Intelligent document processing (IDP) is often confused with optical character recognition (OCR) because they both aim to achieve the same general goal: machine reading.
IDP is different than OCR because it is a software platform that combines multiple tools and technologies to process data. OCR is a single tool that converts pixels to characters and has minimal effectiveness on its own.
All IDP systems rely on OCR technology to translate pixels into the characters they represent. This technology is referred to as an "OCR engine" because it is always part of a bigger outcome. Your car needs an engine, but the engine is not a car.
IDP platforms will use any number of OCR engines to achieve desired outcomes. Some engines are trainable, while others perform better with certain fonts, or on handwriting, for example.
Ultimately, IDP solutions include technology needed to:
- Make OCR engines perform better
- Process OCR results for increased accuracy
- Make decisions about data
- Find and extract specific information
- Integrate data with other systems
None of the above is provided by OCR alone, and none are possible without it. So you see why OCR and IDP are often confused or lumped into the same category.
IDP is like a factory with multiple assembly lines that all result in a final product being created.
OCR is simply one of many assembly lines.
Understanding the Power of IDP and OCR
IDP solutions wouldn't exist without OCR because so much information is still locked in traditional documents that are emailed, printed, or scanned. People often assume that after a document is scanned, it is easy for software to "read," but this is simply not true because OCR is not very accurate on its own.
Getting highly accurate OCR results is only possible using an IDP software because of all the other technology used to improve accuracy. If you're thinking it's a chicken-and-egg conundrum, you are right!
Both Are Required for Optimum Success
IDP wouldn't exist without OCR, and OCR on its own is not hugely impactful to data integration from documents. Early use of OCR made full-text document searching more powerful, but the quality of data isn't good enough to use in core business processes that have little margin for error.
OCR often struggles to differentiate characters that look similar. These "small" errors add up to big problems if we need to trust that the data is accurate. In addition, OCR can't fill in the blanks for certain data types like dates or other fields that may be entered in any number of formats.
IDP gives OCR the best shot at being accurate, and then processes the OCR output using machine learning, natural language processing, and other techniques to create actionable information from text.
