Create a Custom Document Extractor in the Google Cloud console

Create a Custom Document Extractor in the Google Cloud console

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.

This guide describes 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.

The dataset used in these examples comes from Kaggle with a CC0: Public Domain License.

A typical workflow to create and use a CDE is as follows:

1. Create a Custom Document Extractor in Document AI Workbench.
2. Create a dataset using an empty Cloud Storage bucket.
3. Define and create the processor schema.
4. Import documents.
5. Assign documents to the Training and Test sets.
6. Annotate documents manually in Document AI Workbench or with Labeling Tasks.
7. Train the processor.
8. Evaluate the processor.
9. Deploy the processor.
10. Test the processor.
11. Configure Human-in-the-Loop (HITL) for review.
12. Use the processor on your documents.

You can make your own configuration choices that suit your workflow.

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To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:

Guide me

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Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project. Learn how to check if billing is enabled on a project.

4. Enable the Document AI, Cloud Storage APIs.

   Enable the APIs

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project. Learn how to check if billing is enabled on a project.

7. Enable the Document AI, Cloud Storage APIs.

   Enable the APIs

Create a processor

1. In the Google Cloud console, in the Document AI section, go to the Workbench page.

   Workbench

2. For Custom Document Extractor, click Create processor.

3. In the Create processor menu, enter a name for your processor, such as my-custom-document-extractor.

   

4. Select the region closest to you.

5. Click Create. The Processor Details tab appears.

Create a Cloud Storage bucket for the dataset

In order to train this new processor, you must create a dataset with training and testing data to help the processor identify the entities that you want to extract.

This dataset requires a new Cloud Storage bucket. Do not use the same bucket where your documents are currently stored.

1. Go to your processor's Train tab.

2. Click Set Dataset Location. You are prompted to select or create an empty Cloud Storage bucket or folder.

   

3. Click Browse to open Select folder.

4. Click the Create a new bucket icon and follow the prompts to create a new bucket. For more information on creating a Cloud Storage bucket, refer to Cloud Storage buckets.

   Note: A bucket is the top-level storage entity, in which you can nest folders. Instead of creating and selecting a bucket, you could also create and select an empty folder inside an existing bucket, if you prefer. Refer to Cloud Storage folders.

   After you create the bucket, the Select folder page appears for that bucket.

5. On the Select folder page for your bucket, click the Select button at the bottom of the dialog box.

   

6. Make sure the destination path is populated with the bucket name you selected. Click Create Dataset. The dataset might take up to several minutes to create.

   

Import documents into a dataset

Next, you will import your documents into your dataset.

1. On the Train tab, click Import documents.

2. For this example, enter this bucket name in Source path. This links directly to one document.

   cloud-samples-data/documentai/Custom/W2/PDF
   

3. For Data split, select Unassigned. The document in this folder will not be assigned to either the testing or training set. Leave Import with auto-labeling unchecked.

4. Click Import. Document AI reads the documents from the bucket into the dataset. It does not modify the import bucket or read from the bucket after the import is complete.

When you import documents, you can optionally assign the documents to the Training or Test set when imported, or wait to assign them later.

If you want to delete a document or documents that you have imported, select them on the Train tab, and click Delete.

For more information about preparing your data for import, refer to the Data preparation guide.

Define processor schema

You can create the processor schema either before or after you import documents into your dataset. The schema provides labels that you will use to annotate documents.

1. On the Train tab, click Edit Schema in the lower left. The Manage labels page opens.

2. Click Create label.

3. Enter the name for the label. Select the Data type and the Occurrence. Click Create. Refer to Define processor schema for detailed instructions on creating and editing a schema.

   Note: Labels cannot be deleted. Instead, you can disable any label you do not want to use.

4. Create each of the following labels for the processor schema.

   Name	Data Type	Occurrence
   CONTROL_NUMBER	Number	Required multiple
   EMPL_SSN	Plain Text	Required multiple
   EMPLR_ID_NUMBER	Plain Text	Required multiple
   EMPLR_NAME_ADDRESS	Address	Required multiple
   FEDERAL_INCOME_TAX_WH	Money	Required multiple
   SS_TAX_WH	Money	Required multiple
   SS_WAGES	Money	Required multiple
   WAGES_TIPS_OTHER_COMP	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.

5. Click Save when the labels are complete.

   

Label a document

The process of selecting text in a document, and applying labels is known as annotation.

1. Return to the Train tab, and click a document to open the Label management console.

2. Next, you will click on the schema label in the left hand panel that corresponds to the value you want to annotate, and apply the label.

3. 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.

4. In this example, the value of WAGES_TIPS_OTHER_COMP was selected with the Bounding box tool, and that label is applied.

   

   

5. 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:

   

6. Click Mark as Labeled when you have finished annotating the document.

   On the Train tab, the left-hand panel shows that 1 document has been labeled.

Assign annotated document to the training set

Now that you have labeled this example document, you can assign it to the training set.

1. On the Train tab, select the Select All checkbox.

2. From the Assign to Set list, select Training.

The left-hand panel shows that 1 document has been assigned to the training set.

Import pre-labeled data to the training and test sets

In this guide, you are provided with pre-labeled data.

If working on your own project, you will have to determine how to label your data. Refer to Labeling options. Document AI Custom Processors require a minimum of 10 documents in both the training and test sets, along with 10 instances of each label in each set. We recommend that you have at least 50 documents in each set, with 50 instances of each label for best performance. In general, more training data produces higher accuracy.

1. Click Import documents.

2. Enter the following path in Source path. This bucket contains pre-labeled documents in the Document JSON format.

   cloud-samples-data/documentai/Custom/W2/JSON
   

3. 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.

4. Click Import. The import might take several minutes to complete.

When the import is finished, the documents will appear on the Train tab.

Train the processor

Now that you have imported the 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.

1. Click Train New Version.

2. In the Version name field, enter a name for this processor version, such as my-cde-version-1.

3. (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.

4. Click Start training You can check the status on the right-hand panel.

Deploy the processor version

1. After training is complete, navigate to the Manage Versions tab. You can view details about the version you just trained.

2. Click the three vertical dots on the right of the version you want to deploy, and select Deploy version.

3. Select Deploy from the popup window.

   Deployment takes a few minutes to complete.

Evaluate and test the processor

1. After deployment is complete, navigate to the Evaluate & Test tab.

   On this page, you can view evaluation metrics including the F1 score, Precision and Recall for the full document, and individual labels. For more information about evaluation and statistics, refer to Evaluate processor.

2. Download a document that has not been involved in previous training or testing so that you can use it to evaluate the processor version. If using your own data, you would use a document set aside for this purpose.

   Download PDF

3. Click Upload Test Document and select the document you just downloaded.

   The Custom Document Extractor analysis page opens. The screen output will demonstrate how well the document was classified.

   You can also re-run the evaluation against a different test set or processor version.

Optional: Auto-label newly imported documents

After deploying a trained processor version, you can use Auto-labeling to save time on labeling when importing new documents.

1. On the Train page, Import documents.

2. Copy and paste the following Cloud Storage path. This directory contains 5 unlabeled W-2 PDFs. From the Data split dropdown list, select Training.

   cloud-samples-data/documentai/Custom/W2/AutoLabel
   

3. In the Auto-labeling section, select the Import with auto-labeling checkbox.

4. Select an existing processor version to label the documents.

   • For example: 2af620b2fd4d1fcf

5. Click Import and wait for the documents to import. You can leave this page and return later.

   • When complete, the documents appear in the Train page in the Auto-labeled section.

6. You cannot use auto-labeled documents for training or testing without marking them as labeled. Go to the Auto-labeled section to view the auto-labeled documents.

7. Select the first document to enter the labeling console.

8. Verify the label to ensure it is correct. Adjust if it is incorrect.

9. Select Mark as Labeled when finished.

10. Repeat the label verification for each auto-labeled document, then return to the Train page to use the data for training.

Use the processor

You have successfully created and trained a Custom Document Extractor processor.

You can manage your custom-trained processor versions just like any other processor version. For more information, refer to Managing processor versions.

You can Send a processing request to your custom processor, and the response can be handled the same as other entity extraction processors.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

To avoid unnecessary Google Cloud charges, use the Google Cloud console to delete your processor and project if you do not need them.

If you created a new project to learn about Document AI and you no longer need the project, delete the project.

If you used an existing Google Cloud project, delete the resources you created to avoid incurring charges to your account:

1. In the Google Cloud console navigation menu, click Document AI and select My Processors.

2. Click More actions in the same row as the processor you want to delete.

3. Click Delete processor, type the processor name, then click Delete again to confirm.

What's next

• Creating and using a Custom Document Classifier
• Creating and using a Custom Document Splitter