Glossary
Below is a glossary of essential terms to help you understand and maximize intelligent document automation.
Documents classification
Document classification involves assigning one or more classes or categories to a document
Structured document
A structured document is one that follows a consistent pattern. The layout and design (such as colors, fonts, and images) are uniform across all copies of the document. An example of a structured document is an identity document, where every copy adheres to the same format.
Semi-structured document
A semi-structured document contains specific information known in advance, but the position and format of this information may vary within the document. The most common example is an invoice.
Unstructured document
An unstructured document is one that does not adhere to any specific format or content constraints. A typical example of an unstructured document is a contract.
Human errors
Human errors are unintentional mistakes made by individuals, which can reduce the quality, safety, and accuracy of a given activity.
Information extraction
Information extraction involves finding and retrieving data from documents and other sources.
Human-in-the-Loop (HITL)
This is the process where human experts validate the results generated by machine learning models. Combining machine learning with human review offers the best of both worlds.
Intelligent document processing (IDP)
A type of document automation that involves acquiring, extracting, and processing data from various document formats. The goal is to classify, categorize, and extract relevant information using artificial intelligence and deep learning technologies like natural language processing (NLP) and computer vision techniques.
Optical Character Recognition (OCR)
Technology that detects the characters in a document and converts them into machine-readable digital text.
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