Fourier descriptors and handwritten digit recognition
dc.contributor.author | Lu, Yi | en_US |
dc.contributor.author | Schlosser, Steven | en_US |
dc.contributor.author | Janeczko, Michael | en_US |
dc.date.accessioned | 2006-09-11T17:19:38Z | |
dc.date.available | 2006-09-11T17:19:38Z | |
dc.date.issued | 1993-12 | en_US |
dc.identifier.citation | Lu, Yi; Schlosser, Steven; Janeczko, Michael; (1993). "Fourier descriptors and handwritten digit recognition." Machine Vision and Applications 6(1): 25-34. <http://hdl.handle.net/2027.42/46057> | en_US |
dc.identifier.issn | 0932-8092 | en_US |
dc.identifier.issn | 1432-1769 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/46057 | |
dc.description.abstract | This paper presents the results of a comparative study of various Fourier descriptor representations and their use in recognition of unconstrained handwritten digits. Certain characteristics of five distinct Fourier descriptor representations of handwritten digits are discussed, and illustrations of ambiguous digit classes introduced by use of these Fourier descriptor representations are presented. It is concluded that Fourier descriptors are practically effective only within the framework of an intelligent system, capable of reasoning about digit hypotheses. We describe a hypothesisgenerating algorithm based on Fourier descriptors which allows a classifier to associate more than one digit class with each input. Such hypothesis-generating schemes can be very effective in systems employing multiple classifiers. We compare the performance of the five Fourier descriptor representations based on experiment results produced by a particular hypothesis-generating classifier for a test set of 14000 handwritten digits. It is found that some Fourier descriptor formulations are more successful than others for handwritten digit recognition. | en_US |
dc.format.extent | 1020234 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag | en_US |
dc.subject.other | Communications Engineering, Networks | en_US |
dc.subject.other | Handwritten Digit Recognition | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Hypothesis Generation | en_US |
dc.subject.other | Image Processing | en_US |
dc.subject.other | Fourier Descriptors | en_US |
dc.subject.other | Boundary Features | en_US |
dc.subject.other | Rotational Transformation | en_US |
dc.subject.other | Symmetric Transformation | en_US |
dc.title | Fourier descriptors and handwritten digit recognition | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Environmental Research Institute of Michigan, P.O. Box 134001, 48113-4001, Ann Arbor, MI, USA; Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 48128-1491, Dearborn, MI, USA | en_US |
dc.contributor.affiliationother | Environmental Research Institute of Michigan, P.O. Box 134001, 48113-4001, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Environmental Research Institute of Michigan, P.O. Box 134001, 48113-4001, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/46057/1/138_2005_Article_BF01212429.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF01212429 | en_US |
dc.identifier.source | Machine Vision and Applications | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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