Intelligent Document Processing (IDP) is a technology that combines various aspects of artificial intelligence (AI) and machine learning to automate the extraction, understanding, and processing of information from unstructured documents. Unstructured documents can include text-heavy files like invoices, contracts, purchase orders, financial statements, emails, and other forms of business and administrative documents.
IDP solutions typically involve the following components and processes:
- Document Capture: The first step is to capture or import the unstructured documents into the IDP system. This can be done through scanning, uploading digital files, or integrating with email systems.
- Document Recognition: IDP systems use Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques to recognize and extract text, data, and metadata from the documents. OCR converts printed or handwritten text into machine-readable text, while NLP helps in understanding the context and meaning of the content.
- Data Extraction: After document recognition, the system extracts relevant information, such as dates, names, addresses, invoice numbers, and financial figures, from the documents.
- Data Validation and Verification: IDP can also perform validation and verification checks to ensure the extracted data is accurate and compliant with predefined rules. For example, it can check whether an invoice total matches the line items, or if a purchase order adheres to company policies.
- Data Integration: The extracted data is typically integrated into other business systems, such as Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) software, to facilitate further processing and decision-making.
- Workflow Automation: IDP often includes workflow automation capabilities, allowing organizations to route documents for approval, initiate actions based on the data extracted, or trigger notifications and alerts.
IDP can offer significant benefits to organizations by reducing manual data entry, minimizing errors, improving data accuracy, enhancing compliance, and accelerating business processes. It is commonly used in various industries, including finance, healthcare, legal, and logistics, where large volumes of documents need to be processed efficiently and accurately.