Create a Custom Document Splitter in the Google Cloud console

You can create Custom Document Splitters (CDS) that are specifically suited to your documents, and trained and evaluated with your data. This processor identifies classes of documents from a user-defined set of classes. You can then use this trained processor on additional documents. You typically would use a CDS on documents that are different types, then use the identification to pass the documents to an extraction processor to extract the entities.

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

  1. Create a Custom Document Splitter 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.

This guide describes how to use Document AI Workbench to create and train a Custom Document Splitter that splits and classifies procurement documents. Most of the document preparation work has been done so that you can focus on the other mechanics of creating a CDS.

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

Guide me

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.

    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.

    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.


  2. For Custom Document Splitter, click Create processor. Select CDS processor

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

    Create CDS processor

  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 documents that you want to split and classify.

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.

    Create a bucket

  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.

    Select bucket

  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.

    Create dataset

Import documents into a dataset

Next, you will import your documents into your dataset.

  1. On the Train tab, click Import documents. Import documents

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

  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. 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
    invoice Document type
    receipt Document type
    other Document type
  5. Click Save when the labels are complete.

    Manage labels console

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. In the Document type dropdown, select the appropriate label for the document.

  3. If you're using the sample document provided, select invoice.

    The labeled document should look like this when complete: Labeled invoice document

  4. 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.

In the left-hand panel, you can find 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.

  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. Ignore the Apply labels section.

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

When the import is finished, you will find the documents 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-cds-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 Splitter analysis page opens. The screen output will demonstrate how well the document was split and classified.

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

Use the processor

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

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

Once deployed, you can Send a processing request to your custom processor, and the response can be handled the same as other splitter 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.

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