CLASSIFICATION OF SCANNED SYMBOLS INTO EQUIVALENCE CLASSES

Patent №

US 5,778,095

Granted

1998-07-07

Filed 1995

Owner

XEROX CORPORATION

Lab

AI components

2

ml · vision

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

08575305

A method and apparatus for classification of scanned symbol into equivalence classes 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.

Machine learningVisionH03M 7/46G06F 18/28G06T 9/005

AI classification

Vision1.00
Machine learning0.97
AI hardware0.30
Knowledge representation0.01
Natural language0.00
Speech0.00
Evolutionary computation0.00
Planning0.00

Ownership

XEROX CORPORATION

assignment · 78200973

Assignors

DAVIES, DANIEL

On an employer assignment, the assignors are typically the inventors.

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