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.
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In this report, we examine: