Annotate documents

After successfully creating your project and uploading your documents to a specific document type, they are automatically pre-annotated. This is done using specialized models, based on the document type's schema. The schema clearly defines the fields you want to extract from a particular document type. To find the document type's schema, go to the Annotation page and check the Fields section.

Predictions are indicated with underlines on the text within the document and they can't be deleted. If they are incorrect and cannot be matched to a particular field, you can ignore them. During the training process, only confirmed fields are used for training, while the underlines are not taken into account.

As you continue to add more annotations, the prediction underlines should progressively align with your input. There may be a few inconsistencies between underlines and user-annotated fields at the beginning. However, as you make more annotations and the model improves, the underlines should line up more precisely with the user-supplied data.

In the following image, the Shipping Address has been incorrectly predicted to include the person's name.

To fix this, you only need to confirm the Shipping Address. It's not necessary to remove the underlined text related to the name. As you continue with your annotation and correct such errors, the occasions when the underlined text doesn't align with the confirmed field should decrease.

Note: To trigger model training, a minimum of 40 operations is needed. For example, if you have 20 documents, you would need to annotate at least 2 fields per document, resulting in a total of 40 operations.

Tip: To optimize model perfomance, follow the suggestions from the Recommendations section. These suggestions are designed to improve the overall performance of your model.

Validate predicted documents

After all documents are uploaded and predicted, your goal is to either validate or modify the pre-annotated fields. For a document where all fields are accurately predicted, select Confirm to approve all fields at once. A document, once confirmed, will be signified with a green shield symbol in the document list.

If a document is only partially confirmed, it will be marked with an empty shield symbol in the document list. This symbolizes that the annotation process for this particular document is In Progress. Your end aim should be to make sure that all documents are Confirmed.

During validation, you can encounter the following scenarios:

• Prediction is correct and should be validated.
• Prediction is not correct and the field is present on the document.
• Prediction is not correct and the field is missing from the document.
• There is no prediction.

Prediction is correct and should be validated

If the prediction is accurate, you can confirm it by either selecting the underlined text and selecting Confirm or checking the confirmation checkbox for the field. The optimal method, however, is to press the hotkey assigned to the field (“N” in this scenario).

Prediction is incorrect and the field is present on the document

If the prediction is incorrect, select the correct text from the document and the appropriate field from the dropdown, then select Confirm.

When working with tables, you can choose to ignore incorrectly predicted values. These values will not be used for model training, and the retrained model will learn to avoid predicting them in future iterations.

Prediction is incorrect and the field is missing from the document

If there prediction is incorrect and the field is missing from the document, select the three-dot icon ⁝ next to the field name and select Mark as missing.

Important: You can also mark wrong fields as missing. For example, if you do not have a Vendor Address in your document but during processing a different field was pre-labeled as Vendor Address, you can just mark it as missing during validation.

No prediction

Fields that have no prediction are displayed as empty cells. You can mark these cells as missing one by one, or in bulk by selecting the Confirm button.

Document type settings

You can change the document type settings from the Annotate view.

To do so, select the three-dot icon ⁝ on the right side of the document type name and select Settings.

You can change the following settings:

• Base model: Dataset size estimations used in the Recommended Actions depend on the base model used to train. Using the most similar base model to your Document Type will reduce the amount of annotation work required.
• Number of languages: Dataset size estimation used in the Recommended Actions depend on the number of languages in the dataset. More languages generally require annotating more data.