Using hierarchical shape models to spot keywords in cursive handwriting data
- Creators
- Burl, M. C.
-
Perona, P.
Abstract
Different instances of a handwritten word consist of the same basic features (humps, cusps, crossings, etc.) arranged in a deformable spatial pattern. Thus, keywords in cursive text can be detected by looking for the appropriate features in the "correct" spatial configuration. A keyword can be modeled hierarchically as a set of word fragments, each of which consists of lower-level features. To allow flexibility, the spatial configuration of keypoints within a fragment is modeled using a Dryden-Mardia (DM) probability density over the shape of the configuration. In a writer-dependent test on a transcription of the Declaration of Independence (~1300 words, ~7500 characters), the method detected all eleven instances of the keyword "government" with only four false positives.
Additional Information
© 1998 IEEE. Date of Current Version: 06 August 2002. This research has been carried out and/or sponsored in part by (i) the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration, (ii) the Caltech Center for Neuromorphic Systems Engineering as a part of the NSF Engineering Research Center Program, and (iii) the California Trade and Commerce Agency, Office of Strategic Technology. The authors also wish to thank Mario Munich for his assistance.Additional details
- Eprint ID
- 28556
- DOI
- 10.1109/CVPR.1998.698657
- Resolver ID
- CaltechAUTHORS:20111221-152416737
- JPL/Caltech
- NSF Caltech Center for Neuromorphic Systems Engineering (CNSE)
- California Trade and Commerce Agency, Office of Strategic Technology
- Created
-
2011-12-23Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field
- Other Numbering System Name
- INSPEC Accession Number
- Other Numbering System Identifier
- 5985893