Abstract: A computer extracts important terms, phrases or sentences from a document that it segments. The computer generates a square sum matrix from the document segments. The computer determines the importance of a given term, phrase or sentence on the basis of eigenvectors and eigenvalues of the matrix. The computer thereby selects the important terms, phrases or sentences related to the central concepts of the document.

Abstract: A discriminant function is defined by conventional learning discriminant analysis (22) and a value of the discriminant function is calculated (23) for all the training patterns in the in-category pattern set of each category and for all the training patterns in the in-category rival pattern set of the category. The incategory pattern set is composed of all the training patterns defined as belonging to the category. The rival pattern set is composed of the training patterns that belong to other categories and that are incorrectly recognized as belonging to the category. An in-category pattern subset and a rival pattern subset are then formed (24) for each category. The in-category pattern subset for the category is formed by selecting a predetermined number of the training patterns that belong to the in-category pattern set and that, among the training patterns that belong to the in-category pattern set, have the largest values of the discriminant function.

Abstract: Character recognition with an improved recognition ratio is provided without requiring special operations to be performed before character recognition is performed. A preliminary character recognition operation is performed in which the feature vector of an input character is compared to a recognition dictionary that contains a reference vector for each category. The candidate category to which the input character belongs is determined, and the feature vector and recognition result for each input character is saved. The input characters judged to have been recognized with high reliability are selected with reference to their recognition results. The feature vector of each selected input character is used to predict a writer-specific feature vector of a category different from the candidate category to which the selected input character belongs.

Abstract: A reference pattern vector is obtained from training patterns belonging to each class and is held as a parameter of an original distance function in a distance dictionary. A discriminant function is defined which has, as variables, the difference between respective corresponding components of a feature vector of each training pattern and the corresponding reference pattern vector and the square of the difference. Training patterns of all classes are discriminated with the original distance function and a rival pattern set, which includes patterns misclassified as belonging to a respective class, is derived from the results of discrimination of the training patterns. A discriminant analysis is made between the training pattern set of each class and the corresponding rival pattern set to thereby determine parameters of the discriminant function, which are held in a discriminant dictionary.

Abstract: A character in a pattern area is optically scanned and the scanned black and white signal cells of the character are stored in a digital memory. The character in the digital memory is recognized through an initial feature extraction process, unification process, concentration process, and recognition process. In the initial feature extraction process each cell thus scanned is encoded according to the presence of a character line or a black cell in a predetermined direction from each cell. The unification process unifies the code of each cell according to a predetermined algorithm, and the concentration process provides a concentrated code for each cell. The recognition process counts each of the concentrated codes and determines the class to which the character to be recognized belongs when the number of particular concentrated codes reaches a predetermined threshold which is defined for each class.