Abstract: A system and method for automatically recognizing words or phrases in text.

Abstract: A system and method for automatically recognizing words or phrases in text.

Abstract: A system is presented for metermark recognition. This includes a plurality of binarization elements, each being operative to produce a binarized image from a source image. A clustering element clusters pixels within a first binarized image, produced at a first of the plurality of binarization elements, to locate at least one character string. A classification element identifies a plurality of characters comprising the located character string. A verification system evaluates the identified plurality of characters and instructs a second of the plurality of binarization elements to produce a second binarized image if the identified characters are determined to be unreliable.

Abstract: A system is presented for recognizing and identifying postal indicia on an envelope. The system includes an image acquisition element that acquires at least one binarized image of an envelope and isolates at least one region of interest from the at least one image. A candidate locator attempts to locate at least one candidate object within the at least one region of interest according to the distribution of dark pixels within the at least one region of interest. A feature extractor extracts an associated set of numerical feature values from each of the at least one candidate object. A classification element classifies each candidate object according to its associated set of numerical feature values.

Abstract: A system is presented for recognizing and identifying postal indicia on an envelope. The system includes an image acquisition element that acquires a first image, representing a first side of the envelope, and a second image, representing a second side of the envelope, and generates first and second candidate images from respective opposing corners of the first image and third and fourth candidate images from respective opposing corners of the second image. A feature extractor that, for each candidate image, divides the candidate image into a plurality of regions, extracts a plurality of numerical feature values from each of the plurality of regions, and recombines the plurality of feature values into a feature vector that represents the image. A classification element classifies the image into one of a plurality of output classes representing various types of postal indicia according to the numerical feature vector.

Abstract: A system for recognizing and identifying postal indicia on an envelope. This includes an image acquisition element that acquires a first image, representing a first side of the envelope, and a second image, representing a second side of the envelope. A feature extractor, for each of the first and second image, extracts a plurality of numerical feature values from each image as respective first and second feature vectors that represent the envelope. An orientation classification element classifies the envelope into one of a plurality of output classes representing a plurality of possible orientations according to the first and second feature vectors.

Abstract: Systems and methods are provided for determining the orientation of an envelope. A plurality of classification elements are each operative to analyze at least one image of the envelope and produce at least one output value indicative of the orientation of the envelope. An arbitrator determines an associated orientation for the envelope according to the plurality of output values provided by the plurality of classification elements.

Abstract: A method and computer program product are disclosed for refining character segmentation in an optical character recognition system receiving as input a plurality of candidate objects. Each candidate object below a threshold character width is merged with another candidate object at one or more merge lines to form a composite object. The plurality of candidate objects are preclassified to identify a plurality of composite objects and a plurality of character portions. Proposed split lines are determined for each of the composite objects. Regions are defined within each of the composite objects from the position of the merge and split lines. The defined regions are classified to obtain an associated score for each region. Complete region sets are defined for each composite object, each with an associated set ranking determined from the associated score of the regions comprising the set. The set having the highest ranking is selected.

Abstract: A method and computer program product are disclosed for refining character segmentation in an optical character recognition system receiving as input a plurality of candidate objects. Each candidate object below a threshold character width is merged with another candidate object at one or more merge lines to form a composite object. The plurality of candidate objects are preclassified to identify a plurality of composite objects and a plurality of character portions. Proposed split lines are determined for each of the composite objects. Regions are defined within each of the composite objects from the position of the merge and split lines. The defined regions are classified to obtain an associated score for each region. Complete region sets are defined for each composite object, each with an associated set ranking determined from the associated score of the regions comprising the set. The set having the highest ranking is selected.

Abstract: A method and computer program product are provided for classifying a character string. A plurality of candidate segmentations are determined for the character string, each ranked according to an associated score. At least two of the candidate segmentations are provided to a pattern recognition classifier. The character string is classified according to the highest-ranked candidate segmentation to obtain a first classified character string. An acceptor determines if the first classified character string is a valid character string. The classifier iteratively reclassifies the character string according to the ranked candidate segmentations until a valid character string is obtained if the first classified character string is not a valid character string.