Intelligent document processing (IDP) uses advanced technologies, including Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), to automate the extraction and processing of data from analog and unstructured documents.
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The ability to extract data from analog documents, and analyze and process it is essential for reaping the benefits of digital transformation.
Intelligent document processing solutions use a suite of technologies for fast, accurate, and efficient extraction and processing of information.
According to a report by Fortune Business Insights, the intelligent document processing market is expected to grow from 2.30 billion in 2024 to 19.32 billion by 2032. The Finance and Accounting sector leads the market with a share of 45%, followed by Supply Chain and Procurement, HR, Legal, and Marketing.
Key drivers for IDP market growth include a need for processing increasing data volumes, workflow automation, and big data analytics.
This article will take a deep dive into intelligent document processing, including the technologies used, benefits, applications, and a framework to help you choose the best solution.
Intelligent document processing (IDP) uses advanced technologies, including Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), to automate the extraction and processing of data from analog and unstructured documents.
It is the modern counterpart of traditional document processing, which relies on manual input, is time-consuming and is prone to human error.
IDP speeds up the process, minimizes errors, improves overall efficiency, and streamlines organizations’ digital transformation efforts.
The IDP technology stack leverages several core technologies to streamline document processing.
Optical Character Recognition (OCR) technology can recognize letters, numbers, and symbols in scanned documents and images, and convert them into machine-readable text that can be read and edited.
It is widely used in the finance, healthcare, and legal industries for digitizing printed documents, data entry automation, and document management.
OCR is the foundatation of IDP. Even though it is a well-established and mature technology, it does have its limitations, such as processing low-resolution images, blurry text, or documents with heavy background noise. IDP processes overcome these OCR limitations via AI and ML to improve accuracy.
The synergy of Machine Learning (ML) and Artificial Intelligence (AI) supercharges IDP’s processes for extraction and analysis of data from unstructured documents.
ML models use training data to identify patterns, make inferences, and improve performance over time. Multiple learning modes, including supervised and unsupervised learning, are used to improve the accuracy of document classification and extracted data.
AI is an overarching term for computer systems capable of performing tasks traditionally performed by humans, such as reasoning, learning, problem-solving, perception, and decision-making.
In the context of IDP, AI performs several functions, including improving the accuracy of OCR text, document classification, and identification of patterns and correlations in data, to improve the overall efficiency and scalability of data handling processes.
Natural Language Processing (NLP) technology, a subset of ML and AI, uses algorithms and models that allow machines and computers to understand and interpret human language.
Search engines like Google and Bing use NLP models to understand and rank website content. Chatbots and virtual assistants like Alexa and Siri use NLP to understand and respond to user queries.
IDP systems use NLP models to extract keywords, understand the context in which words or phrases are used, and detect sentiment and emotions for deeper data insights.
Robotic Process Automation (RPA) technology uses software to automate repetitive, rule-based tasks, such as data entry and transaction processing.
IDP systems use RPA to improve efficiency by
Intelligent document processing can handle structured data and unstructured data that doesn’t follow a specific format or structure, such as printed documents, scanned images, photographs, handwritten forms, business reports, and legal documents.
Here is an overview of the process.
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The first step is pre-processing, which involves applying techniques, such as noise reduction, binarization, skew correction, and size normalization of scanned images and documents to prepare them for analysis and extraction.
Pre-processing helps to minimize errors and improves accuracy during subsequent stages of the process.
The next step is document classification: intelligent document processing software uses Artificial Intelligence (AI) to classify analog and digital documents into specific categories, such as survey forms, legal contracts, purchase orders, and invoices. This step is critical as it sets the stage for the following steps in the process.
After document classification, IDP systems extract information via OCR, which converts it into machine-readable data. NLP is then applied to identify, extract, and validate specific types of information, including names, dates, numbers, and addresses.
Validation is essential for downstream processing, and IDP systems use a combination of automated and manual validation to improve accuracy and reliability.
Algorithms, rules, and checks are applied during automated validation. Trained ML algorithms can recognize patterns within data and flag anomalies or inconsistencies. The system can cross-reference, for example, extracted data such as company names and addresses with an internal database.
Manual validation, if required, is applied to complex or ambiguous data flagged during the automated validation process.
For maximum efficiency, automated systems handle the bulk of validation tasks and human reviewers focus on complex and high-risk areas requiring higher scrutiny.
Depending on the intended purpose, the extracted and validated data is routed to relevant applications for further processing. For example, at law firms, the data can be routed to a case or contract management software for further processing. At accounting firms, the data can be routed to payroll or billing and invoicing software.
Advanced ML algorithms on IDP systems continuously learn and adapt to improve performance and reduce errors.
Quantifiable metrics, such as error rates and processing time, provide insight into the performance of IDP systems. This insight is a valuable decision-making tool for business leaders and technical managers, helping them evaluate ROI for IDP systems and take appropriate actions to resolve issues and improve performance.
Intelligent document processing is an invaluable tool that catalyzes digital transformation initiatives.
Manual document processing is time-consuming and prone to human error. To scale operations, businesses must allocate more human resources. For practical reasons, the allocated resources can not be increased beyond a certain limit.
IDP removes the limitations of manual processing and allows businesses to scale document processing multifold. Furthermore, the process is essentially error-free.
Traditional document processing is expensive: in addition to labor, there are associated costs for accuracy and lost productivity.
Compared with manual systems, IDP systems offer excellent ROI and a payback period of months rather than years.
The error rates associated with manual document processing vary with document type. Research demonstrates that the human error rate for simple spreadsheets can go up to 40%.
In contrast, IDP offers substantially lower error rates, typically less than 1%. This is a major improvement and improves efficiency while reducing costs associated with manual validation.
Manual, as well as automated, decision-making is dependent on data. The quality, accuracy, and speed at which data is made available are critical factors in the process.
Manual document processing, with its high processing time and low accuracy, impedes effective decision-making.
IDP systems, with significantly higher document throughput and accuracy, enable better and faster decision-making.
IDP systems integrate seamlessly with third-party applications for processing and workflow automation. This feature enables further productivity improvements and drives down costs associated with human intervention.
IDP is a solution that finds application in many industries and commercial sectors, including the following.
At 45%, the finance and accounting sector has a majority share of the IDP market, where it is used to automate, streamline, and enhance various document-intensive processes, such as
IDP is widely used in the healthcare industry for the management of patient records and claims processing, in addition to the following
The legal sector benefits from the reduced manual workload, automated processing, and enhanced compliance offered by IDP solutions. Here are a few examples:
Human resources (HR) is another sector that has to deal with a large volume of documents. Here are some examples of how IDP can benefit HR:
IDP solutions will only benefit businesses when they are implemented properly. Follow this step-by-step process to gain the maximum benefit from your IDP initiatives.
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Each business has its own needs and short- and long-term objectives. The solution that is right for another company will not be ideal for your business.
First, you must understand how document processing can help you achieve business goals. Evaluate your current document processing workflows to identify pain points, inefficiencies, and areas where automation can deliver significant benefits. This will help you define what you want to achieve with IDP, such as reducing processing time, improving accuracy, etc., and how these goals will tie into your business objectives.
Once you have clarity about goals, you can start evaluating vendors, and research and compare different IDP solutions based on their features, scalability, ease of integration, and customer support.
Before finalizing a solution, you can run a pilot test to verify the system’s effectiveness before going all in with a full-scale implementation.
As the saying goes, “Well planned is half done.”
Create a comprehensive implementation plan with timelines, resource allocation, key milestones, and roles and responsibilities, while considering contingency plans for potential risks and challenges.
“No data is clean, but most is useful.” Dean Abbott, Co-founder and Chief Data Scientist at SmarterHQ.
To be truly useful, existing data must be prepared, which involves evaluating data quality, removing incorrect, incomplete, or duplicated data, and adding labels to raw data to provide context.
Therefore, implement systems for data preparation. This step is critical for improving the effectiveness and efficacy of IDP systems.
Before proceeding further, verify that the IDP solution integrates seamlessly with your existing systems, such as ERP systems and CRMs. You may need to develop APIs or other software to facilitate the integration.
Once the data is prepared and integration with other systems is verified, you can customize the IDP solution per your specific business requirements.
If the solution uses ML models, train these models on your document types and business rules. Periodically fine-tune the ML models based on feedback and new data.
No system will deliver benefits if users don't know how to or don’t want to use it.
Therefore, once the system is set up, it's time to train the users. Provide comprehensive training, emphasizing the benefits of the system and how it will make their jobs easier and more impactful.
Create stunning video documentation with Documentations AI for your training sessions. Video is engaging, maximizes knowledge retention, and learners can use it at their convenience.
Test and validate the system before implementing it. Rigorously test for correct document processing and integration with other systems. You can also verify if the system will help you meet business goals.
During testing, it is important to involve end-users to ensure their needs and expectations are met. User feedback can help you make necessary adjustments to improve system performance.
Once testing is complete and issues are resolved, deploy the IDP solution across the organization. You can deploy in phases or as a full rollout depending on your needs.
After deployment, monitor the system's performance by tracking key performance indicators (KPIs) such as processing time, accuracy, and user satisfaction, to ensure it meets expected outcomes.
Intelligent document processing is a powerful solution that overcomes the limitations of manual document processing.
While some of its core technologies, like OCR, have been around for decades, the recent advances in AI and ML have significantly enhanced its performance and benefits and enabled its application for a higher number of use cases.
IDP is not the only technology that has benefitted from AI: Documentations AI offers a platform that allows you to create stunning video and written documentation from simple screen recordings.
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