Document separation

Aluma can auto-detect document boundaries in large document files, bringing structure to old records and allowing new documents to be scanned without manual separation.

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Simple Configuration

The combination of machine learning with a graphical user interface to build rules allows even advanced separation projects to be configured in a few hours.

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Use Cases

A wide range of techniques built from years of experience allows the technology to address anything from archives of medical records or mortgage files to inbound invoices or delivery notes.

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Manual Validation

Where the automated separation is uncertain, we provide a simple web-based validation portal to allow document boundaries to be reviewed and adjusted.

Powerful and flexible auto-separation

Our algorithms use a combination of machine learning and rules to address the widest possible range of automatic separation tasks.

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Auto-everything

Training Aluma to automatically separate your documents can be as simple as providing samples of individual documents and clicking a button!

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Page features

The algorithms can be enhanced with specific page features such as page numbers or a title like “Appendix” to improve accuracy.

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Split and merge rules

In addition to or instead of machine learning, powerful rules can be specified as to exactly where documents should be split apart or merged together.

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High performance

Aluma’s separation technology has been used in the most demanding environments, with hundreds of document types and tens of millions of documents per year.


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