Microsoft's Azure AI Document Intelligence provides a comprehensive solution for automatic scanning and analysis of documents in all file formats. We offer a hands-on introduction to this cost-effective AI technology.
Key facts:
Azure AI Document Intelligence (see Microsoft product page) is a component of the Azure AI platform that employs machine learning to scan, detect, and classify documents. It can handle a wide range of document types, such as invoices, receipts, forms, and even handwritten notes, as well as bespoke document formats.
Unlike standalone solutions like OmniPage or Adobe Acrobat Pro DC, the Azure-based solution integrates with your existing apps via API. This direct link enables you to build an application that is specifically matched to your needs and processes. Some IT work is required for API integration; however, the solution is relatively simple to configure and utilize. Microsoft also offers all code samples, which saves developers considerable time.
Advantages of Azure AI Document Intelligence
Costs
Azure incurs the typical usage-based expenses. The advantage of the cloud option is that you pay based on usage volume. This means you do not have to acquire a solution with hefty license charges, but can pay flexibly based on the pages or documents utilized ("pay as you go" model).
Azure AI Document Intelligence enables different sectors to save money and increase efficiency. In brief, the approach can be useful wherever a significant number of papers are consistently handled. Here are some instances.
Invoice processing: Azure AI Document Intelligence can help you automatically scan incoming invoices, extract key data like the amount, date, and invoice number, and send it to your accounting system. This reduces manual, time-consuming tasks and accelerates the overall accounting process.
Customer service inquiries can be handled more quickly by automatically assessing and categorizing incoming documents such as application forms or letters of complaint. This results in speedier assignment of requests to the appropriate workers, which improves customer service.
Azure AI Document Intelligence improves patient file processing efficiency by automatically gathering key information such as diagnosis and treatment plans. This helps to improve patient care and streamline administrative operations.
In logistics, computerized processing of delivery bills and bills of lading can result in speedier supply chain procedures by extracting and processing key information such as delivery addresses or product lists right away.
In the realm of digital humanities, Azure AI Document Intelligence helps to create digital archives by digitizing and analysing historical texts and manuscripts. Project Gutenberg, which makes thousands of digitized public domain books available for free, and the Internet Archive, which collects digital information ranging from websites to books and music, are two prominent instances of such document libraries. These applications provide widespread access to cultural and historical resources, encourage study and education, and make it easier to create interactive learning materials.
In no time at all, you can set up a new instance of the solution in Azure, try it out interactively in the Studio and then integrate it into your own processes via API.
About this short tutorial:
The steps at a glance:
If you do not currently have an Azure account, you can test Azure for free for 30 days and earn a $200 starting credit, which is more than enough for a large amount of data and experiments.
Now let's create a free cloud instance of Document Intelligence (formerly known as "Form Recognizer"). To achieve this, navigate to the "Azure AI Services" service in Azure and click the "Create" button to establish a new Document Intelligence resource. On this overview page, "Azure AI Services" will always be on the left in the menu beneath "Azure AI Document Intelligence".
Settings:
In the next step, we navigate to the Document Intelligence Studio in Azure.
In the Azure Cloud, there is an interactive “Studio” application for many Azure tools, with which you can easily test the tool.
As a test, we'd want to read a table from an annual report twice: once in PDF and once in scanned visual format. There are existing templates for this in the Studio.
Settings:
Result:
When you click on an extracted area, Document Intelligence displays the extracted data to the right of it, such as a complete table from a business report graphics file, with all cells and headers accurately identified. This is now available in a structured format, making it easier for machines to process.
Azure AI Document Intelligence may be easily integrated with existing applications. Programming languages supported include C#, Java, Python, JavaScript, and REST API.
There are various other software solutions on the market that perform comparable activities to Azure AI Document Intelligence, particularly in the areas of document analysis and processing with artificial intelligence and machine learning. Some of the solutions include:
Standalone (“On Premise”) solutions:
Cloud solutions: