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.
Image credit: Rimell.com
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.
Nikole
