Decipher
Intelligent Document Processing

Intelligent document processing (IDP) uses a blend of computer vision, AI, machine learning (ML) and natural language processing (NLP) to digitize, classify and extract data from different types of documents — transforming and integrating this data into various systems or applications in downstream workflows.

The benefits of intelligent document processing include cost savings, efficiency improvements and enhanced accuracy across document-intensive processes, leading to increased productivity and smarter resource allocation.

Optical character recognition (OCR) and IDP are both technologies used to extract text and data from documents but differ in their capabilities and applications. OCR is basic text recognition technology, while IDP is much more advanced, using AI and ML to add layers of intelligence. OCR converts scanned images, PDFs or other document types into machine-readable text. OCR is widely used for digitizing paper documents and creating searchable archives.

IDP, on the other hand, is a much more advanced technology that goes beyond OCR to extract structured, semi-structured and unstructured data from documents. IDP employs AI and ML to understand the context of data, classify it into different categories, and validate and verify its accuracy.

IDP and RPA are essential intelligent automation components that automate end-to-end business processes. IDP uses the support of RPA and other cognitive technologies in IA to handle data from various document types, such as invoices, contracts, CVs, purchase orders, etc. 

For instance, IDP extracts crucial details with a handwritten purchase order, classifies it as a 'purchase order,' and validates data accuracy using ML. Then, RPA steps in to further process the data, like updating inventory systems or notifying human operators of errors. Together, IDP, RPA and cognitive IA enhance accuracy, efficiency and the scope of automation programs in organizations.

Many industries and business operations still rely on documents — whether physical or digital, structured or unstructured. IDP can be used for any industry reliant on manual data entry and processing large volumes of documents requiring accuracy.

For example, IDP processes a large volume of invoices, loan applications, insurance claims and other documents in financial services, banks and insurance companies that generally maintain a large staff to execute that type of work. In healthcare, IDP can manage patient information from medical records, insurance forms and lab reports. All the data is accurately captured and securely processed with IDP.

IDP typically involves several stages, including ingestion, processing, classification, extraction, validation and integration.

First, documents in different formats and from different sources are ingested and processed by intelligent document processing software. Then, they’re processed by type for automation. Next, AI and ML can be deployed to support the extraction of relevant data, irrespective of whether it’s structured, semi- or unstructured. Then, this is followed by validation to ensure data accuracy, applying a HITL for review where necessary.

The extracted data is then integrated into any number of tools and applications, including business process management (BPM) and RPA solutions, as well as enterprise resource planning (ERP) or customer relationship management (CRM) systems for use in downstream workflows.