Comprehend Medical, for example. It offers a service that uses machine learning to detect key details and reveal relationships among medical data. Amazon does not publish a comprehensive list of healthcare providers using Amazon Comprehend Medical due to confidentiality and HIPAA-related privacy requirements. However, the service has seen adoption across a wide spectrum of healthcare organizations.
Insurance claims
Insurance companies process a vast number of claim files that appear in many forms. IDP can be used here to convert varied paperwork into organized data that supports fast review. The system can extract policy numbers, claim dates, amounts, and damage details from accident reports, repair estimates, and receipts. It can also classify claims by type and check the data for completeness against policy conditions.
Many IDP platforms now include fraud detection algorithms that analyze historical claims data and detect unusual patterns or anomalies. This capability flags suspicious claims early in the process, and insurers can review potentially fraudulent activity and take appropriate measures before losses escalate.
IDP solutions can also be used for claims validation. Once key data is extracted from claim documents, the system cross-references this information against existing policy records and external databases. This automated validation ensures that details like claim amounts, policy numbers, and supporting documents match the established records.
Retail order management
Retail companies handle a high volume of orders, invoices, and shipping records. IDP can come in handy and turn physical documents into digital data that integrates with inventory and accounting systems.
The system can help extract order numbers, customer details, item descriptions, and invoice totals from paper forms and scanned files. As a retailer, you may want to deploy an IDP solution to reduce data entry errors and accelerate order fulfillment, among other things.
H2: Benefits of Intelligent Document Processing
Organizations that manage large amounts of paperwork stand to gain many rewards with intelligent document processing. Here are a few advantages.
Improved efficiency
IDP works with minimal human involvement. It speeds up document handling and frees employees to focus on higher-value tasks. Faster document conversion results in quicker responses and better customer experience. Companies benefit when staff devote their time to strategic activities rather than repetitive data entry.
Reduced costs
Manual document work demands significant resources and expenses. With IDP in place, you can cut the need for extra staff during busy periods and lower the overhead associated with paperwork. With routine tasks being automated, you save money and scale operations without resorting to temporary hires or additional staffing.
Minimized mistakes
Human error often occurs when employees work under pressure, especially with repetitive tasks. IDP ensures high accuracy in data extraction and classification. Fewer errors lead to smoother workflows and improved customer satisfaction. Reliable, error-free data also prevents delays and reduces the risk of costly corrections.
Increased data security and control
Protecting customer and employee data remains a top priority for any business in any domain. IDP digitizes documents, which allows companies to dispose of physical copies properly and securely. Digital records offer better control over sensitive information and lower the risk of data breaches or legal issues.
Of course, these are just a few benefits that make IDP an appealing solution for businesses that seek smoother operations and stronger growth. There are more interesting and useful things IDP tools can bring to the table.
H2: Off-the-Shelf IDP Solutions vs Custom IDP Software
The next big question is how to choose the right intelligent document processing system. And it depends on your business needs. You can either opt for a ready-to-use, commercially available solution or build a custom IDP system designed for your unique workflows.
When to opt for available intelligent document processing platforms
Off-the-shelf IDP solutions better suit organizations that need quick deployment and proven reliability. These commercially available tools offer robust features, extensive vendor support, and different yet often limited integration options with popular ecosystems.
For example, here are five popular IDP platforms to consider:
- Microsoft Syntex is a robust IDP platform that relies on AI to manage, extract, and classify document content. It integrates with Microsoft 365, so it can be a strong choice for businesses already in the Microsoft ecosystem.
- Azure AI Document Intelligence, a part of the Microsoft Azure suite, is a cloud-based IDP tool that extracts text, key-value pairs, and table data from various document types. It offers high extraction accuracy and scalability for growing data needs.
- Document AI from Google is another comprehensive IDP solution that converts unstructured document data into structured information. It uses advanced machine-learning techniques to analyze and classify a wide range of document formats.
- Amazon Comprehend is an IDP platform that provides advanced natural language processing capabilities. It extracts and analyzes text data within the Amazon Web Services (AWS) ecosystem and provides reliable and secure processing.
- IBM Watson Discovery is also a powerful IDP tool that uncovers insights from large document collections. It can search, analyze, and visualize unstructured data, which is ideal for enterprises with complex information needs.
These off-the-shelf IDP solutions work best when you require a proven, vendor-supported system that can be deployed rapidly with minimal customization.
When to choose custom IDP tools
In some cases, a custom IDP solution is the better option. Custom intelligent document processing software allows you to tailor the system to your specific workflows, data types, and business challenges.
This approach offers several benefits:
- Tailored functionality. You get a solution built to handle the unique documents, formats, and processes of your organization.
- Scalability and flexibility. A custom IDP tool can grow and adapt as your business evolves. Also, it addresses specific needs that off-the-shelf products might not cover.
- Competitive edge. Custom solutions provide features that match your exact requirements, giving you a strategic advantage over competitors who use generic platforms.
So, which way to go? It’s always better to start with a clear focus: pinpoint the document tasks that slow your team down. Work closely with your technical experts to select the ideal IDP solution – whether off-the-shelf or custom-built – that fits your unique workflow. Choose systems that offer natural language processing, advanced analytics, and the flexibility to evolve as your business grows.
H2: How Uptech Can Help with IDP
At Uptech, we specialize in building custom IDP solutions that cater to your precise business needs. We understand that one size does not fit all, and our expert team can design and deploy an IDP tool that gives efficiency, accuracy, and control in your document processing workflows.
One of our most recent cases is a project that focuses on medical document processing for a private diagnostic clinic in the USA. The clinic aimed to automate its document workflows to support a doubling of monthly patient volume and enhance patient care.
The Uptech team helped design a custom AI-powered system that digitized and processed millions of documents. The solution saved many work hours and cut processing time by 34%. Our tailored solution integrated OCR, NLP, and machine learning to streamline data extraction and patient communication. As a result, the clinic managed to boost patient service satisfaction.
So, if off-the-shelf tools fall short of your expectations, contact us to learn how our custom solutions can drive your business forward.
FAQs
What is the difference between OCR and IDP?
OCR is a technology that simply recognizes characters and converts images into machine-readable text. IDP further uses AI, NLP, and machine learning to extract, classify, and validate data from documents.
Is automatic document processing the same as intelligent document processing?
No, these are different technologies. Automatic document processing typically uses rule-based methods to handle documents. Intelligent document processing adds AI and machine learning to understand context and extract actionable insights.
What is the difference between RPA and IDP?
RPA (Robotic Process Automation) automates repetitive, rule-based tasks across systems. IDP transforms unstructured document content into structured data using technologies like OCR, NLP, and ML and then adds RPA to the workflow.
What is the difference between IDP and NLP?
IDP is a comprehensive solution that extracts, processes, and integrates data from documents using various technologies. NLP (Natural Language Processing) is a subset of AI that focuses specifically on understanding and interpreting human language. NLP is used with IDP software.