Data Types

Data Types

What is a Data Type?

Data types are used by the system to specify the kinds of characters expected, or not expected. For example, the Numeric type is best used for values where only numbers are expected, versus the Generic Text type, which is best used for values containing text.

Supported Characters and Default Data Types

Choosing a Data Type

A Data Type is a type of metadata that is used by the system to specify the kinds of characters expected, or not expected, for a field. Choosing the most appropriate data type ensures high automation rates and fewer Supervision tasks. You can learn more about it in the documentation.

Navigating the Field Dictionary

Overview The Field Dictionary allows you to define a field so that it consistently corresponds to a specific data type and an optional output name. This is specifically used for creating Structured layouts – if numerous layouts have the same field, they can be easily managed together.

Defining Fields in the Field Dictionary

You can define metadata for new fields in the Field Dictionary so they can be used in Structured layouts. You can define fields either individually or as a group. Defining a single field: Click on the Add Fields button to start the process.

Editing Fields in the Field Dictionary

Editing a field definition in the Field Dictionary will automatically update all layout drafts which use that field’s dictionary definition. This ensures that field changes are made consistently across all impacted layout drafts.

Creating Data Types with ML Configurations

To create a data type with a descriptive display name that matches the name of the field in your specific document, you can use the system's existing ML configuration values to reduce confusion for users who manage field definitions in the Layout Editor.

Creating Data Types with a List of Expected Values

Data types can be created with a user-provided list of values – we call these "list custom field data types". List CFDTs enable the system to more accurately transcribe fields where a defined list of values is expected – for example, a list of account types.

Creating Data Types with Custom Patterns

Data types can be created to validate field values against a set pattern – we call these "pattern custom field data types". Pattern CFDTs enable the system to more accurately transcribe fields where a pattern is expected – for example, an account number format.

Checkboxes and Signatures

Checkboxes and signatures are data types for non-text fields. Hyperscience allows you to extract signature and checkbox fields from Structured and Semi-structured documents. The possible values for checkboxes and signatures are: True or False.

Default Data Types