Azure Document Intelligence: Transforming Data into Insights

Azure Document Intelligence is a game-changer for businesses that deal with large amounts of unstructured data. It allows you to extract valuable insights from documents, such as contracts, receipts, and invoices.

By leveraging machine learning and natural language processing, Azure Document Intelligence can automatically classify and categorize documents, reducing manual labor and increasing efficiency. This means you can focus on higher-level tasks and make data-driven decisions.

With Azure Document Intelligence, you can also extract specific data points from documents, such as names, addresses, and dates. This information can be used to populate databases, trigger workflows, or inform business decisions.

Azure Document Intelligence is a cloud-based service that leverages machine learning models to extract data from documents.

It can automatically identify text, tables, and key-value pairs from a variety of document types.

This service helps businesses automate document processing and improve search capabilities.

Azure Document Intelligence can work with a range of document types, making it a versatile tool for many industries.

By automating document processing, businesses can save time and resources, and focus on more strategic tasks.

This service is designed to make document analysis easier and more efficient, helping businesses to get the most out of their documents.

Azure Document Intelligence offers a range of key features and capabilities that make it a powerful tool for extracting information from documents.

It supports custom neural models, which add support for overlapping fields and table cell confidence, starting with API version 2024-02-29-preview.

With Azure AI Document Intelligence, you can create custom models tailored to your specific business needs by training models with your data.

Custom neural models currently support key-value pairs, selection marks, and structured fields (tables), and also support overlapping fields, which have some limits.

The service can intelligently detect and extract tables from documents while preserving their structure, making it easy to work with tabular data.

Azure Document Intelligence can recognize and extract text in multiple languages, making it versatile for global use cases.

The service includes the following key features:

It provides prebuilt models that can handle widely used document types, such as invoices, receipts, and IDs, and can automatically extract text, tables, key-value pairs, and more.

Each extraction result includes a confidence score that indicates the accuracy level of the identified information, helping determine if further manual verification is needed.

Azure Document Intelligence integrates with other Azure services like Azure Logic Apps, Power Automate, and Azure Synapse Analytics, enabling the automation of document workflows, data transformation, and analysis.

Azure Document Intelligence is a powerful tool that uses AI to extract fields, text, and data from your documents and forms. It ingests content from forms and documents, applies machine learning technology to identify keys, associated values, and tables, and then outputs structured data that includes the relationships within the original file.

Azure Document Intelligence includes several prebuilt models for common types of forms and documents, such as invoices, receipts, and W-2 US tax declarations. You can also create a custom model and train it by using examples of completed forms.

Azure Document Intelligence provides APIs for each of the model types, supporting languages like C#/.NET, Java, Python, and JavaScript. If you prefer to use another language, you can call Azure AI Document Intelligence by using its RESTful web service.

Here are some of the prebuilt models available in Azure Document Intelligence:

Azure AI Document Intelligence uses three main types of models: Document Analysis Models, Prebuilt Models, and Custom Models, to extract and analyze text, structure, and data from documents.

These models can extract information quickly and accurately, without heavy manual intervention or extensive data science expertise, by applying machine learning technology to identify keys, associated values, and tables.

Document Intelligence classifies documents, extracts fields or key value pairs, and structure like tables, selection marks from documents and forms, making it a powerful tool for automating business processes.

The service includes options for extracting information from documents and forms, and can be used to integrate extracted data into business processes, improving automation and decision-making.

By using AI Builder and Azure Form Recognizer, you can configure a Power Automate workflow to extract text from images in stored SharePoint documents, making them more searchable and accessible.

Document Intelligence uses AI to extract fields, text, and data from documents and forms, and ingests content from forms and documents to apply machine learning technology and output structured data.

Using with Azure AI Document Intelligence is a breeze, thanks to its prebuilt models and APIs. You can use a model to inform Azure AI Document Intelligence about the type of data you expect to be in the documents you’re analyzing.

Azure AI Document Intelligence includes several prebuilt models for common types of forms and documents, such as invoices, receipts, and W-2 US tax declarations. These models can extract information from documents without training a custom model.

You can also use the general document analysis prebuilt models, which include "Read", "General document", and "Layout", to extract information from unusual or unique types of forms.

The service supports multiple programming languages, including C#/.NET, Java, Python, and JavaScript, making it easy to integrate into your existing applications.

Here are some of the prebuilt models you can use with Azure AI Document Intelligence:

These models can be used to extract information from documents, making it easier to automate tasks and improve document searchability.

The solution architecture of Azure Document Intelligence is a robust and efficient system that enables businesses to automate document processing. At its core, it consists of five key components: AI Builder, Form Recognizer, Power Automate, Azure Functions, and PnP Modern Search.

AI Builder is a Power Platform capability that lets you train models to recognize objects in images, while Form Recognizer uses machine-learning models to extract and analyze form fields, text, and tables from your documents. Power Automate is an online workflow service that automates actions across apps and services, and Azure Functions is an event-driven serverless compute platform that runs on demand and at scale in the cloud. PnP Modern Search is a set of SharePoint Online modern web parts that let you create highly flexible and personalized search-based experiences.

Here's a high-level overview of the solution architecture:

This architecture enables businesses to automate text extraction, improve searchability, and enhance document management by extracting and analyzing critical data from complex documents efficiently.

The solution architecture for Azure AI Document Intelligence is designed to automate document processing pipelines, making it easier to extract and analyze critical data from complex documents.

AI Builder is a key component of this architecture, allowing you to train models to recognize objects in images and use prebuilt models for object detection.

The solution consists of five main components: AI Builder, Form Recognizer, Power Automate, Azure Functions, and PnP Modern Search.

Here's a breakdown of each component:

By integrating these components, businesses can automate text extraction, improve searchability, and enhance document management, making it easier to extract and analyze critical data from complex documents.

To ensure your custom model performs well, it's essential to follow some best practices. Start with a neural model and test its functionality to determine if it meets your needs.

Dealing with variations is a common challenge when working with custom models. Create a single model for all variations of a document type, and add at least five labeled samples for each variation to the training dataset.

Labeling fields properly is crucial for accurate key-value pairs extraction. Name fields in the language of the document, and make sure the field name is relevant to the value. For example, if a field contains a supplier ID, name it "supplier_id".

When labeling contiguous values, ensure that value tokens or words of one field are either separate or together. They can't be a mix of both.

Representative data in training cases is vital for model performance. Use diverse and representative values, such as actual dates, instead of synthetic values like random strings. This will help your model learn and generalize better.

Data Ingestion and Processing is a crucial step in Azure Document Intelligence.

Documents are ingested through a browser at the front end of a web application.

The back-end application posts a request to a Form Recognizer REST API endpoint that uses one of the models mentioned above.

The response from Form Recognizer contains raw OCR data and structured extractions.

The App Service back-end application uses the confidence values to check the extraction quality.

When the extraction quality meets requirements, the data enters Azure Cosmos DB for downstream application consumption.

Azure Document Intelligence can be a game-changer for businesses looking to automate document processing. This service is ideal for processing documents with complex layouts or embedded text in images.

In the finance department, for example, invoices can be automatically scanned and key information such as invoice number, total amount, and due date can be extracted. This helps in automating accounts payable workflows and reducing manual errors.

Azure AI Document Intelligence can also be used to improve document searchability for regulatory and compliance purposes. By transforming complex images into accessible text, businesses can streamline their document management processes.

Here are some real-world use cases for Azure Document Intelligence:

In the healthcare sector, this service can be used to extract data from medical forms and insurance claims. This can include patient information extraction and insurance claims processing.

In the banking and financial services industry, Azure Document Intelligence can be used to automate loan application processing. This includes customer onboarding, loan document processing, and fraud detection.

Some specific examples of what can be extracted from documents include:

Azure Document Intelligence offers numerous benefits and advantages.

You can save time and effort by automating the extraction of text from images in your documents. This can be a huge time-saver, especially when dealing with large volumes of documents.

By using Azure Document Intelligence, you can improve the searchability and accessibility of your documents by adding metadata that reflects the content of the images. This makes it easier for others to find specific information within your documents.

This solution can also enhance your document management and analysis by using AI to identify and extract relevant information from complex diagrams. This can be particularly useful for businesses that deal with technical documents.

Here are some of the specific benefits of Azure Document Intelligence:

Azure Document Intelligence offers a flexible pricing model that allows you to pay only for what you use.

You can choose from a variety of pricing plans, including Pay as You Go, which is ideal for small projects or testing.

With Pay as You Go, you can get started with a free tier that includes up to 500 pages per month for web and container instances.

Here's a breakdown of the pricing for different instance types:

Note that the free tier does not support premium features, and does not include Query Meter.

Azure Document Intelligence has some limitations to be aware of. Custom neural models don't recognize values split across page boundaries.

If you're using a dataset labeled for custom template models to train a custom neural model, unsupported field types will be ignored. This can impact the accuracy of your model.

Custom neural models are also limited to 20 build operations per month. If you need more, you'll need to open a support request to increase the limit.

Here are the limitations of custom neural models in more detail:

To get started with Azure Document Intelligence, you can sign up for an Azure account or log in if you already have one.

Creating an Azure Form Recognizer Resource is a crucial step, which involves setting it up in the Azure portal under the Cognitive Services category.

You can start processing documents by uploading them, which can be PDFs, scanned images, or structured forms. This is done after creating the Form Recognizer resource.

To extract data from documents, you can choose a prebuilt or custom model. Prebuilt models are available for common documents like invoices or receipts, while custom models can be created and trained for specific documents.

Once the documents are processed, you can review the extracted data to verify its accuracy. This data can then be exported in a structured format, such as JSON or CSV.

Here's a quick rundown of the steps to get started with Azure Document Intelligence:

By following these steps, you can unlock the full potential of Azure Document Intelligence and automate workflows with other Azure services like Power Automate, Logic Apps, or Azure Synapse.

To get started with Azure AI, you'll need to create an Azure account if you don't already have one. This will give you access to all the tools and resources you need to get started.

First, you'll need to set up an Azure Form Recognizer Resource, which is now known as Azure AI Document Intelligence. This can be done in the Azure portal under the Cognitive Services category.

To get started with Azure AI Document Intelligence, you'll need to upload the documents you want to process. This can include PDFs, scanned images, or structured forms.

You can choose to use a prebuilt model for common documents like invoices or receipts, or create and train a custom model for your specific documents.

Here's a step-by-step guide to get you started:

Once you've uploaded your documents, you can review the extracted data to verify its accuracy. This data can then be exported in a structured format, such as JSON or CSV.

Azure AI Document Intelligence is a game-changer for organizations looking to automate document processing. It can extract data from a wide range of document types, revolutionizing how businesses handle document processing.

With its ability to scale and integrate into existing workflows, Azure AI Document Intelligence enables businesses to improve efficiency and reduce manual errors. This is especially useful for industries like finance and healthcare, where accuracy and speed are crucial.

The applications of Azure AI Document Intelligence are vast, providing immediate business value across various sectors. From legal and retail to finance and healthcare, this technology can unlock valuable insights from data.

By adopting AI-driven solutions for document processing, organizations can gain a competitive edge. As more businesses adopt this technology, the benefits will only continue to grow.