Getting Started with Azure Forms and Form Recognizer

Getting started with Azure Forms and Form Recognizer is easier than you think. You can start by creating a new Azure Form Recognizer resource in the Azure portal.

To create a Form Recognizer resource, you need to have an Azure subscription. If you don't have one, you can sign up for a free trial or use your existing subscription.

With your Form Recognizer resource set up, you can start building and training your first form model. This involves uploading a sample form and configuring the model's settings.

Form Recognizer supports various types of forms, including PDFs, images, and even handwritten forms.

First, you'll need to install the Azure Form Recognizer client library for Python using pip.

This version of the client library defaults to the 2023-07-31 version of the service.

To determine the correct version of the client library to install, refer to this table:

The client library versions are closely tied to the supported API versions of the service.

Note that starting with version 3.2.X, a new set of clients were introduced to leverage the newest features of the Document Intelligence service.

Azure Form Recognizer is a cloud service that uses machine learning to analyze text and structured data from documents.

It can detect multiple languages in a document, recognizing over 160 languages of printed text and a few languages of handwritten text.

The service can extract data from various types of documents, including receipts, invoices, IDs, and even generic documents, providing results in a JSON formatted output.

You can build a custom model to process your specific set of forms, allowing for very intelligent output using pre-defined keys in the key-value pairs.

Here are the types of models provided by Form Recognizer:

You can work with Form Recognizer across all major platforms, including Windows, MacOS, Linux, Docker, and JavaScript on all browsers. This flexibility makes it easy to integrate into your development workflow.

Developers can use either the REST API interface or the SDK to interact with Form Recognizer. The implementation process is straightforward, with two main steps: capturing the form as an image or PDF and converting it to Base64, and then posting it to the Form Recognizer endpoint.

The client application will then poll the endpoint to check for results, receiving a response with a status of "running" until the processing is complete and the results are returned in the response payload. This process typically takes around 5-8 seconds, making it suitable for batch processing or backend processing.

You can make the POST and GET calls using either the SDK interface or the REST API interface, both of which take the model name as a parameter. For custom models, you need to create the model first and use the custom model name in the calls.

Form Recognizer is a cloud service that uses machine learning to analyze text and structured data from documents. It provides the extracted information with proper context in the form of JSON formatted results.

Form Recognizer can detect multiple languages in a document, recognizing over 160 languages of printed text and a few languages of handwritten text. This includes languages like English, Japanese, Chinese Simplified, Korean, French, Portuguese, German, Spanish, and Italian.

The service can recognize and automatically extract data from various types of documents, including receipts, invoices, and IDs. It can also extract data from any document that cannot be categorized and provide results in a generic format.

Form Recognizer allows you to build a custom model to process your specific set of forms. This is done using the Form Recognizer Studio, which provides an excellent environment to create and test models before employing them into your development cycle.

Here are the different types of models provided by Form Recognizer:

Form Recognizer can return results in about 5-8 seconds, making it very appropriate for batch processing or backend processing. However, if you expect to use it for real-time or near real-time processing, you may need to look at the entire workflow and come up with alternate mechanisms to keep the user engaged.

Azure Form Recognizer can detect multiple languages in a document, recognizing over 160 languages of printed text and a few languages of handwritten text.

It can recognize and automatically extract data from various types of documents, including receipts, invoices, and IDs, and even provide results in a generic format for documents that can't be categorized.

The Form Recognizer provides five types of models: Read OCR, Layout Analysis, General Document, Prebuilt, and Custom models.

Here are the different types of models provided by Form Recognizer:

The first three models provide low-level information in their output, while the last two options have a more intelligent output that makes it easier to consume the data.

Azure Forms offers a range of learning resources, including tutorials and documentation.

Microsoft provides a comprehensive tutorial on creating Azure Forms applications, which covers everything from setting up a new project to deploying it to the cloud.

You can find these tutorials on the Azure website.

The Azure Form Library offers a range of models to help you extract valuable information from forms. You can choose from four types of models: Read OCR, Layout Analysis, General Document, and Prebuilt models.

The first three models provide low-level information in their output, which may require significant processing to make the data ready for consumption. This can be a bit of a challenge, but it's worth it for the right applications.

Here are the four types of models in more detail:

Prebuilt models have a more intelligent output, making it easier to consume the data. Custom models, on the other hand, allow you to get very intelligent output using pre-defined keys in the key-value pairs.