Intelligent document processing (IDP) is the automated classification and extraction of data from documents providing information required by a business processes. Machine learning (ML) and Artificial Intelligence (AI) technologies are essential components of modern Intelligent Document Processing solutions.

The Intelligent Document Processing (IDP) market is rapidly expanding, leveraging advanced AI technologies to automate the extraction, classification, and processing of information from structured, semi structured, and unstructured documents. As a critical component of digital transformation, IDP addresses challenges in manual document handling, data extraction, and regulatory compliance management.

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. Machine learning (ML) and various Artificial Intelligence (AI) technologies are essential components of modern Intelligent Document Processing solutions.

IDP encompasses a variety of cutting-edge technologies and solutions, including Optical Character Recognition (OCR) and handprint recognition (ICR), Optical Mark Recognition (OMR) for form scanning, Natural Language Processing (NLP) and text analytics, Content Classification and separation, Data extraction from text, tables, and images, Content Parsing, anonymization, and validation, and Chain of Custody Tracking.

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.