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Data Extraction from Documents

Know the Pro Ways to Do Data Extraction from Documents

All the data your organization uses in everyday life is available in various sources and formats. Before you can begin using it, you must first extract it from a variety of sources and clean it up. The process of obtaining data from a source is called document data extraction. It is done either manually or automatically. It can be used to get information from many different places, like files, databases, and websites.

There are many reasons why document data extraction is essential, including the following:

– Information extraction from lengthy, densely packed texts that are difficult to read.

– Information extraction from texts posted online in PDFs, webpages, word documents, PDFs, or any other file type.

– Extracting information from texts that need to be translated into the native language but were published in other languages.

Before diving deeper into it, let’s first understand what type of data can be extracted from documents:-

  • Structured data

Structured data complies with a data model, has a clearly defined structure, follows a consistent order, and is simple for a person or computer program to access and utilize. Typically, structured data is kept in databases or other places with explicit schemas. Usually, this type of data is tabular with well-defined column and row headings for each of its properties. Structured data are extracted and processed efficiently. An example of structured data could be relational data.

  • Unstructured data

Unstructured data is data that is not established in terms of organization or lacks a preset data model, making it a poor fit for a standard relational database. Unstructured data can therefore be stored and managed on different platforms, and organizations use it in a variety of business intelligence and analytics applications. In IT systems, unstructured data is becoming more prevalent. For instance, media logs in Word, PDF, and other formats.

Types of document data extraction tools

There are two alternatives for document data extraction methods: logical and physical.

1. Logical extraction

It offers both full extraction and incremental extraction as options.

a. Full extraction

This document data extraction method ignores any changes made to the document since the previous time it was processed and extracts all the data from the document in one go. All data formats are converted into digitized text and then compared to previously extracted text to identify changes.

b. Incremental extraction

In this document data extraction method, you can keep track of the document’s modifications while doing so each time it is updated. As a result, just the changes made must be extracted for the data, which must then be added to the already retrieved digital file.

2. Physical extraction

There are two types of physical extraction options — online and offline extraction.

a. Online extraction

Online extraction involves direct data transmission from the document to the internal database for archiving. Access to the document is granted to the data extraction program, which subsequently extracts and sends data to the required system.

b. Offline extraction

This kind of intelligent document data extraction tool does not take the data straight from the original document. It is instead taken from another document that is not part of the extraction software.

Document data extraction technology types

  • Template-based OCR- Optical character recognition, or OCR, converts scanned images into structured data that can be extracted, edited, and searched. As we know, scanners are just the beginning of the digitization process for your records. It produces an image of the document, but neither the image nor the data can be searched for or edited. It is where OCR technology steps in to help with the data extraction from these scanned documents.
  • Computer vision- Computer vision plays an essential role in document data extraction. How? By applying AI algorithms for picture registration, segmentation, annotation, and multimodal image fusion, one can use computer vision to extract data from images to understand uncommon patterns. These include, among others, Dropbox, AWS Textract, Azure OCR, and GoogleVision. The area is experiencing an exciting period as more and more applications for computer vision algorithms become accessible.
  • IDP (intelligent data processing)- Modern IDP systems can handle millions of variations of documents, including invoices, receipts, loan documents, and insurance documents, without creating templates. They also offer Intelligent Data Extraction. The benefits of intelligent document data extraction include:
  • Eliminates time-consuming and tedious tasks so that the staff can focus on high-value jobs.
  • Reduces overall operating expenses as all incoming information from email, mobile phones, and paperwork is digitized. Also, fewer manual resources are spent on inputting and validating huge data sets.
  • Enhances organizational synergy since intelligent capture facilitates dynamic collaboration through a shared data set without geographical proximity.

Document data extraction is an automated process, which also makes the data usable. It verifies the collected data using cutting-edge computer vision and natural language processing so that downstream software can use it immediately.

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