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 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 and structure is provided which allows for a plurality of patterns or characters which are joined to be separated for further processing. For characters which are determined not to have a fixed pitch, a first approximation cleavage point is determined based on the width of the segment. Next, a caliper histogram is formed plotting the distance between the uppermost and bottommost "on" pixel in each column of the segment. If a satisfactory minimum in the caliper histogram is found, this segment is cleaved into left and right segments at this point. It not, a raw histogram, indicating the total number of "on" pixels in each column of the segment, is formed. If an adequate minimum of the raw histogram is found, the segment is cleaved into right and left segments at this point. In one embodiment of this invention, the caliper histogram operation is not performed if it is determined that the segment includes top and bottom serifs.

Abstract: Means is provided for use in an optical character recognition system to narrow the possible characters associated with a given unknown input character, primarily based upon subline information. This means also serves to add to the possibility set additional possible characters, and to determine point sizes for each character. In the event that the subline information provided is erroneous, the subline information is corrected.

Abstract: A method of determining the outline of a character in a character recognition system, the character being included in a segment comprising a plurality of pixels which are either white corresponding to the absence of information or black corresponding to the presence of information, the method including the steps of: searching the segment of pixels to detect the location of the first black pixel nearest one edge of the segment; storing the location in the segment of the first detected black pixel; scanning those pixels in the segment within a predetermined distance from the first detected black pixel to detect a second black pixel; storing the location in the segment of the second detected black pixel; and continuing the scanning to successively detect black pixels surrounding the last detected black pixel.

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 is provided for forming feature vectors representing the pixels contained in a pattern desired to be recognized, and reference patterns. One part of the feature vector is representative of the pixels contained in the pattern itself, while not requiring a very large feature vector which exactly defines each pixel of the pattern. One embodiment of this invention provides that another part of the feature vector, consisting of one or more bytes of the feature vector, defines the aspect ratio of the pattern. In one embodiment, each byte of the feature vector representing the pixels contained in the character represents the relative ratio of black pixels to total pixels in a specific area of the character; other functions relating input matrix and output feature vector information can be used.