Learn how Intelligent Document Processing (IDP) can transform your workflow by automating and enhancing tasks related to documents. IDP is a technology that extracts and organizes data from documents to drive business process automation. It integrates optical character recognition (OCR) with artificial intelligence (AI) and machine learning (ML) algorithms to streamline the processing of complex documents in various formats. Unlike conventional OCR solutions, IDP not only identifies and extracts text from documents but also comprehends the context and meaning of the information.
Intelligent Document Processing (IDP) utilizes advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) to automate document handling. This process includes data extraction, content comprehension, and decision-making based on the information within the documents.
IDP automates manual data entry from paper documents or images to seamlessly integrate with digital business processes. For instance, in a workflow that automatically places orders with suppliers when inventory is low, no shipment occurs until payment is confirmed. Typically, the supplier sends an invoice via email, and the accounts team manually inputs the data before processing payment, which can introduce delays or errors. IDP systems, however, automatically extract invoice details and input them into the accounting software in the necessary format. Document processing can automate management tasks by leveraging machine learning (ML) and various AI technologies.
IDP surpasses traditional Optical Character Recognition (OCR) by integrating Natural language processing (NLP) and other AI methods to grasp context and semantics, resulting in more precise data extraction and categorization.
IDP uses a range of technologies to process different kinds of documents.
Optical character recognition (OCR) converts an image of text into a machine-readable text format. You can use OCR to scan paper documents and convert them into images with searchable text data. OCR is vital to document processing because it converts paper forms, receipts, invoices, contracts, legal documents, and more into digitized documents.
NLP is an ML technology that enables computers to analyze, interpret, and understand human language. NLP software processes text and voice data to analyze the sentiment, content, or intent. NLP uses a range of technologies—including ML, computational linguistics, and deep learning models—to process human language. The following are some of these technologies:
NLP is especially useful when working with unstructured documents and unstructured data, like live recordings or human speech.
Robotic process automation (RPA) is a form of technology that facilitates the building and deployment of software that automates human actions. You can automate business workflows with RPA software. For example, a user can record how they process a document. The RPA software then repeats the same steps, eliminating the need for manual document processing work. You can use RPA to automate any process, from data extraction to data capture and more.
Data is at the heart of digital transformation, yet most business data is inaccessible, embedded in documents, emails, images, and PDFs. AI document processing makes any data accessible for business processing by converting unstructured and semi-structured documents into usable information to fuel automating document-centric business processes. IDP uses AI technologies such as natural language processing (NLP), computer vision, machine learning (ML), and generative AI to classify, categorize, and extract relevant information, as well as validate the extracted data. IDP tools are completely non-invasive, integration-friendly, and work seamlessly with Intelligent Automation to power digital operations.
The first step in intelligent document processing is pre-processing. This step involves binarization, noise reduction, de-skewing, and de-speckling. These techniques help to improve the quality of the document images before they are processed by OCR and AI algorithms. This ensures that the data extracted is as accurate as possible, minimizing errors in downstream processes.
The next step is intelligent document classification. This step involves NLP, unsupervised and supervised learning, OCR, and Google Vision to classify documents based on their type and content. This allows for more efficient routing of documents to the appropriate processing workflows. To decipher difficult content, intelligent character recognition (ICR) takes OCR to the next level, applying AI to better identify glyphs and other textual elements that are difficult to read.
The third step is data extraction, where AI algorithms are used to extract relevant data from the classified documents. This can include text, numeric values, and even images or signatures. Extraction employs NLP, deep learning, machine learning, OCR, and Google Vision.
The fourth step is domain-specific validation, accomplished by applying fuzzy logic, regular expression (RegEx), rules, and scripts to assess, match, and manage the extracted data for accuracy and relevance to the specific industry or business context. Additionally, enhanced validation with robotic process automation (RPA) can further verify the extracted data for suitability to the prescribed purpose or process.
Human-in-the-loop (HITL) validation is another component of IDP that increases the quality of automated data processing. HITL validation uses supervised learning to provide a rapid feedback loop and fine-tune AI training by correcting data via human input.
Automation is limited by the availability of data to work with. In typical RPA-driven automation systems, initiating data extraction for automation often requires a separate third-party project, which can incur additional costs and create fragile integration points.
Efficient data extraction and organization are key to automating the majority of business processes that currently depend on manual effort and intervention. By integrating intelligent document processing into a comprehensive Intelligent Automation platform, businesses can achieve full end-to-end process automation. When IDP and Intelligent Automation are combined within the same platform, the essential components of the automation system work together seamlessly.
From OCR to generative AI, intelligent document processing technology continues to advance and play a central role in automating business processes.
Document processing has long been a labour-intensive and time-consuming task for organizations. Data entry represented a full-time effort in and of itself. For decades, optical character recognition (OCR) provided the only data extraction solution, enabling partial automation of data capture by converting images into text. OCR solutions applied templates to map extracted text into a usable structured format.
With the rise of computing and digital documents, the amount of business data increased astronomically. Initial document processing solutions provided user-friendly interfaces atop OCR functionality. This added accessibility, making it easier to connect OCR output with desired data fields.
Intelligent document processing gets its name from the AI technologies that power its data extraction and transformation capabilities, extending automation beyond structured and semi-structured documents to unstructured information. At the core of most IDP solutions are machine learning (ML) models that address a specific range of use cases, such as invoices or mortgage documents, enabling high-accuracy data extraction and processing but requiring extensive training.
Recent advancements in AI have led to transformative change in IDP technology. Driven by the emergence of generative AI and the integration of large language models (LLMs), innovations have opened up new possibilities for automating documents that could not be automated before.
IDP can interpret, classify, and extract data from a variety of document types, ranging from structured data to unstructured texts such as emails or reports. The following is an overview of the process.
The first step in IDP is capturing and classifying documents. This involves importing both paper and digital documents into the system. Document processing tools use AI to recognize and categorize different types of scanned documents, such as invoices, purchase orders, or legal contracts. This classification is crucial for determining the subsequent processing steps for each document type.
After classification, the system extracts relevant data from the documents. Using OCR and NLP, IDP systems accurately identify specific information such as dates, amounts, or names.
After extraction, the system also performs data validation to ensure accuracy. For instance, the system might cross-reference extracted data with existing databases or use predefined rules to check for errors.
After validation, the extracted data is processed according to its purpose. For instance, invoice data might be routed for payment processing, and contract details could be sent to a legal platform. The IDP system integrates with other business systems, such as ERP and CRM, for seamless data flow and automating actions based on the processed data.
A key feature of IDP systems is their ability to learn and improve over time. By using ML algorithms, the systems learn from previous errors and adapt to changes in document formats to enhance accuracy. The continuous learning process ensures that the system remains effective even as business needs and document types evolve.
IDP systems can track metrics such as processing time, error rates, and throughput volumes. They can be further processed by business analytics to derive insights that help identify bottlenecks, improve workflows, and make data-driven decisions for overall efficiency.
IDP offers a range of benefits for businesses. The following are some of the key advantages. By automating document processing, IDP reduces the time and effort required to locate, validate, and input data for business processes, allowing employees to focus on higher-value work.
Manual document processing can result in human errors, reducing the efficiency of your business. It also introduces limits on how many documents you can process at a time. With IDP solutions, you can accurately scan documents at scale. ML/AI solutions process documents without mistakes. You can manage heavy operational demands with improved accuracy and efficiency.
Automation of document processing and analysis reduces overhead costs. You can automate any repetitive tasks central to your operations and overcome bottlenecks, eliminating costs that arise from manual data entry and processing. You can leverage IDP to boost productivity and streamline workflows across your business operations.
With IDP, you can handle customer documents faster. You can use IDP to automate tasks such as customer onboarding, bookings, and payments that involve documentation. Chatbots can use data from customer documents to respond to customer queries in a more personalized manner. Providing answers and services to customers more quickly enhances customer relationships.
Intelligent document processing is useful to businesses in many different industries.
IDP improves the management of healthcare records. The healthcare industry must keep immaculate patient records across every touchpoint with a hospital or medical institution. Healthcare businesses use IDP to extract data from patient records and better organize medical documents. The healthcare insurance industry also uses IDP to verify claims and reduce manual paperwork in this field.
The financial sector uses IDP to automate several aspects of expense management and invoice processing. Businesses can streamline expense report generation by extracting data from expenses, forms, and business receipts. Financial departments can manage employee and contractor payments with speed and efficiency. For example, an IDP solution can extract figures from financial documents and process data for future payments.
Businesses in the legal sector can use IDP to analyze contracts. Legal teams use natural language processing (NLP) to analyze a legal contract's terms and obligations. They can extract data from legal documents and court records to build more robust legal cases.
Businesses that work in logistics need to track shipments, transit permits, and other vital documents. Companies use IDP for processing documents to reduce the chance of a human error creating a critical mistake. IDP helps with data extraction, validation, and classification, so companies in the logistics sector can speed up logistic functions.
Human resources (HR) agents use IDP to extract important information from a candidate’s resume. An IDP system saves time and ensures that HR teams focus on choosing between top candidates. The HR industry also uses IDP when managing payroll, leave allotment and other HR functions.
The future of IDP is bright, with continuous advancements in AI and ML driving further improvements in accuracy and capabilities. One emerging trend is the use of cognitive automation, which combines IDP with other AI technologies to create more intelligent and autonomous systems.
As more businesses adopt IDP, we can expect to see increased integration with other enterprise systems, such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms. This will enable seamless data flow and further streamline business processes, paving the way for a more efficient and automated workplace.
Intelligent document processing has wide-reaching benefits across functional areas within enterprises. Some of the functional areas that can benefit the most from IDP include finance, legal, and HR by automating document-based workflows and improving efficiency. Any function that deals with large volumes of documents and complex data is an ideal candidate for streamlining processes through IDP.