Understanding IDP: Data Extraction

 

Gartner recently released, “Infographic: Understand Intelligent Document Processing”. According to Gartner, “The market for document capture, extraction, and processing is highly fragmented. Data and analytics leaders should use this research to understand the process flow and differentiated capabilities offered by intelligent document processing solutions.” In this series of posts, we speak to the 6 critical flows in Intelligent Document Processing (IDP) that Gartner covers and how Infrrd solutions stack up.

1. Capture or Ingestion
2. Document Preprocessing
3. Document Classification
4. Data Extraction
5. Validation and Feedback Loop
6. Integration

In this third post, we explore Data Extraction. (Check out our earlier posts in this series, Capture and Preprocessing, and Document Classification.)

Why did the world need a new data extraction solution?

When people hear data extraction, the first thing that usually comes to mind is OCR. For the last several years, traditional OCR solutions have been the preferred choice for extracting data. However, OCR solutions have their share of challenges because they are primarily focused on converting handwritten or printed text into a machine-readable, digital data format. 

Mere data extraction without intelligence for understanding what that data indicates is a huge waste of potential. With changing technology, businesses are benefiting from the advent of neural networks and algorithms for natural language processing or computer vision used in modern IDP solutions. 

Here is a comparison table between traditional OCR and modern IDP systems:

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