Extract information using prebuilt models in Azure Form Recognizer

Extracting information from unstructured data such as forms, invoices, and receipts can be a time-consuming and error-prone task. However, with the help of prebuilt models in Azure Form Recognizer, you can quickly and accurately extract relevant information from these documents. In this article, we will explore how to leverage Azure Form Recognizer to extract information using prebuilt models.

Azure Form Recognizer

Azure Form Recognizer is a cloud-based service that uses machine learning technology to analyze and extract information from various documents. It offers both custom models, which can be trained on specific document layouts, and prebuilt models, which are trained on a wide range of document types and structures. In this article, we will focus on using the prebuilt models provided by Azure Form Recognizer.

Step 1: Install the Required Packages

To interact with Azure Form Recognizer in your application, you will need to install the azure-ai-formrecognizer package. You can install this package using pip:

pip install azure-ai-formrecognizer

Step 2: Authenticate and Create a Client Object

To authenticate and create a client object, you will need the endpoint URL and access key for your Form Recognizer resource. You can pass these credentials while creating the client object:

from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import FormRecognizerClient

credential = AzureKeyCredential(key)
client = FormRecognizerClient(endpoint=endpoint, credential=credential)

Step 3: Extract Information from a Document

To extract information from a document, you can simply call the begin_recognize_receipts or begin_recognize_invoices method on the client object. These methods analyze the document and return the extracted information in a structured format:

with open("", "rb") as document:
result = client.begin_recognize_receipts(document).result()

for recognized_receipt in result:
for receipt_item in recognized_receipt.fields.values():
print("Receipt Item:")
print("Name:", receipt_item.value)
print("Value:", receipt_item.value_data.text)
print("Confidence:", receipt_item.confidence)

In the code snippet above, we open the document file in binary mode and call the begin_recognize_receipts method. We then iterate over the recognized receipts and their fields to access the extracted information.

Step 4: Handle the Extracted Information

The extracted information is returned as a dictionary of fields, where each field has a name, value, confidence score, and other relevant metadata. You can access this information and use it as per your requirements. For example, you can store it in a database, perform further processing, or use it to automate downstream workflows.

With the above steps in place, you can now leverage the prebuilt models in Azure Form Recognizer to extract information from various types of documents. It streamlines the information extraction process and saves valuable time and effort.

In conclusion, Azure Form Recognizer provides prebuilt models that make it easier to extract information from unstructured documents. By following the steps outlined in this article, you can quickly integrate Form Recognizer into your applications and automate the extraction of relevant information. This can be a game-changer for organizations dealing with a large volume of forms, invoices, and receipts.