Master of Science (MS)
First Committee Member
Number of Pages
In a system where medical paper document images have been converted to a digital format by a scanning operation, understanding the document types that exists in this system could provide for vital data indexing and retrieval. In a system where millions of document images have been scanned, it is infeasible to expect a supervised based algorithm or a tedious (human based) effort to discover the document types. The most sensible and practical way to do that is an unsupervised algorithm. Many clustering techniques have been developed for unsupervised classification. Many rely on all data being presented at once, the number of clusters to be known, or both. Presented in this thesis is a clustering scheme that is a two-threshold based technique relying on a hierarchical decomposition of the features. On a subset of document images, it discovers document types at an acceptable level and confidently classifies unknown document images.
Documents; Image; Learning; Types; Unsupervised
University of Nevada, Las Vegas
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Curtis, Dean Patrick, "Unsupervised learning of document image types" (2007). UNLV Retrospective Theses & Dissertations. 2227.