Overview
Document AI is a document understanding solution that takes unstructured data (e.g. documents, emails, invoices, forms, etc.) and makes the data easier to understand, analyze, and consume. The API provides structure through content classification, entity extraction, advanced searching, and more. With Document AI Workbench, you can achieve higher document processing accuracy by creating fully customized models using your own training data.
You can create Custom Document Extractors (CDE) that are specifically suited to your documents, and trained and evaluated with your data. This processor identifies and extracts entities from your documents. You can then use this trained processor on additional documents. You typically would use a CDE on documents that are all of one type, such as your institution's enrollment forms.
In this lab, you will learn how to use Document AI Workbench to create and train a Custom Document Extractor that processes W-2 (US tax form) documents. Most of the document preparation work has been done so that you can focus on the other mechanics of creating a CDE.
Objectives
In this lab, you will learn how to perform the following tasks:
Create a Custom Document Extractor in Document AI Workbench
Define and create the processor schema
Import documents
Annotate documents manually in Document AI Workbench
Use generative AI to auto-label documents
Kick off a training job for the processor
Setup and requirements
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.
This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
- Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.
- Time to complete the lab—remember, once you start, you cannot pause a lab.
Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.
How to start your lab and sign in to the Google Cloud console
Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:
The Open Google Cloud console button
Time remaining
The temporary credentials that you must use for this lab
Other information, if needed, to step through this lab
Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
Note: If you see the Choose an account dialog, click Use Another Account.
If necessary, copy the Username below and paste it into the Sign in dialog.
student-00-b132012bdd59@qwiklabs.net
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You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
f1iWnFssPEc1
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You can also find the Password in the Lab Details pane.
Click Next.
Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials.
Note: Using your own Google Cloud account for this lab may incur extra charges.
Click through the subsequent pages:
Accept the terms and conditions.
Do not add recovery options or two-factor authentication (because this is a temporary account).
Do not sign up for free trials.
After a few moments, the Google Cloud console opens in this tab.
Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.
Activate Cloud Shell
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
Click Activate Cloud Shell at the top of the Google Cloud console.
Click through the following windows:
When you are connected, you are already authenticated, and the project is set to your Project_ID, qwiklabs-gcp-04-9ae3e0a47584. The output contains a line that declares the Project_ID for this session:
Your Cloud Platform project in this session is set to qwiklabs-gcp-04-9ae3e0a47584
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
- (Optional) You can list the active account name with this command:
gcloud auth list
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- Click Authorize.
Output:
ACTIVE: *
ACCOUNT: student-00-b132012bdd59@qwiklabs.net
To set the active account, run:
$ gcloud config set account `ACCOUNT`
- (Optional) You can list the project ID with this command:
gcloud config list project
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Output:
[core]
project = qwiklabs-gcp-04-9ae3e0a47584
Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.
Task 1. Enable the Document AI API
Before you can begin using Document AI, you must enable the API.
- In Cloud Shell, run the following command to enable the API for Document AI.
gcloud services enable documentai.googleapis.com
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You should see something like this:
Operation "operations/..." finished successfully.
- Run the following command to install the Python client libraries for Document AI.
pip3 install --upgrade google-cloud-documentai
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You should see something like this:
...
Installing collected packages: google-cloud-documentai
Successfully installed google-cloud-documentai-2.15.0
Now, you're ready to use the Document AI API!
Enable the Document AI API
Task 2. Create a processor
You must first create an Custom Document Extractor processor to use for this lab.
You must first create a Form Parser processor instance to use in the Document AI Platform for this tutorial.
- From the Navigation menu select View all products. Under Artificial Intelligence, select Document AI.
Click Create Custom Processor.
Inside the Custom Extractor box, click Create Processor.
Give it the name lab-custom-extractor and select the region US (United States) on the list.
Click Create to create your processor.
Click Check my progress to verify the objective.
Create a Processor
Task 3. Define processor fields
You are now on the Processor overview page of the processor you just created.
You can specify the fields you want the processor to extract and begin labeling documents.
Click on the Get started tab. The Fields menu appears.
Click Create New Field.
Enter the name for the field. Select the Data type and the Occurrence. Click Create. Refer to Define processor schema for detailed instructions on creating and editing a schema.
Create each of the following labels for the processor schema.
| Name | Data Type | Occurrence |
control_number | Number | Optional multiple |
employees_social_security_number | Number | Required multiple |
employer_identification_number | Number | Required multiple |
employers_name_address_and_zip_code | Address | Required multiple |
federal_income_tax_withheld | Money | Required multiple |
social_security_tax_withheld | Money | Required multiple |
social_security_wages | Money | Required multiple |
wages_tips_other_compensation | Money | Required multiple |
You can also create and use other types of labels in your processor schema, such as checkboxes and tabular entities. For example, the W-2 forms contain a Statutory employee, Retirement plan, and Third party sick pay check boxes that you could also add to the schema.
Click Check my progress to verify the objective.
Create Labels
Task 4. Upload a sample document
Next, you upload a sample W-2 PDF and label it.
Click Upload Sample Document.
In the sidebar, click Import documents from Google Cloud Storage.
For this example, enter this bucket name in Source path. This links directly to one document.
cloud-samples-data/documentai/Custom/W2/PDF/W2_XL_input_clean_2950.pdf
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- Click Import.
You are redirected to the labeling console.
Task 5. Label a document
The process of selecting text in a document, and applying labels is known as annotation.
- When you're at the labeling console, notice that many of the labels are already populated.
Note: Your results might look slightly different than the sample image.
To use the suggested labels, hold the pointer over each label in the side panel, and click on the check mark to confirm the label is correct. You can edit the values if they do not match the document text.
In this example, the values at the bottom of the document were not identified automatically, so you will need to label them manually.
Use the Bounding box tool by default, or the Select text tool for multi-line values, to select the content and apply the label.
Note: The Select text tool does not work for all text values, so use the Bounding box if appropriate. You can also select non-text fields such as checkboxes using the Bounding box tool.
- In this example, the value of
wages_tips_other_compensation was selected with the Bounding box tool, and that label is applied.
- Review the detected text values to ensure that they reflect the correct text from the document.
The labeled W-2 document should look like this when complete:
If needed, you can click Create New Field to add a new field to the schema from this page.
Click Mark as Labeled when you have finished annotating the document.
You are redirected to the Get started tab.
Task 6. Build processor version using foundation model
After labeling a single document, you can create a processor version using the pretrained foundation model to extract entities.
- Click on the Build tab.
Under Call foundation model, click Create New Version.
Enter a name for your processor version, such as w2-foundation-model.
Click Create. It takes a few minutes to create.
Note: Once you create a processor version, you cannot delete fields you have created. You can disable them on the fields page if you no longer need them.
- Optional: Click on the Deploy & Use tab. On this page, you can view the available processor versions and the deployment status of the new version.
You test and evaluate this version later in the lab.
Click Check my progress to verify the objective.
Build processor version using foundation model
Task 7. Use generative AI to auto-label documents
The foundation model can accurately extract fields for a variety of document types, but you can also provide additional training data to improve the accuracy of the model for specific document structures.
Document AI Workbench uses the label names you define and previous annotations to make it quicker and easier to label documents at scale with auto-labeling.
Go to the Build page.
Click Import Documents.
In the sidebar, click Import documents from Google Cloud Storage.
Enter this bucket name in Source path. This contains unlabeled W-2 PDF files.
cloud-samples-data/documentai/Custom/W2/AutoLabel
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From the Data split list, select Auto-split. This automatically splits the documents to have 80% in the training set and 20% in the test set.
In the Auto-labeling section, select the Import with auto-labeling checkbox.
Select the foundation model processor version you just created to label the documents.
Click Import and wait for the documents to import. You can leave this page and return later.
You must verify the auto-labeled documents before you can use them for training or testing. Click Start Labeling to view the auto-labeled documents.
To use the suggested labels, hold the pointer over each annotation, and click on the check mark to confirm the label is correct. You can edit the values if they do not match the document text.
Click Mark as Labeled when you have finished annotating the document.
Repeat for each auto-labeled document. For this tutorial, you can skip any documents that were not successfully auto-labeled.
Task 8. Import prelabeled training documents
In this lab, you are provided with prelabeled data. If working on your own project, you have to determine how to label your data. Refer to Labeling options for more details. In general, more training data produces higher accuracy.
Go to the Build page.
Click Import Documents.
In the sidebar, click Import documents from Google Cloud Storage.
Enter the following path in Source path. This bucket contains prelabeled documents in the Document JSON format.
cloud-samples-data/documentai/Custom/W2/JSON-2
From the Data split list, select Auto-split. This automatically splits the documents to have 80% in the training set, and 20% in the test set. Leave Import with auto-labeling unchecked.
Click Import. Import takes several minutes.
(Optional) From the Build page, you can access the Manage Dataset console to view and edit all documents and labels in the dataset.
Task 9. Train the processor
Now that you have sufficient training and test data, you can train the processor. Because training might take several hours, make sure you have set up the processor with the appropriate data and labels before you begin training.
Under Train a custom model, click Create New Version.
If Create New Version cannot be clicked, click on View Full Requirements for information about the dataset requirements.
In the Version name field, enter a name for this processor version, such as w2-custom-model.
(Optional) Click View Label Stats to find information about the document labels. That can help determine your coverage. Click Close to return to the training setup.
Under Model training method, select Model based.
Click Start training.
(Optional) Click on the Deploy & Use tab. On this page, you can view the available processor versions and the training status of the new version.
Click Check my progress to verify the objective.
Train the model
Great! You have now started training your first Custom Document AI Processor. Since the training job will take around a few hours, the lab will end here. If you are interested in learning about how to deploy and test the model version, you can check out the following section in the documentation.
Solution of Lab
Quick
Step 1 – Cloud Shell Setup (Run Script)
curl -LO raw.githubusercontent.com/ePlus-DEV/storage/refs/heads/main/labs/GSP1142/lab.sh
source lab.sh
Script Alternative
curl -LO raw.githubusercontent.com/kamitabh24/GSP/refs/heads/main/docai_master.sh
chmod +x docai_master.sh
./docai_master.sh
Step 2 – Create Processor
Go to Google Cloud Console → Navigation Menu → Document AI → Workbench
Click Create Processor → Custom Extractor
Name: lab-custom-extractor
Location: us
Click Create
Step 3 – Define Schema Fields
Add schema fields one by one:
| Name | Data Type | Occurrence |
| control_number | Number | Optional multiple |
| employees_social_security_number | Number | Required multiple |
| employer_identification_number | Number | Required multiple |
| employers_name_address_and_zip_code | Address | Required multiple |
| federal_income_tax_withheld | Money | Required multiple |
| social_security_tax_withheld | Money | Required multiple |
| social_security_wages | Money | Required multiple |
| wages_tips_other_compensation | Money | Required multiple |
Step 4 – Upload & Label Sample
Go to Processor → Get Started tab → Upload Sample Document
In the labeling console:
Click Mark as Labeled
Step 5 – Build Foundation Model Version
Go to Processor → Build tab → Call foundation model → Create New Version
Name: w2-foundation-model
Wait a few minutes → Processor version ready
Step 6 – Import AutoLabel Data
Processor → Build → Import Documents
Source path: cloud-samples-data/documentai/Custom/W2/AutoLabel
Data split: Auto-split (80% training, 20% test)
Check ✅ Import with auto-labeling
Select the w2-foundation-model version
Import → Verify auto-labels → Click Mark as Labeled
Step 7 – Import Prelabeled JSON Data
Processor → Build → Import Documents
Source path:
cloud-samples-data/documentai/Custom/W2/JSON-2
Data split: Auto-split
Import → Data already labeled ✅
Step 8 – Train Custom Processor
Processor → Build → Train a Custom Model → Create New Version
Name: w2-custom-model
Training method: Model based
Click Start Training (may take a few hours ⏳)
Monitor progress → Deploy & Use tab
Manual
