Site icon VMVirtualMachine.com

Boost Efficiency When Handling Scanned PDFs with Amazon Q Business | Amazon Web Services

Boost Efficiency When Handling Scanned PDFs with Amazon Q Business | Amazon Web Services
Spread the love



Amazon Q Business is an AI-powered generative assistant that can answer questions, provide summaries, generate content, and extract information directly from PDF documents and scanned documents in their customers’ business data sources without the need to extract text first.

Clients in sectors such as finance, insurance, healthcare, biological sciences, and more need to extract information from various types of documents such as receipts, healthcare plans, or tax statements, which are often in scanned PDF format. These documents usually have a semi-structured or unstructured format, requiring processing to extract the text before indexing with Amazon Q Business.

The launch of compatibility with scanned PDF documents with Amazon Q Business can help process a variety of multimodal document types seamlessly through the AWS Management Console and API in all supported AWS regions. This feature eliminates the need for development effort to extract text from scanned PDF documents outside of Amazon Q Business and enhances the document processing process to create a generative AI assistant with Amazon Q Business.

In this post, we demonstrate how to index asynchronously and run real-time queries with scanned PDF documents using Amazon Q Business.

You can use Amazon Q Business to handle scanned PDF documents from the console, AWS SDK, or AWS Command Line Interface (CLI).

Amazon Q Business offers a versatile set of data connectors that can integrate with a wide range of enterprise data sources, allowing you to develop generative AI solutions with minimal setup. Once your Amazon Q Business application is ready for use, you can directly upload scanned PDF files to an Amazon Q Business index via the console or APIs. Amazon Q Business offers multiple data source connectors that can integrate and synchronize data from multiple data repositories into a single index.

To demonstrate document indexing, we use examples like an invoice, a health plan summary, an employment verification form, and some text documents. The indexing process involves uploading the documents to the Amazon Q Business application through direct upload or connectors like Amazon S3.

After indexing the documents, you can run queries on the Amazon Q Business interface to extract specific information or answer questions related to the content of the scanned PDF documents. The system can handle dense, unstructured, structured, tabular, and semi-structured data found within the documents, providing accurate and relevant responses.

In conclusion, Amazon Q Business provides a powerful solution for handling various types of documents, including scanned PDF files. The platform’s generative AI capabilities can effectively extract and analyze information from these documents, enabling users to gain insights and answers to their queries without the need for manual text extraction. The integration of scanned PDF documents expands the possibilities for businesses to leverage AI technology for document processing and analysis efficiently.

Article Source
https://aws.amazon.com/blogs/machine-learning/improve-productivity-when-processing-scanned-pdfs-using-amazon-q-business/

Exit mobile version