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Aadit Unni
Aadit Unni

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Extract raw text, table, and forms from scanned documents using Amazon Textract.

[23/100] #100DaysOfCloud Today, I extracted raw text, table, and forms from scanned documents using Amazon Textract.

  • Amazon Textract is a fully managed machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables.
  • Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes).
  • To overcome these manual and expensive processes, Textract uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. - - You can quickly automate document processing and act on the information extracted, whether you’re automating loans processing or extracting information from invoices and receipts.
  • Textract can extract the data in minutes instead of hours or days. Additionally, you can add human reviews with Amazon Augmented AI to provide oversight of your models and check sensitive data.

Use cases :

  • Financial services

    • Accurately extract critical business data such as mortgage rates, applicant names, and invoice totals across a variety of financial forms to process loan and mortgage applications in minutes.
  • Healthcare and life sciences

    • Better serve your patients and insurers by extracting important patient data from health intake forms, insurance claims, and pre-authorization forms. Keep data organized and in its original context, and eliminate manual review of output.
  • Public sector

    • Easily extract relevant data from government-related forms such as small business loans, federal tax forms, and business applications with a high degree of accuracy.

You can try do it by yourself by following the steps from the link below:

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