Azure Form Recognizer client library for Python Azure SDK for Python 2.0.0 documentation

Posted by Janel Helmers on Wednesday, June 12, 2024

Azure Cognitive Services Form Recognizer is a cloud service that uses machine learning to recognize text and table data from form documents. It includes the following main functionalities:

  • Custom models - Recognize field values and table data from forms. These models are trained with your own data, so they’re tailored to your forms. You can then take these custom models and recognize forms. You can also manage the custom models you’ve created and see how close you are to the limit of custom models your account can hold.

  • Content API - Recognize text and table structures, along with their bounding box coordinates, from documents. Corresponds to the REST service’s Layout API.

  • Prebuilt receipt model - Recognize data from USA sales receipts using a prebuilt model.

Source code | Package (PyPI) | API reference documentation| Product documentation | Samples

Getting started

Prerequisites

Install the package

Install the Azure Form Recognizer client library for Python with pip:

pip install azure-ai-formrecognizer

Create a Form Recognizer resource

Form Recognizer supports both multi-service and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Form Recognizer access only, create a Form Recognizer resource.

You can create the resource using

Option 1: Azure Portal

Option 2: Azure CLI. Below is an example of how you can create a Form Recognizer resource using the CLI:

# Create a new resource group to hold the form recognizer resource - # if using an existing resource group, skip this step az group create --name my-resource-group --location westus2 
# Create form recognizer az cognitiveservices account create \ --name form-recognizer-resource \ --resource-group my-resource-group \ --kind FormRecognizer \ --sku F0 \ --location westus2 \ --yes

Authenticate the client

Looking up the endpoint

You can find the endpoint for your form recognizer resource using the Azure Portal or Azure CLI:

# Get the endpoint for the form recognizer resource az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "endpoint"

Types of credentials

The credential parameter may be provided as a AzureKeyCredential from azure.core. See the full details regarding authentication of cognitive services.

To use an API key, pass the key as a string into an instance of AzureKeyCredential("<api_key>"). The API key can be found in the Azure Portal or by running the following Azure CLI command:

az cognitiveservices account keys list --name "resource-name" --resource-group "resource-group-name"

Use the key as the credential parameter to authenticate the client:

from azure.ai.formrecognizer import FormRecognizerClient from azure.core.credentials import AzureKeyCredential endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_recognizer_client = FormRecognizerClient(endpoint, credential)

Key concepts

FormRecognizerClient

FormRecognizerClient provides operations for:

  • Recognizing form fields and content using custom models trained to recognize your custom forms. These values are returned in a collection of RecognizedForm objects.

  • Recognizing form content, including tables, lines and words, without the need to train a model. Form content is returned in a collection of FormPage objects.

  • Recognizing common fields from US receipts, using a pre-trained receipt model on the Form Recognizer service. These fields and meta-data are returned in a collection of USReceipt objects.

FormTrainingClient

FormTrainingClient provides operations for:

  • Training custom models to recognize all fields and values found in your custom forms. A CustomFormModel is returned indicating the form types the model will recognize, and the fields it will extract for each form type. See the service’s documents for a more detailed explanation.

  • Training custom models to recognize specific fields and values you specify by labeling your custom forms. A CustomFormModel is returned indicating the fields the model will extract, as well as the estimated accuracy for each field. See the service’s documents for a more detailed explanation.

  • Managing models created in your account.

Please note that models can also be trained using a graphical user interface such as the Form Recognizer Labeling Tool.

Long-Running Operations

Long-running operations are operations which consist of an initial request sent to the service to start an operation, followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has succeeded, to get the result.

Methods that train models or recognize values from forms are modeled as long-running operations. The client exposes a begin_<method-name> method that returns an LROPoller. Callers should wait for the operation to complete by calling result() on the operation returned from the begin_<method-name> method. Sample code snippets are provided to illustrate using long-running operations below.

Examples

The following section provides several code snippets covering some of the most common Form Recognizer tasks, including:

Recognize Forms Using a Custom Model

Recognize name/value pairs and table data from forms. These models are trained with your own data, so they’re tailored to your forms. You should only recognize forms of the same form type that the custom model was trained on.

from azure.ai.formrecognizer import FormRecognizerClient from azure.core.credentials import AzureKeyCredential endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_recognizer_client = FormRecognizerClient(endpoint, credential) model_id = "<your custom model id>" # Make sure the form type is one of the types of forms your custom model can recognize with open("<path to your form>", "rb") as fd: form = fd.read() poller = form_recognizer_client.begin_recognize_custom_forms(model_id=model_id, stream=form) result = poller.result() for recognized_form in result: print("Form type ID: {}".format(recognized_form.form_type)) for label, field in recognized_form.fields.items(): print("Field '{}' has value '{}' with a confidence score of {}".format( label, field.value, field.confidence ))

Recognize Content

Recognize text and table structures, along with their bounding box coordinates, from documents.

from azure.ai.formrecognizer import FormRecognizerClient from azure.core.credentials import AzureKeyCredential endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_recognizer_client = FormRecognizerClient(endpoint, credential) with open("<path to your form>", "rb") as fd: form = fd.read() poller = form_recognizer_client.begin_recognize_content(form) page = poller.result() table = page[0].tables[0] # page 1, table 1 for cell in table.cells: print(cell.text) print(cell.bounding_box) print(cell.confidence)

Recognize Receipts

Recognize data from USA sales receipts using a prebuilt model.

from azure.ai.formrecognizer import FormRecognizerClient from azure.core.credentials import AzureKeyCredential endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_recognizer_client = FormRecognizerClient(endpoint, credential) with open("<path to your receipt>", "rb") as fd: receipt = fd.read() poller = form_recognizer_client.begin_recognize_receipts(receipt) result = poller.result() r = result[0] print("Receipt contained the following values with confidences: ") print("Receipt Type: {} has confidence: {}".format(r.receipt_type.type, r.receipt_type.confidence)) print("Merchant Name: {} has confidence: {}".format(r.merchant_name.value, r.merchant_name.confidence)) print("Transaction Date: {} has confidence: {}".format(r.transaction_date.value, r.transaction_date.confidence)) print("Receipt items:") for item in r.receipt_items: print("...Item Name: {} has confidence: {}".format(item.name.value, item.name.confidence)) print("...Item Quantity: {} has confidence: {}".format(item.quantity.value, item.quantity.confidence)) print("...Individual Item Price: {} has confidence: {}".format(item.price.value, item.price.confidence)) print("...Total Item Price: {} has confidence: {}".format(item.total_price.value, item.total_price.confidence)) print("Subtotal: {} has confidence: {}".format(r.subtotal.value, r.subtotal.confidence)) print("Tax: {} has confidence: {}".format(r.tax.value, r.tax.confidence)) print("Tip: {} has confidence: {}".format(r.tip.value, r.tip.confidence)) print("Total: {} has confidence: {}".format(r.total.value, r.total.confidence))

Train a model

Train a machine-learned model on your own form type. The resulting model will be able to recognize values from the types of forms it was trained on. Provide a container SAS url to your Azure Storage Blob container where you’re storing the training documents. See details on setting this up in the service quickstart documentation.

from azure.ai.formrecognizer import FormTrainingClient from azure.core.credentials import AzureKeyCredential endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_training_client = FormTrainingClient(endpoint, credential) container_sas_url = "xxx" # training documents uploaded to blob storage poller = form_training_client.begin_train_model(container_sas_url) model = poller.result() # Custom model information print("Model ID: {}".format(model.model_id)) print("Status: {}".format(model.status)) print("Created on: {}".format(model.created_on)) print("Last modified: {}".format(model.last_modified)) print("Recognized fields:") # looping through the submodels, which contains the fields they were trained on for submodel in model.models: print("We have recognized the following fields: {}".format( ", ".join([label for label in submodel.fields]) )) # Training result information for doc in model.training_documents: print("Document name: {}".format(doc.document_name)) print("Document status: {}".format(doc.status)) print("Document page count: {}".format(doc.page_count)) print("Document errors: {}".format(doc.errors))

Manage Your Models

Manage the custom models attached to your account.

from azure.ai.formrecognizer import FormTrainingClient from azure.core.credentials import AzureKeyCredential from azure.core.exceptions import ResourceNotFoundError endpoint = "https://<region>.api.cognitive.microsoft.com/" credential = AzureKeyCredential("<api_key>") form_training_client = FormTrainingClient(endpoint, credential) account_properties = form_training_client.get_account_properties() print("Our account has {} custom models, and we can have at most {} custom models".format( account_properties.custom_model_count, account_properties.custom_model_limit )) # Here we get a paged list of all of our custom models custom_models = form_training_client.list_model_infos() print("We have models with the following ids: {}".format( ", ".join([m.model_id for m in custom_models]) )) # Now we get the custom model from the "Train a model" sample model_id = "<model id from the Train a Model sample>" custom_model = form_training_client.get_custom_model(model_id=model_id) print("Model ID: {}".format(custom_model.model_id)) print("Status: {}".format(custom_model.status)) print("Created on: {}".format(custom_model.created_on)) print("Last modified: {}".format(custom_model.last_modified)) # Finally, we will delete this model by ID form_training_client.delete_model(model_id=custom_model.model_id) try: form_training_client.get_custom_model(model_id=custom_model.model_id) except ResourceNotFoundError: print("Successfully deleted model with id {}".format(custom_model.model_id))

Optional Configuration

Optional keyword arguments can be passed in at the client and per-operation level. The azure-core reference documentation describes available configurations for retries, logging, transport protocols, and more.

Troubleshooting

General

Form Recognizer client library will raise exceptions defined in Azure Core.

Logging

This library uses the standard logging library for logging. Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Detailed DEBUG level logging, including request/response bodies and unredacted headers, can be enabled on a client with the logging_enable keyword argument:

import sys import logging from azure.ai.formrecognizer import FormRecognizerClient from azure.core.credentials import AzureKeyCredential # Create a logger for the 'azure' SDK logger = logging.getLogger('azure') logger.setLevel(logging.DEBUG) # Configure a console output handler = logging.StreamHandler(stream=sys.stdout) logger.addHandler(handler) endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/" credential = AzureKeyCredential("<api_key>") # This client will log detailed information about its HTTP sessions, at DEBUG level form_recognizer_client = FormRecognizerClient(endpoint, credential, logging_enable=True)

Similarly, logging_enable can enable detailed logging for a single operation, even when it isn’t enabled for the client:

poller = form_recognizer_client.begin_recognize_receipts(receipt, logging_enable=True)

Next steps

The following section provides several code snippets illustrating common patterns used in the Form Recognizer Python API.

More sample code

These code samples show common scenario operations with the Azure Form Recognizer client library. The async versions of the samples (the python sample files appended with _async) show asynchronous operations with Form Recognizer and require Python 3.5 or later.

Additional documentation

For more extensive documentation on Azure Cognitive Services Form Recognizer, see the Form Recognizer documentation on docs.microsoft.com.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Indices and tables

Developer Documentation

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