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.