Abstract: The disclosed invention utilizes a dictionary-based approach to identify languages within different zones in a multi-lingual document. As a first step, a document image is segmented into various zones, regions and word tokens, using suitable geometric properties. Within each zone, the word tokens are compared to dictionaries associated with various candidate languages, and the language that exhibits the highest confidence factor is initially identified as the language of the zone. Subsequently, each zone is further split into regions. The language for each region is then identified, using the confidence factors for the words of that region. For any language determination having a low confidence value, the previously determined language of the zone is employed to assist the identification process.

Abstract: A method for recognizing an object by processing reference feature vectors a collection of reference feature vectors is generated; each representing a reference pattern that belongs to one of a plurality of predefined classes. Each class has associated all reference feature vectors representing reference patterns belonging to that class. For a selected class, an associated hierarchy of one or more sets of possibility regions is generated, wherein for a selected set in the hierarchy the number of possibility regions in said selected set is significantly less than the number of reference feature vectors belonging to said selected class, and such that each reference feature vector belonging to said selected class is contained in at least one possibility region of said selected set, and such that each possibility region of said selected set contains relatively few reference feature vectors not belonging to said selected class.

Abstract: A method for preprocessing reference feature vectors representing patterns in order to form, for each selected pattern class, collections of regions. A hierarchy of possibility regions is formed, wherein all reference feature vectors of a pattern class are contained in each level of the hierarchy of possibility regions associated with the pattern class. This hierarchy is later used to exclude a pattern class from consideration if a feature vector representing an unknown pattern is not contained in some level of its associated hierarchy of possibility regions. A collection of certainty regions is used, wherein no reference feature vector not of a pattern class is contained within any certainty region associated with the pattern class. The certainty regions are later used to classify a feature vector representing an unknown pattern as belonging to a pattern class.

Abstract: A method of identifying an object within a set of object candidates includes the steps of:calculating the probability of occurrence of each member of a set of string candidates, wherein each string candidate contains one member of the set of object candidates, the calculating employing formulae using a method of groups and projections; andidentifying one of the objects based on the calculated probability.

Abstract: A method of identifying an object within a set of object candidates includes the steps of:calculating the probability of occurrence of each member of a set of string candidates, wherein each string candidate contains one member of the set of object candidates, the calculating employing formulae using a method of groups and projections; andidentifying one of the objects based on the calculated probability.

Abstract: A method for preprocessing reference feature vectors representing patterns in order to form, for each selected pattern class, collections of regions. A hierarchy of possibility regions is formed, wherein all reference feature vectors of a pattern class are contained in each level of the hierarchy of possibility regions associated with the pattern class. This hierarchy is later used to exclude a pattern class from consideration if a feature vector representing an unknown pattern is not contained in some level of its associated hierarchy of possibility regions. A collection of certainty regions is used, wherein no reference feature vector not of a pattern classis contained within any certainty region associated with the pattern class. The certainty regions are later used to classify a feature vector representing an unknown pattern as belonging to a pattern class.

Abstract: A method for classifying an input feature vector representing an unknown pattern as belonging to one of a set of pre-defined pattern classes, wherein the input feature vector is compared with pre-constructed regions. A hierarchy of possibility regions is used to exclude a pattern class from consideration. Certainty regions are used to classify with certainty the input feature vector as belonging to some pattern class. Confidence regions are used to identify, although not with certainty, the unknown input pattern and assign a confidence value indicating the relative confidence associated with the possiblity that this unknown pattern belongs to given pattern class.

Abstract: A method of identifying an object within a set of object candidates includes the steps of:calculating the probability of occurrence of each member of a set of string candidates, wherein each string candidate contains one member of the set of object candidates, the calculating employing formulae using a method of groups and projections; andidentifying one of the objects based on the calculated probability.