Initially referred to the ability to extend OCR from reading printed characters, As its name implies, Intelligent Document Processing (IDP) now encompasses a broader range that uses advanced technologies, such as AI, OCR, natural language processing, and computer vision, in order to process documents that contain hand-written characters, unstructured data, and other complex documents more intelligently.
In particular, institutions or organizations use IDP to convert unstructured* and semi-structured* information into data fit for computer processing. Moreover, it can alsomay be able classify and verify the data to prepare them for vital business processes.
Unstructured data pertains to information that does not follow conventional models, which makes it difficult to store or manage in a relational database system. Some unstructured data sources are invoices, emails, records, media and entertainment data.
Meanwhile, semi-structured data does not also follow traditional models associated with a relational database, but it has a structure. Its difference from structured data is its lack of rigid or fixed schema. Some semi-structured data sources are emails, markup languages, zipped files, and web pages.
IDP’s verification process is one of its main advantages. After data extraction and classification, the IDP technology can authenticate documents by comparing them with official records and databases. It can check the validity of signatures, dates, and invoice numbers or detect document anomalies, such as font, pixel quality, or metadata changes.
Unlike OCR, which only converts images of texts into machine-encoded text, IDP has a deeper cognitive understanding of documents, which makes document processing more efficient and convenient for any business. Furthermore, IDP does not face issues in processing a variety of documents and does not have limited scalability.