Organizations rely on several types of documents for information exchange and important business insights, such as identifying critical suppliers, tracking material movements, and gathering customer feedback. However, these documents are ineffective in driving business decisions in their raw form. Until recently, enterprises largely extracted information from such documents via manual processes that were usually time-consuming, error-prone, and inscrutable. Intelligent Document Processing (IDP) offers an AI-based document-processing approach that helps automate data extraction from semi-structured and unstructured documents.
Often, semi-structured documents are considered simpler and easier to process than unstructured documents. However, this is not always the case. Semi-structured documents present their own challenges in the form of high variability in structure and multiple data types. Therefore, enterprises need to be cognizant of such complexities to fully understand the capabilities they need from their IDP platforms.
In this viewpoint, we examine the benefits of adopting IDP solutions to overcome the complexities associated with semi-structured document processing.
Scope
All industries and geographies
Contents
In this report, we examine:
Document processing challenges for enterprises
IDP solutions and their capabilities
Complexities of semi-structured document processing
Application of IDP solutions across common semi-structured documents
Key considerations and best practices to ensure successful IDP adoption
AI has advanced rapidly in recent years, with continuous advances in Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), among other areas. However, there is one area in which traditional AI has fallen short – displaying…
