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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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.

Explore more features

Aluma provides a broad set of automation technologies which can be mixed and matched to meet your requirements.

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