Addressing the challenges of efficient document processing, explore a novel solution to extract structured data from documents using Azure AI Document Intelligence and Azure OpenAI.
In today’s data-driven landscape, efficient document processing is crucial for most organizations worldwide. Accurate document analysis is essential to provide much needed streamlining of business workflows to enhance productivity.
In this article, we’ll explore the key challenges that solution providers face with extracting relevant, structured data from documents. We'll also showcase a novel solution to solve these challenges using Azure AI Document Intelligence and Azure OpenAI.
ISVs and Digital Natives building document data extraction solutions often grapple with the complexities of finding a reliable mechanism to parse their customer’s documents. The key challenges include:
With many Azure AI services to build solutions with, it can be difficult for teams to identify the best approach to resolve these challenges.
As solution providers for document data extraction capabilities, the following approach enables these benefits over other approaches:
Let’s explore how to extract structured data from documents with both Azure AI Document Intelligence and Azure OpenAI in more detail.
Updated in March 2024, the pre-built layout model in Azure AI Document Intelligence gained new capabilities to extract content and structure from Office file types (Word, PowerPoint, and Excel) and HTML, alongside the existing PDF and image capabilities.
This introduced the capability for document processing solutions to take any document, such as a contract or invoice, with any layout or file format, and convert it into a structured Markdown output. This has the significant benefit of maintaining the content’s hierarchy when extracted.
This is important when we consider the capabilities of the Azure OpenAI GPT models. GPT models are pre-trained on vast amounts of natural language data, which helps them to understand structures and semantic patterns. The simplicity of Markdown’s markup allows GPT models to interpret structures such as headings, lists, and tables, as well as formatting such as links, emphasis (italic/bold), and code blocks.
When you combine these capabilities for data extraction with efficient prompting, you can easily and accurately extract relevant data as structured JSON.
The following diagram illustrates this novel approach, introducing the new Markdown capabilities of Azure AI Document Intelligence’s pre-built layout model with completion requests to Azure OpenAI to extract the data.
This approach is achieved in the following way:
For a full code sample demonstrating this capability, check out the using Azure AI Document Intelligence and Azure OpenAI GPT-3.5 Turbo to extract structured data from documents sample on GitHub. Along with the code, this sample includes the necessary infrastructure-as-code Bicep templates to deploy the Azure resources for testing.
Adopting Azure AI Document Intelligence and Azure OpenAI to extract structured data from documents simplifies the challenges of document processing today. This well-rounded solution offers significant benefits over alternatives, removing the requirement to train custom models and improving overall accuracy of data extraction in most use cases.
Consider the following recommendations to maximize the benefits of this approach:
By adopting this approach, solution providers can streamline their document processing workflows, enhancing productivity for themselves and their customers.
Thank you for taking the time to read this article. We are sharing our insights for ISVs and Digital Natives that enable document intelligence in their AI-powered solutions, based on real-world challenges we encounter. We invite you to continue your learning through our additional insights in this series.
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