Automated Document Classification
Identify, Extract, & Group any Document Type
Use machine learning and rules-based logic to organize the chaos of structured and unstructured documents.
And you don’t have to be a data scientist to build a training set with Grooper. Discover how to use document classification logic with transparent features that you train and control.
3 Approaches to Solve Classification Problems:
We call our classification ‘ESP’ because it’s almost like a 6th sense. Grooper’s ESP Auto Separation Engine classifies and separates documents at the same time, based on page content.
And the beautiful part? All training is performed in a visual editor so operators see in real-time how documents will be processed.
Transparent A.I. removes any mystery as to how the machine learning models are functioning.
Find unique key words or features that identify a document, like a title, section heading, or any specific data element.
Classify documents faster and easier than ever before!
What is ESP Auto Separation?
It is the ideal solution for the most complicated document classification and separation challenges. By combining classification logic with extracted page data, it classifies and separates documents at the same time.
This means that the worst document nightmares are no problem for Grooper. Whether the documents are structured, unstructured, disorganized, or mis-labeled, Grooper has the tools to help you get around these problems.
Users train document examples in a visual interface to see how the ESP Separation Engine interprets the content of each page. Then, the resulting classification and grouping of pages is simulated so there are no surprises at run-time.
Any errors from pages that were incorrectly organized or added by mistake are easy to spot and correct.
- “Train-by-example” interface
- Real-time confidence scores
- Mis-filed pages are intelligently reorganized
Sit Back and Watch the Classification
Simply give Grooper document training examples and watch it learn the right document type for each one.
When batch testing documents, any with low confidence scores can be flagged and sent to a queue for an operator to look at and provide more training.
Photo and Image Classification
Classify photos through Grooper’s integration with A.I. cloud services. Use the Azure Computer Vision API to return words (or tags) that describe the content of a picture.
For example, you can use it to quickly find and read text within images. Or you can extract and tag documents by using info from the text found within pictures.
The extracted data can be used to to classify image files or photos within documents, or to add metadata.
How does this help you? One way is by reducing risk and ensuring compliance through creating workflows that will move documents or images with particular or sensitive content to a secure place.