Consolidation of equivalence classes of scanned symbols
First Claim
1. 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.
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Abstract
A method and apparatus for consolidation of equivalence classes of scanned symbols as may be used for image data compression. The present invention performs run-length symbol extraction and classifies symbols based on both horizontal and vertical run length information. An equivalence class is represented by an exemplar. Feature-based classification criteria for matching an exemplar is defined by a corresponding exemplar template. The feature-based classification criteria all use quantities that can be readily computed from the run endpoints. Reducing the number of equivalence classes is achieved through a process called equivalence class consolidation. Equivalence class consolidation utilizes the symbol classifier to identify matched exemplars indicating equivalence classes which may be merged. For a consolidated equivalence class, the exemplar matching the most symbols is selected as the representative for the class.
74 Citations
20 Claims
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1. 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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19)
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16. A system for classifying symbols in image data comprising:
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symbol extraction means for extracting a symbol from said image data; classification means coupled to said symbol extraction means, said classification means for defining equivalence classes and classifying extracted symbols into a plurality of equivalence classes based on a first criteria, said classification means operating on endpoint ranges derived from run data of extracted symbols; equivalence class consolidation means coupled to said classifications means said equivalence class consolidation means for reducing the number of equivalence classes defined by said classification means based on a second criteria; and output generation means coupled to said equivalence class consolidation means, said output generation means for generating an output comprised of a plurality of pairs of positional information and exemplar identifiers, and a dictionary of a plurality of exemplars.
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20. 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:
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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.
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Specification