Classify Through ESP Feature Collection

Grooper’s ESP engine finds the different features of each page in order to group images together as classified documents. ESP uses three key feature collection mechanisms:

Lexical

Natural language processing looks at the language of the complete document to understand context.

Lexical Classification image

Rules-Based

Find unique key words or phrases that positively identify a document, like a title or section heading.

Rules-based document classification

Visual

Computer vision finds structured forms based on what they look like without having to read from OCR.

Computer vision-based document classification

What is ESP Separation?

Train document examples and see how the ESP separation engine interprets the content of each page, groups pages into documents, and simulates page breaking and classification.

Grooper page classification
  • A simple “train-by-example” interface lets you quickly teach ESP how to identify each document.
  • Real-time confidence scores show you both the document type and assumed page number for each page in a batch.
  • Estimated Page Index (EPI) finds page numbers on your documents. This data is used by ESP to decide if an unknown page is likely part of a surrounding document.

Sit Back and Watch the Classification

Provide document examples and watch as Grooper learns the correct document type for each instrument provided. When batch testing, unclassified documents (those with low confidence scores) can be flagged and sent to a queue for more training.

Grooper document classification

Photo Classification

You can also classify photos now through Grooper and leading artificial intelligence cloud services. Leverage the Azure Cognitive Services Computer Vision API to return words (or tags) that describe the content of a photo.

This can be used to classify photos or photos in documents, or to add to metadata. Adult or racy content can be detected and classified in a separate location.

Image Classification Animation

Give it a Try

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