What is Intelligent Document Processing (IDP)?

Intelligent document processing (IDP) automates the process of manual data entry from paper-based documents or document images to digital form, for integration with other digital business processes. For example, consider a business process workflow that automatically issues orders to suppliers when stock levels are low. Although the process is automated, no order is shipped until the supplier receives payment. The supplier sends an invoice via email, and the business’s accounts team enters the data manually before completing payment, introducing manual checkpoints that create bottlenecks or errors. Instead, IDP systems automatically extract invoice data and enter it in the required format in the accounting system. You can use document processing with machine learning (ML) and various artificial intelligence (AI) technologies to automate document management.

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IDP offers a range of benefits for businesses. The following are some of the key advantages.

Scalability

Manual document processing can result in human input errors, reducing the efficiency of your business. Manual data extraction 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. Automated document processing means that you can manage heavy operational demands with improved accuracy and efficiency.

Cost-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.

Customer satisfaction

With IDP, you can handle customer documents faster than traditional document processing. You can use IDP to automate tasks such as customer onboarding, bookings, and payments that involve documentation. Chatbots can perform further processing on 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, across diverse business processes.

Healthcare

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.

Finance

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.

Legal

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.

Logistics

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 logistics functions.

Human resources

Human resources (HR) agents use IDP to extract important information from a candidate’s resume during the approval process. 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 following image shows how IDP can extract key information from an employee’s pay stub, such as year-to-date (YTD) gross pay and sick time earned.

IDP uses a range of technologies to process different kinds of documents.

Optical character recognition

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.

There are several types of OCR, each of which has different applications:

• Simple OCR software uses matching algorithms to compare text images to text and font image pattern templates
• Intelligent character recognition (ICR) software uses ML software to process different image attributes, like curves and lines, to process text
• Intelligent word recognition uses principles similar to ICR but focuses on processing entire words instead of working on individual characters
• Optical mark recognition uses a matching algorithm to identify text systems, logos, and watermarks

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Natural language processing

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:

• Computational linguistics involves semantic and syntactic analysis to create frameworks that capture the essence of human language
• ML technology enables NLP models to improve their understanding of metaphors, sentence structure changes, grammar, colloquialisms, sarcasm, and other elements of human speech
• Deep learning neural networks enable computers to recognize, classify, and identify complex patterns in sample data

NLP is especially useful when working with unstructured documents and unstructured data, like live recordings or human speech.

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Robotic process automation

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.

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.

Document classification

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 intelligent document classification techniques, including AI. These techniques 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.

Data extraction

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. If the document data structure is well defined, structured documents become far easier to automate data extraction.

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.

Data processing

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.

Continuous learning

A key feature of IDP systems is their ability to learn and improve over time. Using ML algorithms, these systems can 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.

Reporting and analytics

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.

Amazon Web Services (AWS) offers two services to support your IDP and business process automation requirements.

• Amazon Textract intelligent document processing software makes it easy to automatically extract printed or handwritten text, layout elements, printed text, and data from any document. Amazon Textract uses ML to read, process, and understand any type of document without the need for manual interaction. With Amazon Textract, you can:
  • Extract vital information from business documents with a high degree of accuracy
  • Scale your document processing pipeline so that you have the flexibility that you need to adapt to market demands
  • Automate data processing in a secure environment that meets compliance standards
• Amazon Comprehend is an NLP service that uses ML to uncover valuable insights and connections in text. It’s a fully managed and continuously trained service, so you don’t have to manage the scaling of resources, maintenance of code, or maintenance of the training data. With the Amazon Comprehend intelligent document processing solution, you can:
  • Discover valuable insights from text in any form of document
  • Simplify the document processing pipeline by extracting sentiment, text, phrases, or topics from documents
  • Identify and redact personally identifiable information (PII) from private documents

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