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Intelligent document processing automation (IDP) is a set of machine learning, natural language processing (one of the main machine learning subfields), and artificial intelligence techniques, used to extract data from documents.
IDP is often assisted by optical character recognition (OCR). It can deal with any type of document: digitally typed, handwritten, or scanned. Because documents often contain pictures and text, computer vision algorithms are used as well. There are several standard steps, with specific cases requiring fewer or more stages:
IDP software uses robotic process automation, artificial intelligence, machine learning, and natural language processing to reduce or even eliminate manual processing and the associated errors that occur when humans carry out repetitive tasks.
Intelligent document processing solutions unlock the value of unstructured data. How? By transforming it into high-quality, structured, and relevant information that can be further analyzed.
Specific techniques that are used within IDP are:
There are three main data structure types:
Structured data: fixed-format documents like application forms and questionnaires. The layout often includes graphical elements such as boxes, checkmarks, and separators, but their position is fixed. Here, simple extraction is sufficient.
Semi-structured data: multi-variant documents with flexible layouts. There’s some visual layout such as boxes, but the format is more flexible, with variants of specific layouts. For example, you may have various invoice layouts from different vendors. This data type requires an IDP solution that can quickly learn new formats and field positions.
Unstructured data: documents with plain, natural language text. In this case, there’s little or no visual organization of text, and whole blocks of text must be read and understood before info is extracted. Because this is the most complex data type, it requires segmentation, entity extraction, and large volumes of data samples. Intelligent document solutions thrive in this type of data.
Optical character recognition (OCR) is a data conversion technique whereby an image of text is converted into a machine-readable form. This long-standing method is the basis of document scanning. But, OCR typically can’t extract context from the content, making automated data extraction and interpretation impossible.
Following advances in automated document processing, OCR is now a sub-process of IDP. Here are the steps:
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