Building a Structured Use Case

Hyperscience extracts data from documents and converts them into a machine-readable format. We support Structured, Semi-structured, and Additional documents. To learn how to differentiate between the document types, see Understanding Document Types.

Structured use cases

Use Structured layouts for documents that follow a consistent, predefined format. The pages in these documents have a clear visual design with fixed fields and standardized, repetitive patterns.

Key aspects of Structured layouts

• Fixed field locations - The fields are always in the same place on the document.
• Standardized formats - The format of the forms is consistent across all documents.
• Predictable patterns - The structure of the document remains consistent. To learn more about layout types, see Determining Layout Type.

In this article, you’ll learn how to build and evaluate your Structured models in Hyperscience for efficient document processing.

Step 1 - Sample your documents

Reviewing your documents is the first step in creating a robust, structured model.

• Gather document samples: Collect examples of the document type you want to process. Include different variations if available. Learn more in the Layout Variations section of this article.

• Review field data: Analyze the information in each field. Ensure the data fits the expected format and doesn’t include unusual or unsupported characters. Learn more in our Supported Characters and Default Data Types article.

• Example: Check if the field “Date” follows the same format throughout your dataset.

• Check for patterns and consistency: Consistent documents are crucial for your Structured model performance. Ensure all fields across the documents have the same position and format. That way, you will set the proper data type for each field when creating the Structured layout. Learn more about data types in What is a Data Type? and Choosing a Data Type.

• Document quality: Remove documents that would reduce model performance (e.g., documents containing unrelated information or highly distorted, skewed, noisy, pixelated, or duplicated pages).

• Edge cases: Check for documents that don’t fit your sample. Examples of edge cases are documents with unexpected formats.

Layout variations

Sometimes, your documents have small differences in their visual design. In Hyperscience, these differences are handled seamlessly by using layout variations within Structured layouts.

Example

• The position of fields (like names, dates, or totals) might be different from one document to another, or some fields might be missing in one document and present in another.

Step 2 - Upload a blank form

To process filled documents with the same visual design through Hyperscience, you need to create a Structured layout based on a non-filled (blank) form.

Upload a blank form

To upload a blank form:

1. Go to Library > Layouts.
2. Click Add Layout.
3. Click Structured Layout, and then click Next.
4. Upload a PDF, TIFF, JPG, or PNG file in one of the following ways:
   • Drag and drop the image file in the dialog box, or
   • click Choose File to upload from your machine.
5. Click Next.
6. Enter a name for your layout in the Layout Name field.
7. Choose the language you expect people to use when filling out the documents from the Language drop-down menu.
8. Click Create.

Once the blank form is added, you need to create a Structured layout in the Layout Editor. The Layout Editor is where you define what information Hyperscience should extract from the document. Learn more in Step 3 - Create a Structured Layout section of this article.

Step 3 - Create a Structured layout

Field types

Create a layout that shows Hyperscience where to find the data you need in the Layout Editor. It helps you map fields on the form to the information you want to extract. You can extract data from the following data points:

• Fields - The individual elements of a document that contain key information, such as names, dates, addresses, or amounts.
• Checkboxes - A checkbox is used to capture two-option answers, like “Yes”/”No” or “True”/”False.” For example, a form might ask,

Data types

Data types help the system understand what kind of information to expect in a specific field. It allows the system to process your documents more accurately. For example:

• The Numeric data type is used for fields that contain only numbers.
• The Generic Text data type is for fields with sentences or general text.
• Some data types like Date, Currency Amount, or Email Address expect specific formats or lengths.

Creating a Structured layout

To access the Layout Editor:

1. Go to Library > Layouts and click on the name of your layout.
2. Once in the Layout Variations tab, click on the name of the variation.

Layout Editor best practices

Drawing bounding boxes

• Use your cursor to draw bounding boxes around each field you want to extract.
• Ensure the bounding boxes are precise to avoid cutting off text, which could reduce machine confidence during extraction.

Field names and data types

• Assign a clear and descriptive name for each field.
• Select the appropriate data type (e.g., Numeric, Generic Text, Date) based on the field’s content.

Step 5 - Commit changes and deploy

• To commit your changes to the draft version of the layout variation, click Commit Changes on the right-side of the page.

Step 6 - Evaluate Structured models

After you’ve committed changes and deployed your Structured layout, it is Live and ready to process documents. The next step is to upload your filled documents and monitor the model’s performance.

Upload your completed documents

Upload your documents as submissions by following the steps below.

1. Go to Submissions.
2. Click Create Submission.
3. Upload the filled documents.
4. Select the flow you’re using for the model from the Flow drop-down list.
5. Select the layout used for the model from the Layout drop-down list.
6. Click Upload.

Using QA to improve accuracy

Use the Documents tab to review the extracted data and identify potential issues. This step is key to ensuring the quality of your model's predictions.

Field Transcription QA allows you to sample and review individual fields from submitted documents.