Data extraction is a crucial part of document processing that allows businesses to extract valuable information from their documents quickly and efficiently.
In this article, we quickly define automated data extraction for document processing. Following which, we discuss different document types, and different data extraction components.
Most importantly, we provide a step-by-step guide for businesses to choose an automated document processing software by discussing:-
i) Scale of the problem,
ii) Need for automated document classification
iii) Required accuracy metric,
iv) Need for customization
v) Cost and ROI of the project.
By the end of this article, readers will have a comprehensive understanding of automated data extraction and be able to make informed decisions about which approach is best for their specific needs.
So, let’s jump right into it:-
What is data extraction?
Data extraction can be defined as the process of transforming unstructured or semi-structured data into structured information. This structured information provides companies with meaningful insights to be available for reporting and analytics.
Automated data extraction is the process of extracting data from unstructured or semi-structured data without manual intervention. It is a pipeline with components like Data preprocessing, Data extraction, and Data validation. The higher the accuracy of the Data extraction component, the higher the automation.
Data extraction from documents for automated document processing
However, systems often have to deal with long, textual data made of long strings of typed characters. These documents may contain images, videos, spreadsheets, audio files, and other multimedia content. This data is collectively referred to as unstructured data because it had no fixed format.
When we look at documents from this lens, all documents collectively can be categorized into the unstructured data category.
This is the first point of confusion – unstructured and structured data do not map to structured and unstructured documents.
All documents are unstructured data. But within these documents, we can further classify them into three categories based on how they appear:
- Structured Documents
- Semi-Structured Documents
- Unstructured Documents
