The Framework provides the opportunity to train the classifiers, to improve recognition of the document classes. If a document wasn’t classified properly, it means it was unknown to the active classifiers.
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The Intelligent Keyword Classifier activity can not only classify but also "split" files that contain multiple document types within them.The Keyword Based Classifier activity is the first such classifier, targeting classification for titled documents.The classification results help in applying the right strategy in extraction. The important thing is that you can use multiple classifiers in the same scope, you can configure the classifiers and, later in the framework, train them. If you are working with multiple documents types in the same project, to extract data properly you need to know what type of document you're working with. After digitization, the document is classified.This allows identifying what type of document a file is by using any classification algorithm.
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You can achieve this using the Classify Document Scope activity. The outputs of this step are the Document Object Model and a string variable containing all the document text and are passed down to the next steps. The difference for non-digital (scanned) documents is that you need to apply the OCR engine of your choice. As the documents are processed one by one, they go through the digitization process.
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This retrieves the text from any PDF or image, using, only if necessary, the OCR engine of your choice. You can achieve this using the Digitize Document activity. The package allows you to: Digitize documents