A Comprehensive Guide to Document Data Extraction in 2023

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Document data extraction plays an essential role in this data-driven world, where firms continuously fetch information from different sources. Then, convert this into an editable format to analyze and make better decisions. The importance of proficient text recognition tools should be considered as organizations depend on data to drive their operations. As Statista states, the value of the document data analytics market is projected to reach more than 655 Billion US dollars in 2029 from 241 Billion in 2021. 

What is Document Data Extraction?

The OCR data extraction process fetches relevant data from different document types, whether print or digital. It concerns recognizing and retrieving essential data points such as purchase order numbers, invoices, addresses, and names. 

The procedure allows firms to have valuable data hidden in unstructured documents. The only goal is to transform unstructured data into structured and easily keep data in warehouses for different business intelligence initiatives. 

Document Types 

Most organizational workflows involve acquiring details from print media. So, they use OCR services to convert text images into data. Let’s see how the organization deals with different unstructured documents, such as: 

  1. Legal Documents

Organizations extract data from licensing agreements, contracts, service-level agreements, and non-disclosure agreements, which are a few standard documents 

  1. Healthcare Records

These involve medical data such as prescription or electronic health records, lab reports, etc. 

  1. Finance and Banking Documents

Usually, these involve loan applications, financial statements, and account opening forms. 

  1. Insurance Documents

Insurance firms usually extract data from policy documents, insurance applications, medical records, and claim forms. 

  1. Invoices

Key details extracted from the following documents include names, vendor details, contact information, invoices, tax numbers, line-item details, subtotals, discounts, and payment terms. 

OCR technology is used to extract data, so let’s see how firms can use this service to improve their business operations.

Uses of OCR

The following use cases demonstrate how OCR can extract meaningful information and easily convert text from images. 

  1. Banking

OCR text scanner is a popular technology in the banking industry used to verify and process paperwork to deposit checks, loan documents, and other financial transactions. This verification removed fraud and improved transaction security. Some industries provide finances to small and medium-sized businesses. They used a cloud-based OCR app to develop a good product for small companies to access loan programs quickly. 

  1. Healthcare

The healthcare industry uses OCR scanners online to process patient records, such as tests, insurance payments, treatments, and hospital records. It assists in streamlining workflows and minimizes manual work at the hospital by keeping patient records up-to-date. 

Some companies provide medical and health insurance that accepts hundreds of claims per day. Its clients take medical invoice pictures and submit these via OCR apps. 

  1. Logistics

Logistics firms use OCR technology to effectively chase invoices, labels, receipts, and other essential documents. Manual entry of physical paper documents was error-prone and time-intensive as employees had to enter data in different accounting systems. However, advanced software reads characters more efficiently among different layouts, improving business efficiency. 

Optical Character Recognition Benefits 

OCR service providers want to improve efficiency and data accuracy by scanning different documents, extracting information, or translating it into editable, consistent, and machine-encoded format. 

  1. Operational Efficiency

Firms can enhance efficiency by using OCR technology to incorporate digital or document workflows in the business automatically. Below are a few examples of what OCR can do: 

  • Scans hand-filled documents for automated analysis, reviews, verification, and editing. This saves the employees time needed for conventional data entry and document processing. 

  • Transform handwritten text into editable documents and texts. 

  • Discover the needed document by instantly looking for a term in the warehouse so that employees don’t have to search the files manually. 

  1. Artificial Intelligence Solutions 

OCR service is almost a part of other artificial intelligence solutions that firms can execute. For instance, it scans road signs and number plates while driving cars, detects company logos in social media captions, or sees goods packaging in publicity images. So, artificial intelligence technology aids organizations in making better operational and marketing decisions that minimize expenses, improving the client experience.

  1. Searchable Text

Organizations convert their old and new data into fully searchable archives. They also process the text automatically using data analytics software for knowledge processing. 

Final Verdict 

The advanced technology offered by OCR is impressive in its abilities. In a business context, with the right conditions, text recognition dramatically enhances productivity or efficiency, improving the efficacy of data handling systems and yielding cost savings. 

Considering a wider societal perspective, OCR technology has proven invaluable in aiding visually impaired users, providing translation services, encouraging text-to-speech apps, and indexing historical documents. 

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