Handwritten numerical recognition based on multiple algorithms
dc.contributor.author | Kimura, F. | en_US |
dc.contributor.author | Shridhar, M. | en_US |
dc.date.accessioned | 2006-04-10T14:56:14Z | |
dc.date.available | 2006-04-10T14:56:14Z | |
dc.date.issued | 1991 | en_US |
dc.identifier.citation | Kimura, F., Shridhar, M. (1991)."Handwritten numerical recognition based on multiple algorithms." Pattern Recognition 24(10): 969-983. <http://hdl.handle.net/2027.42/29653> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6V14-48MPMB9-12Y/2/4f7abf98cae0efe0a8edbb388db8c4fe | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/29653 | |
dc.description.abstract | In this paper, the authors combine two algorithms for application to the recognition of unconstrained isolated handwritten numerals. The first algorithm employs a modified quadratic discriminant function utilizing direction sensitive spatial features of the numeral image. The second algorithm utilizes features derived from the profile of the character in a structural configuration to recognize the numerals. While both algorithms yield very low error rates, the authors combine the two algorithms in different ways to study the best polling strategy and realize very low error rates (0.2% or less) and rejection rates below 4%. | en_US |
dc.format.extent | 1043506 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Handwritten numerical recognition based on multiple algorithms | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | University of Michigan-Dearborn, Dearborn, Michigan, U.S.A. | en_US |
dc.contributor.affiliationum | University of Michigan-Dearborn, Dearborn, Michigan, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/29653/1/0000742.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0031-3203(91)90094-L | en_US |
dc.identifier.source | Pattern Recognition | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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