I am only working on one sheet and need a formula to display a value only if another cell has data in it: If D44 is empty (or blank), then G44 will be blank. Otherwise I want to return the following value in G44: (F44*24)*D$1.
I'm not sure if I can use IF with ISBLANK....
Thank you!!
Form Recognizer is a powerful tool to help build a variety of document machine learning solutions. It is one service however its made up of many prebuilt models that can perform a variety of essential document functions. You can even custom train a model using supervised or unsupervised learning for tasks outside of the scope of the prebuilt models! Read more about all the features of Form Recognizer here. In this example we will be looking at how to use one of the prebuilt models in the Form Recognizer service that can extract the data from a PDF document dataset. Our documents are invoices with common data fields so we are able to use the prebuilt model without having to build a customized model.
\n\n
Sample Invoice:
\n\n
\n
After we take a look at how to do this with Python and Azure Form Recognizer, we will take a look at how to do the same process with no code using the Power Platform services: Power Automate and Form Recognizer built into AI Builder. In the Power Automate flow we are scheduling a process to happen every day. What the process does is look in the raw blob container to see if there is new files to be processed. If there is new files to be processed it gets all blobs from the container and loops through each blob to extract the PDF data using a prebuilt AI builder step. Then it deletes the processed document from the raw container. See what it looks like below.
\n
Power Automate Flow:
\n\n
\n
First lets create the Form Recognizer Cognitive Service.
\nNow lets create a storage account to store the PDF dataset we will be using in containers. We want two containers, one for the processed PDFs and one for the raw unprocessed PDF.
processed, raw\n
Upload your dataset to the Azure Storage raw folder since they need to be processed. Once processed then they would get moved to the processed container.
\n
The result should look something like this:
\n\n
\n
\n
Now that we have our data stored in Azure Blob Storage we can connect and process the PDF forms to extract the data using the Form Recognizer Python SDK. You can also use the Python SDK with local data if you are not using Azure Storage. This example will assume you are using Azure Storage.
\nCreate a new Jupyter notebook in VS Code.
\nInstall the Python SDK
\n!pip install azure-ai-formrecognizer --pre\nimport os\nfrom azure.core.exceptions import ResourceNotFoundError\nfrom azure.ai.formrecognizer import FormRecognizerClient\nfrom azure.core.credentials import AzureKeyCredential\nimport os, uuid\nfrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, __version__\n\n
endpoint and key with the values from the service you created. These values can be found in the Azure Portal under the Form Recongizer service you created under the Keys and Endpoint on the navigation menu.endpoint = \"<your endpoint>\"\nkey = \"<your key>\"\nendpoint and key to connect to the service and create the FormRecongizerClientform_recognizer_client = FormRecognizerClient(endpoint, AzureKeyCredential(key))\nprint_results helper function for use later to print out the results of each invoice.def print_result(invoices, blob_name):\n for idx, invoice in enumerate(invoices):\n print(\"--------Recognizing invoice {}--------\".format(blob_name))\n vendor_name = invoice.fields.get(\"VendorName\")\n if vendor_name:\n print(\"Vendor Name: {} has confidence: {}\".format(vendor_name.value, vendor_name.confidence))\n vendor_address = invoice.fields.get(\"VendorAddress\")\n if vendor_address:\n print(\"Vendor Address: {} has confidence: {}\".format(vendor_address.value, vendor_address.confidence))\n customer_name = invoice.fields.get(\"CustomerName\")\n if customer_name:\n print(\"Customer Name: {} has confidence: {}\".format(customer_name.value, customer_name.confidence))\n customer_address = invoice.fields.get(\"CustomerAddress\")\n if customer_address:\n print(\"Customer Address: {} has confidence: {}\".format(customer_address.value, customer_address.confidence))\n customer_address_recipient = invoice.fields.get(\"CustomerAddressRecipient\")\n if customer_address_recipient:\n print(\"Customer Address Recipient: {} has confidence: {}\".format(customer_address_recipient.value, customer_address_recipient.confidence))\n invoice_id = invoice.fields.get(\"InvoiceId\")\n if invoice_id:\n print(\"Invoice Id: {} has confidence: {}\".format(invoice_id.value, invoice_id.confidence))\n invoice_date = invoice.fields.get(\"InvoiceDate\")\n if invoice_date:\n print(\"Invoice Date: {} has confidence: {}\".format(invoice_date.value, invoice_date.confidence))\n invoice_total = invoice.fields.get(\"InvoiceTotal\")\n if invoice_total:\n print(\"Invoice Total: {} has confidence: {}\".format(invoice_total.value, invoice_total.confidence))\n due_date = invoice.fields.get(\"DueDate\")\n if due_date:\n print(\"Due Date: {} has confidence: {}\".format(due_date.value, due_date.confidence))\n\n
raw and processed containers that we created earlier.# Create the BlobServiceClient object which will be used to get the container_client\nconnect_str = \"<Get connection string from the Azure Portal>\"\nblob_service_client = BlobServiceClient.from_connection_string(connect_str)\n\n# Container client for raw container.\nraw_container_client = blob_service_client.get_container_client(\"raw\")\n\n# Container client for processed container\nprocessed_container_client = blob_service_client.get_container_client(\"processed\")\n\n# Get base url for container.\ninvoiceUrlBase = raw_container_client.primary_endpoint\nprint(invoiceUrlBase)\nHINT: If you get a \"HttpResponseError: (InvalidImageURL) Image URL is badly formatted.\" error make sure the proper permissions to access the container are set. Learn more about Azure Storage Permissions here
\n\n
We are ready to process the blobs now! Here we will call list_blobs to get a list of blobs in the raw container. Then we will loop through each blob, call the begin_recognize_invoices_from_url to extract the data from the PDF. Then we have our helper method to print the results. Once we have extracted the data from the PDF we will upload_blob to the processed folder and delete_blob from the raw folder.
\n
print(\"\\nProcessing blobs...\")\n\nblob_list = raw_container_client.list_blobs()\nfor blob in blob_list:\n invoiceUrl = f'{invoiceUrlBase}/{blob.name}'\n print(invoiceUrl)\n poller = form_recognizer_client.begin_recognize_invoices_from_url(invoiceUrl)\n\n # Get results\n invoices = poller.result()\n\n # Print results\n print_result(invoices, blob.name)\n\n # Copy blob to processed\n processed_container_client.upload_blob(blob, blob.blob_type, overwrite=True)\n\n # Delete blob from raw now that its processed\n raw_container_client.delete_blob(blob)\nEach result should look similar to this for the above invoice example:
\n\n
\n
The prebuilt invoices model worked great for our invoices so we don't need to train a customized Form Recognizer model to improve our results. But what if we did and what if we didn't know how to code?! You can still leverage all this awesomeness in AI Builder with Power Automate without writing any code. We will take a look at this same example in Power Automate next.
\n\n
You can achieve these same results using no code with Form Recognizer in AI Builder with Power Automate. Lets take a look at how we can do that.
\n\n
Create then click Scheduled Cloud Flow. You can trigger Power Automate flows in a variety of ways so keep in mind that you may want to select a different trigger for your project.\n
New StepList blobs Step\nAzure Blob Storage and select List blobsCreate new connection if your storage account isn't already connected\nConnection Name, Azure Storage Account name (the account you created), and the Azure Storage Account Access Key (which you can find in the resource keys in the Azure Portal)Createraw.\n
Your flow should look something like this:
\n\n
\n
\n
\n
Control then Apply to eachvalue propertyadd action from within the Apply to each Flow step.Get blob content step:\nAzure Blob Storage and select Get blob contentPath property. This will get the File content that we will pass into the Form Recognizer.Process and save information from invoices step:\nadd new actionProcess and save information from invoicesFile Content from the Get blob content sectionCopy Blob step:\nAzure Blob Storage and select Copy BlobSource url text box and select the Path propertyDestination blob path and put /processed for the processed containerOverwrite? dropdown and select Yes if you want the copied blob to overwrite blobs with the existing name.Delete Blob step:\nAzure Blob Storage and select Delete BlobBlob text box and select the Path propertyThe Apply to each block should look something like this:
\n
This prebuilt model again worked great on our invoice data. However if you have a more complex dataset, use the AI Builder to label and create a customized machine learning model for your specific dataset. Read more about how to do that here.
\n\n
We went over a fraction of the things that you can do with Form Recognizer so don't let the learning stop here! Check out the below highlights of new Form Recognizer features that were just announced and the additional doc links to dive deeper into what we did here.
\nQuickstart: Use the Form Recognizer client library or REST API
\nTutorial: Create a form-processing app with AI Builder
\n\nAI Essentials video including Form Recognizer
","body@stringLength":"33445","rawBody":"Form Recognizer is a powerful tool to help build a variety of document machine learning solutions. It is one service however its made up of many prebuilt models that can perform a variety of essential document functions. You can even custom train a model using supervised or unsupervised learning for tasks outside of the scope of the prebuilt models! Read more about all the features of Form Recognizer here. In this example we will be looking at how to use one of the prebuilt models in the Form Recognizer service that can extract the data from a PDF document dataset. Our documents are invoices with common data fields so we are able to use the prebuilt model without having to build a customized model.
\n\n
Sample Invoice:
\n\n
After we take a look at how to do this with Python and Azure Form Recognizer, we will take a look at how to do the same process with no code using the Power Platform services: Power Automate and Form Recognizer built into AI Builder. In the Power Automate flow we are scheduling a process to happen every day. What the process does is look in the raw blob container to see if there is new files to be processed. If there is new files to be processed it gets all blobs from the container and loops through each blob to extract the PDF data using a prebuilt AI builder step. Then it deletes the processed document from the raw container. See what it looks like below.
\n
Power Automate Flow:
\n\n
First lets create the Form Recognizer Cognitive Service.
\nNow lets create a storage account to store the PDF dataset we will be using in containers. We want two containers, one for the processed PDFs and one for the raw unprocessed PDF.
processed, raw\n
Upload your dataset to the Azure Storage raw folder since they need to be processed. Once processed then they would get moved to the processed container.
\n
The result should look something like this:
\n\n
\n
Now that we have our data stored in Azure Blob Storage we can connect and process the PDF forms to extract the data using the Form Recognizer Python SDK. You can also use the Python SDK with local data if you are not using Azure Storage. This example will assume you are using Azure Storage.
\nCreate a new Jupyter notebook in VS Code.
\nInstall the Python SDK
\n!pip install azure-ai-formrecognizer --pre\nimport os\nfrom azure.core.exceptions import ResourceNotFoundError\nfrom azure.ai.formrecognizer import FormRecognizerClient\nfrom azure.core.credentials import AzureKeyCredential\nimport os, uuid\nfrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, __version__\n\n
endpoint and key with the values from the service you created. These values can be found in the Azure Portal under the Form Recongizer service you created under the Keys and Endpoint on the navigation menu.endpoint = \"<your endpoint>\"\nkey = \"<your key>\"\nendpoint and key to connect to the service and create the FormRecongizerClientform_recognizer_client = FormRecognizerClient(endpoint, AzureKeyCredential(key))\nprint_results helper function for use later to print out the results of each invoice.def print_result(invoices, blob_name):\n for idx, invoice in enumerate(invoices):\n print(\"--------Recognizing invoice {}--------\".format(blob_name))\n vendor_name = invoice.fields.get(\"VendorName\")\n if vendor_name:\n print(\"Vendor Name: {} has confidence: {}\".format(vendor_name.value, vendor_name.confidence))\n vendor_address = invoice.fields.get(\"VendorAddress\")\n if vendor_address:\n print(\"Vendor Address: {} has confidence: {}\".format(vendor_address.value, vendor_address.confidence))\n customer_name = invoice.fields.get(\"CustomerName\")\n if customer_name:\n print(\"Customer Name: {} has confidence: {}\".format(customer_name.value, customer_name.confidence))\n customer_address = invoice.fields.get(\"CustomerAddress\")\n if customer_address:\n print(\"Customer Address: {} has confidence: {}\".format(customer_address.value, customer_address.confidence))\n customer_address_recipient = invoice.fields.get(\"CustomerAddressRecipient\")\n if customer_address_recipient:\n print(\"Customer Address Recipient: {} has confidence: {}\".format(customer_address_recipient.value, customer_address_recipient.confidence))\n invoice_id = invoice.fields.get(\"InvoiceId\")\n if invoice_id:\n print(\"Invoice Id: {} has confidence: {}\".format(invoice_id.value, invoice_id.confidence))\n invoice_date = invoice.fields.get(\"InvoiceDate\")\n if invoice_date:\n print(\"Invoice Date: {} has confidence: {}\".format(invoice_date.value, invoice_date.confidence))\n invoice_total = invoice.fields.get(\"InvoiceTotal\")\n if invoice_total:\n print(\"Invoice Total: {} has confidence: {}\".format(invoice_total.value, invoice_total.confidence))\n due_date = invoice.fields.get(\"DueDate\")\n if due_date:\n print(\"Due Date: {} has confidence: {}\".format(due_date.value, due_date.confidence))\n\n
raw and processed containers that we created earlier.# Create the BlobServiceClient object which will be used to get the container_client\nconnect_str = \"<Get connection string from the Azure Portal>\"\nblob_service_client = BlobServiceClient.from_connection_string(connect_str)\n\n# Container client for raw container.\nraw_container_client = blob_service_client.get_container_client(\"raw\")\n\n# Container client for processed container\nprocessed_container_client = blob_service_client.get_container_client(\"processed\")\n\n# Get base url for container.\ninvoiceUrlBase = raw_container_client.primary_endpoint\nprint(invoiceUrlBase)\nHINT: If you get a \"HttpResponseError: (InvalidImageURL) Image URL is badly formatted.\" error make sure the proper permissions to access the container are set. Learn more about Azure Storage Permissions here
\n\n
We are ready to process the blobs now! Here we will call list_blobs to get a list of blobs in the raw container. Then we will loop through each blob, call the begin_recognize_invoices_from_url to extract the data from the PDF. Then we have our helper method to print the results. Once we have extracted the data from the PDF we will upload_blob to the processed folder and delete_blob from the raw folder.
\n
print(\"\\nProcessing blobs...\")\n\nblob_list = raw_container_client.list_blobs()\nfor blob in blob_list:\n invoiceUrl = f'{invoiceUrlBase}/{blob.name}'\n print(invoiceUrl)\n poller = form_recognizer_client.begin_recognize_invoices_from_url(invoiceUrl)\n\n # Get results\n invoices = poller.result()\n\n # Print results\n print_result(invoices, blob.name)\n\n # Copy blob to processed\n processed_container_client.upload_blob(blob, blob.blob_type, overwrite=True)\n\n # Delete blob from raw now that its processed\n raw_container_client.delete_blob(blob)\nEach result should look similar to this for the above invoice example:
\n\n
The prebuilt invoices model worked great for our invoices so we don't need to train a customized Form Recognizer model to improve our results. But what if we did and what if we didn't know how to code?! You can still leverage all this awesomeness in AI Builder with Power Automate without writing any code. We will take a look at this same example in Power Automate next.
\n\n
You can achieve these same results using no code with Form Recognizer in AI Builder with Power Automate. Lets take a look at how we can do that.
\n\n
Create then click Scheduled Cloud Flow. You can trigger Power Automate flows in a variety of ways so keep in mind that you may want to select a different trigger for your project.\n
New StepList blobs Step\nAzure Blob Storage and select List blobsCreate new connection if your storage account isn't already connected\nConnection Name, Azure Storage Account name (the account you created), and the Azure Storage Account Access Key (which you can find in the resource keys in the Azure Portal)Createraw.\n
Your flow should look something like this:
\n\n
\n
\n
Control then Apply to eachvalue propertyadd action from within the Apply to each Flow step.Get blob content step:\nAzure Blob Storage and select Get blob contentPath property. This will get the File content that we will pass into the Form Recognizer.Process and save information from invoices step:\nadd new actionProcess and save information from invoicesFile Content from the Get blob content sectionCopy Blob step:\nAzure Blob Storage and select Copy BlobSource url text box and select the Path propertyDestination blob path and put /processed for the processed containerOverwrite? dropdown and select Yes if you want the copied blob to overwrite blobs with the existing name.Delete Blob step:\nAzure Blob Storage and select Delete BlobBlob text box and select the Path propertyThe Apply to each block should look something like this:
This prebuilt model again worked great on our invoice data. However if you have a more complex dataset, use the AI Builder to label and create a customized machine learning model for your specific dataset. Read more about how to do that here.
\n\n
We went over a fraction of the things that you can do with Form Recognizer so don't let the learning stop here! Check out the below highlights of new Form Recognizer features that were just announced and the additional doc links to dive deeper into what we did here.
\nQuickstart: Use the Form Recognizer client library or REST API
\nTutorial: Create a form-processing app with AI Builder
\n\nAI Essentials video including Form Recognizer
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