Extract (Activity)

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export_notes Extract is an Activity that retrieves information from folder Batch Folder documents, as defined by Data Elements in a data_table Data Model. This is how Grooper locates unstructured data on your documents and collects it in a structured, usable format.

About

Data extraction is configured using Data Model objects in a Content Model. This is where you define the data elements you wish to extract from your documents. Appropriately, you define the data to be extracted by adding Data Element objects to the Data Model. There are three main Data Elements:

• Data Field
• Data Section
• Data Table
  • Data Tables are also configured with their own special child Data Element: The Data Column object.

The Data Field object is the simplest Data Element. This will allow you to extract a simple list of fields (Such as "Invoice Date", "Invoice Number", "Invoice Amount", etc.).

The Data Table object allows you to extract tabular data. Tables are more complex than simple fields, in that they are a repeating series of fields organized into rows and columns. This requires a more robust Data Element to describe this data structure; hence, the addition of the Data Table object along with it's child Data Column objects.

The Data Section object allows you to extract Data Fields and/or Data Tables in repeating sections of a document. Data Sections may even have their own child Data Sections. This allows you to divide your document into sections and sub-sections, giving your Data Model its own levels of data hierarchy.

When the Extract activity runs, it will populate the Data Model with values extracted from the document's text data (obtained from the Recognize activity). How this text is located and returned is determined by the extraction configurations set on each Data Element.

Data Extractors

After defining what Data Elements you want to extract, you need to define how to populate those fields, tables, and sections with data. This is done with Data Extractors, often shorthanded to just "extractors".

Data Hierarchy

As discussed earlier, you can create hierarchical relationships within a single Data Model using Data Sections and Data Tables. As a direct child of a Data Model a Data Field will execute against the entire document. However, as a child of a Data Section a Data Field will only execute against the portion of the document described by that Data Section.

Data Models also benefit from a Content Model's inheritance structure. For example, the Content Model itself may have a Data Model but a Document Type may also have its own Data Model. The Document Type, as a child of the Content Model, will inherit all Data Elements from the parent Content Model's Data Model.

Glossary

Activity: Grooper Activities define specific document processing operations done to a inventory_2 Batch, folder Batch Folder, or contract Batch Page. In a settings Batch Process, each edit_document Batch Process Step executes a single Activity (determined by the step's "Activity" property).

• Batch Process Steps are frequently referred by the name of their configured Activity followed by the word "step". For example: "Classify step".

Batch Folder: The folder Batch Folder is an organizational unit within a inventory_2 Batch, allowing for a structured approach to managing and processing a collection of documents. Batch Folder nodes serve two purposes in a Batch. (1) Primarily, they represent "documents" in Grooper. (2) They can also serve more generally as folders, holding other Batch Folders and/or contract Batch Page nodes as children.

• Batch Folders are frequently referred to simply as "documents" or "folders" depending on how they are used in the Batch.

Content Model: stacks Content Model nodes define a classification taxonomy for document sets in Grooper. This taxonomy is defined by the collections_bookmark Content Categories and description Document Types they contain. Content Models serve as the root of a Content Type hierarchy, which defines Data Element inheritance and Behavior inheritance. Content Models are crucial for organizing documents for data extraction and more.

Data Column: view_column Data Columns represent columns in a table extracted from a document. They are added as child nodes of a table Data Table. They define the type of data each column holds along with its data extraction properties.

• Data Columns are frequently referred to simply as "columns".
• In the context of reviewing data in a Data Viewer, a single Data Column instance in a single Data Table row, is most frequently called a "cell".

Data Element: Data Elements are a class of node types used to collect data from a document. These include: data_table Data Models, insert_page_break Data Sections, variables Data Fields, table Data Tables, and view_column Data Columns.

Data Extractor: Data Extractor (or just "extractor") refers to all Value Extractors and Extractor Nodes. Extractors define the logic used to return data from a document's text content, including general data (such as a date) and specific data (such as an agreement date on a contract).

Data Field: variables Data Fields represent a single value targeted for data extraction on a document. Data Fields are created as child nodes of a data_table Data Model and/or insert_page_break Data Sections.

• Data Fields are frequently referred to simply as "fields".

Data Model: data_table Data Models are leveraged during the Extract activity to collect data from documents (folder Batch Folders). Data Models are the root of a Data Element hierarchy. The Data Model and its child Data Elements define a schema for data present on a document. The Data Model's configuration (and its child Data Elements' configuration) define data extraction logic and settings for how data is reviewed in a Data Viewer.

Data Section: A insert_page_break Data Section is a container for Data Elements in a data_table Data Model. variables They can contain Data Fields, table Data Tables, and even Data Sections as child nodes and add hierarchy to a Data Model. They serve two main purposes:

1. They can simply act as organizational buckets for Data Elements in larger Data Models.
2. By configuring its "Extract Method", a Data Section can subdivide larger and more complex documents into smaller parts to assist in extraction.
   • "Single Instance" sections define a division (or "record") that appears only once on a document.
   • "Multi-Instance" sections define collection of repeating divisions (or "records").

Data Table: A table Data Table is a Data Element specialized in extracting tabular data from documents (i.e. data formatted in rows and columns).

• The Data Table itself defines the "Table Extract Method". This is configured to determine the logic used to locate and return the table's rows.
• The table's columns are defined by adding view_column Data Column nodes to the Data Table (as its children).

Document Type: description Document Type nodes represent a distinct type of document, such as an invoice or a contract. Document Types are created as child nodes of a stacks Content Model or a collections_bookmark Content Category. They serve three primary purposes:

1. They are used to classify documents. Documents are considered "classified" when the folder Batch Folder is assigned a Content Type (most typically, a Document Type).
2. The Document Type's data_table Data Model defines the Data Elements extracted by the Extract activity (including any Data Elements inherited from parent Content Types).
3. The Document Type defines all "Behaviors" that apply (whether from the Document Type's Behavior settings or those inherited from a parent Content Type).

Extract: export_notes Extract is an Activity that retrieves information from folder Batch Folder documents, as defined by Data Elements in a data_table Data Model. This is how Grooper locates unstructured data on your documents and collects it in a structured, usable format.

Recognize: format_letter_spacing_wide Recognize is an Activity that obtains machine-readable text from contract Batch Pages and folder Batch Folders. When properly configured with an library_booksOCR Profile, Recognize will selectively perform OCR for images and native-text extraction for digital text in PDFs. Recognize can also reference an perm_mediaIP Profile to collect "layout data" like lines, checkboxes, and barcodes. Other Activities then use this machine-readable text and layout data for document analysis and data extraction.