I found this article about BeyondRecognition written by Mimi Dionne which does an excellant job of explaining in plain english how BR can benefit every business with large unstructured data collections.
You can read the entire article right here
Ever heard of BeyondRecognition? If not, the time to learn is now. The Chantilly, Va.-based "document textnology" software provider offers document managers an alternative to optical character recognition (OCR), while delivering results with accuracy and speed.
How BeyondRecognition Works
BeyondRecognition (“BR”) may be a young innovation, but it is a viable alternative to OCR. It utilizes glyphs, a letter or character formed by pixels that are of a sufficiently different color from the background of the document as to be identifiable. BR groups like glyphs into clusters at the character and word level. BR converts one glyph per cluster to text as appropriate.
While OCR continuously decides what each glyph is, BeyondRecognition’s single instance technology need only recognize one glyph per cluster to form a catalog of letters or characters. The advantage: the return on investment of using single instance recognition technology is much higher with a smaller data set — a faster processing speed and better accuracy rate — which shortens the Records Management program’s work breakdown structure significantly.
Because BeyondRecognition software is glyph dependent — not text — it is more versatile:
- BR is language agnostic. It currently recognizes over forty languages.
- BR is symbology agnostic. It can recognize and relate non-text elements.
- BR clusters visual similarities. It works on all kinds of documents.
- BR is over ninety-nine percent accurate.
- BR scales. It can analyze millions of pages per day per the BeyondRecognition server.
BeyondRecognition’s zonal attribute extraction permits subject matter experts to extract attributes from document classifications by clicking and dragging zones on one document per document type cluster.
Again for more of Mimi's article click here