When you call the doctor’s office to schedule an appointment, one of the first things they ask you for is your name and birthday. This is a form of data extraction, which makes it simple for the receptionist to find your files in the system.
Data extraction is a way to replace manual data entry via metadata extraction from documents. There are three different types of data extraction:
- Graphical: This method can only be used with forms; the intelligent document understanding extracts the data through a visual observation.
- Rules-based: This technique is performed by using keyword anchors and expressions such as “birthday” or “social security number”.
- Semantic understating: In this method, areas of interest are connected through a metadata field and run on many documents allowing it to learn and retrieve the information in the future without human interaction.
Companies and vendors that utilize data extraction have established three ways of understating.
- Self-learning: The utilization of semantic understanding and machine learning.
- Fuzzy understanding: Knowing that e-mails and documents can include misspellings or mistakes, this method finds data on degrees of truth rather than absolutes.
- Validation systems: This is similar to a database look up. However, since misspellings can occur in databases too, fuzzy understanding still applies.
Want to learn more about data extraction? Read our whitepaper about this topic.