Abstract: Described herein are methods for determining a type and unique features of a document. The methods generally include generating at least one document hypothesis corresponding to the type of the document. For each document hypothesis, the document type is verified. A best type hypothesis is selected. A document name is formed based on the best type hypothesis and one or more unique features of the document. Such steps are generally included in automatically or programmatically naming of documents. A unique or semi-unique name is given, one that reproduces some of the document's contents, attributes and/or characteristics. Each document is provided with a name that can be easily understood and that is related to the content of the document.

Abstract: Described herein are methods for finding substantially similar/different sources (files and documents), and estimating similarity or difference between given sources. Similarity and difference may be found across a variety of formats. Sources may be in one or more languages such that similarity and difference may be found across any number and types of languages. A variety of characteristics may be used to arrive at an overall measure of similarity or difference including determining or identifying syntactic roles, semantic roles and semantic classes in reference to sources.

Abstract: Techniques and methods are disclosed herein for combining and weighting of values from and associated with classifiers. Classifiers are used to recognize characters as part of an optical character recognition (OCR) system. Various methods of normalization facilitate combining of results of classifiers. For example, weight values may be entered into a weight table having two columns, one that includes weights from comparing patterns with images of correct characters, the other column includes weights from comparing patterns with images of incorrect characters.

Abstract: In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.

Abstract: In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.