Consolidation of equivalence classes of scanned symbols

Claim 2

In a system for classifying symbols in image data, said image data representing an image, a method for merging equivalence classes to create a consolidated equivalence class, said method comprising the steps of: a) classifying symbols extracted from image data to create a plurality of equivalence classes, each of said equivalence classes having an exemplar representing the equivalence class; b) matching a first equivalence class exemplar to a second equivalence class exemplar, said matching step using endpoint ranges derived from one of run endpoints of the first equivalence class exemplar and run endpoints of the second equivalence class exemplar; c) if said first equivalence class exemplar and said second equivalence class exemplar match, combining said first equivalence class and said second equivalence class exemplar to create a consolidated equivalence class; d) repeating step b)-c) for each of said plurality of equivalence classes to create a plurality of consolidated equivalence classes; and e) for each of said plurality of consolidated equivalence classes, selecting an exemplar from a set of exemplars used to create said consolidated equivalence class, to represent said consolidated equivalence class.

Claim 3

The method as recited in claim 1 wherein said step of matching a first equivalence class exemplar to a second equivalence class exemplar is further comprised of the steps of: b1) determining if said second equivalence class is a size within an allowable range of said first equivalence class and said first equivalence class is a size within an allowable range of said second equivalence class; b2) if not within said ranges, determining that said first equivalence class exemplar and said second equivalence class exemplar do not match; b3) if within said range comparing symbol features of said second equivalence class exemplar to thresholds specified by said first equivalence class exemplar; b4) if symbol features of said second equivalence class exemplar are not within thresholds specified by said first equivalence class exemplar, determining that said first equivalence class exemplar and said second equivalence class exemplar do not match; b5) if symbol features of said second equivalence class exemplar are within thresholds specified by said first equivalence class exemplar, comparing run endpoints of said second class equivalence class exemplar with run endpoints of runs in said first equivalence class exemplar to determine if a match exists; b6) if run endpoints do not match, determining that said first equivalence class exemplar and said second equivalence class exemplar do not match; and b7) if run endpoints do match, determining that said first equivalence class exemplar and said second equivalence class exemplar do match.

Claim 4

The method as recited in claim 2 wherein said step of combining said first equivalence class and said second equivalence class exemplar to create a consolidated equivalence class further comprises the steps of: c1) specifying an allowable range of said consolidated equivalence class to include limitations of both said first equivalence class and said second equivalence class; and c2) specifying an actual range of said consolidated equivalence class to include exemplars of both said first equivalence class and said second equivalence class.

Claim 5

The method as recited in claim 3 wherein said first equivalence class is an existing consolidated equivalence class and said second equivalence class has a single exemplar.

Claim 6

The method as recited in claim 3 wherein said first equivalence class is an existing consolidated equivalence class and said second equivalence class is an existing consolidated equivalence class.

Claim 7

The method as recited in claim 3 wherein said first equivalence class has a single exemplar and said second equivalence class is an existing consolidated equivalence class.

Claim 8

The method as recited in claim 3 wherein said first equivalence class has a single exemplar and said second equivalence class has a single exemplar.

Claim 9

The method as recited in claim 3 wherein said step of classifying symbols extracted from image data to create a plurality of equivalence classes is performed using information derived from runs of extracted symbols.

Claim 10

The method as recited in claim 8 wherein said step of selecting an exemplar from a set of exemplars used to create said consolidated equivalence class, to represent said consolidated equivalence class is comprised of determining which of said exemplars matches the most extracted symbols.

Claim 11

The method as recited in claim 1 wherein step of classifying symbols extracted from image data to create a plurality of equivalence classes is further comprised of the step of generating an output data stream comprised of a dictionary of exemplars and exemplar identifier/position information, said output data stream for representing said image.

Claim 12

The method as recited in claim 10 further comprising the step of updating said dictionary to reflect consolidation of equivalence classes.

Claim 13

The method as recited in claim 11 wherein said output data stream is a compressed version of said image data.

Claim 14

The method as recited in claim 1 wherein said first equivalence class exemplar is compared in a first unshifted version.

Claim 15

The method as recited in claim 13 wherein said first equivalence class exemplar is compared in a second right shifted version.

Claim 16

The method as recited in claim 14 wherein said first equivalence class exemplar is compared in a third up shifted version.

Claim 19

The system as recited in claim 13 wherein said first criteria is based on features and runs of an extracted symbol and a single exemplar.

Claim 20

The system as recited in claim 14 wherein said second criteria is based on exemplars of equivalence classes being considered for consolidation.

Claim 21

The system as recited in claim 13 wherein said classification means further comprises means for determining whether endpoint positions of the runs of a first of the equivalence class exemplars are within corresponding endpoint ranges of legal endpoint locations of a second of the equivalence class exemplars.

Claim 22

In a system for classifying symbols in image data, said image data representing an image, a method for merging equivalence classes to create a consolidated equivalence class, said method comprising the steps of: a) classifying symbols extracted from image data to create a plurality of equivalence classes, each of said equivalence classes having an exemplar representing the equivalence class; b) matching a first equivalence class exemplar to a second equivalence class exemplar, said matching step using endpoint ranges derived from one of run endpoints of the first equivalence class exemplar and run endpoints of the second equivalence class exemplar; wherein said matching step further comprises the step of determining whether endpoint positions of the runs of one of the first and the second equivalence class exemplars are within corresponding endpoint ranges of legal endpoint locations of the other of the first and the second equivalence class exemplars; c) if said first equivalence class exemplar and said second equivalence class exemplar match, combining said first equivalence class and said second equivalence class exemplar to create a consolidated equivalence class; d) repeating step b)-c) for each of said plurality of equivalence classes to create a plurality of consolidated equivalence classes; and e) for each of said plurality of consolidated equivalence classes, selecting an exemplar from a set of exemplars used to create said consolidated equivalence class, to represent said consolidated equivalence class.

Claim 23

In a system for classifying symbols in image data, said image data representing an image, a method for merging equivalence classes to create a consolidated equivalence class, said method comprising the steps of: a) classifying symbols extracted from image data to create a plurality of equivalence classes, each of said equivalence classes having an exemplar representing the equivalence class; b) matching a first equivalence class exemplar to a second equivalence class exemplar, said matching step using endpoint ranges derived from one of run endpoints of the first equivalence class exemplar and run endpoints of the second equivalence class exemplar; wherein said matching step further comprises the step of determining whether endpoint positions of the runs of one of the first and the second equivalence class exemplars are within corresponding endpoint ranges of legal endpoint locations of the other of the first and the second equivalence class exemplars; c) if said first equivalence class exemplar and said second equivalence class exemplar match, combining said first equivalence class and said second equivalence class exemplar to create a consolidated equivalence class; d) repeating step b)-c) for each of said plurality of equivalence classes to create a plurality of consolidated equivalence classes; and e) for each of said plurality of consolidated equivalence classes, selecting an exemplar from a set of exemplars used to create said consolidated equivalence class, to represent said consolidated equivalence class.