2023.1:Classification (Concept)

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Classification, in Grooper, is the process of assigning a Content Type (specifically a Document Type of a Content Model) to a Batch Folder.

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As far as Grooper is concerned a document is a Batch Folder objects with Batch Page objects as its children. Before classification, the document (Batch Folder) is an unclassified or "blank" document. Grooper doesn't know what kind of document it is yet. Documents are classified by:

1. Most often, the Classify activity using training data or rules set on a Content Model
   • A Classify step will automate document classification in a Batch Process. During the Classify activity, Grooper will use information from the document and its pages (generally text) and configurations from a Content Model (such as the Classification Method used) to assign the document a Document Type from a Content Model.
2. In some cases, the Separate activity by assigning a Document Type to each new folder created
   • For example, the ESP Auto Separation Separation Provider is a classification-based method of separation. It will both separate pages into document folders and classify the documents during the Separate activity.
3. Manually assigning a Document Type by right-clicking a Batch Folder and using the "Apply Document Type" command.

Classification Methods

A document can be classified in a variety of ways, through training examples of a Document Type and matching similarity to the training data or creating extractor based rules using key words phrases or other text data (or a combination of the two). The method you choose is determined by the Classification Method property of a Content Model. There are four Classification Methods available in Grooper

1. Lexical
2. Rules-Based
3. Visual
4. Labelset-Based

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In our documentation you may read about a "rules based" or "training based" classification approaches. A "rules based" approach refers not only to the Rules-Based method but to using Positive and Negative Extractors in general to set up "classification rules". A "training based" approach refers to using either the Lexical or Visual methods to classify documents using trained document samples. A "mixed classification" approach would use both training and rules together to classify documents.

Classification and Data Extraction

Classification is performed before data extraction. Classification is actually a critical part of data extraction. Data extraction executes using configured Data Elements in a Data Model. A Data Model is part of a Content Model's hierarchy. Therefore, a document must be assigned a Content Type (specifically a Document Type of a Content Model) in order for the Extract activity to see the Data Models specifications for data extraction.

Until a document is classified, it has no Content Type assigned to it. It doesn't know which Content Model and corresponding Document Types and Data Models you're using to extract data. Without this information, Grooper will not understand which Data Elements to look for on which Document Types. Nor will it know the the extractors used to return values to the Data Elements in a Data Model.

In other words, the document must be classified (having a Document Type assigned to it) before performing the Extract activity.