Abstract: A visual Reference tag is formed from an array of cells, wherein each is visually distinct from all other cells in a given neighborhood and each of said cells contains a single visual cue, for example a unique color, selected from a finite number of visual cues.

Abstract: A visual Reference tag is formed from an array of cells, wherein each cell is visually distinct from all other cells in a given neighborhood and each of said cells contains a single visual cue, for example a unique color, selected from a finite number of visual cues.

Abstract: A visual Reference tag is formed from an array of cells, wherein each cell is visually distinct from all other cells in a given neighborhood and each of said cells contains a single visual cue, for example a unique color, selected from a finite number of visual cues.

Abstract: A visual Reference tag is formed from an array of cells, wherein each cell is visually distinct from all other cells in a given neighborhood and each of said cells contains a single visual cue, for example a unique color, selected from a finite number of visual cues.

Abstract: A method and system for building a classification model for classifying documents comprising: representing each of a plurality of documents by a vector of n dimensions, said n dimensions forming a vector space; and representing the classification of already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes.

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A visual Reference tag is formed from an array of cells, wherein each cell is visually distinct from all other cells in a given neighborhood and each of said cells contains a single visual cue, for example a unique color, selected from a finite number of visual cues.

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector spaces whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A computer-implemented method of generating an input to be used by a classifying apparatus based on an electronic document comprising a plurality of elements, said method comprising: analyzing said electronic document to obtain one or more of said elements together with information about their corresponding position in said document; generating an electronic layout document to be used as said input of said classifying apparatus, said electronic layout document comprising: a representation of a plurality of said elements obtained in said analyzing step together with information representing their absolute and/or relative position in said electronic document.

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents