Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/6043
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | - |
dc.creator | Lu, Z | - |
dc.creator | Chi, ZG | - |
dc.creator | Siu, WC | - |
dc.date.accessioned | 2014-12-11T08:24:37Z | - |
dc.date.available | 2014-12-11T08:24:37Z | - |
dc.identifier.issn | 1017-9909 | - |
dc.identifier.uri | http://hdl.handle.net/10397/6043 | - |
dc.language.iso | en | en_US |
dc.publisher | SPIE-International Society for Optical Engineering | en_US |
dc.rights | Lu Z, Chi Z and Siu W, "Length estimation of digit strings using a neural network with structure-based features," J. Electron. Imaging., 7(1), p. 79-85 (1998) | en_US |
dc.rights | Copyright 1998 Society of Photo-Optical Instrumentation Engineers & Society for Imaging Science and Technology. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. | en_US |
dc.rights | http://dx.doi.org/10.1117/1.482629 | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Feedforward neural nets | en_US |
dc.subject | Fuzzy neural nets | en_US |
dc.subject | Image recognition | en_US |
dc.subject | Image segmentation | en_US |
dc.subject | Multilayer perceptrons | en_US |
dc.subject | Parameter estimation | en_US |
dc.title | Length estimation of digit strings using a neural network with structure-based features | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Author name used in this publication: Zheru Chi | en_US |
dc.identifier.spage | 79 | - |
dc.identifier.epage | 85 | - |
dc.identifier.volume | 7 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1117/1.482629 | - |
dcterms.abstract | Accurate length estimation is very helpful for the successful segmentation and recognition of connected digit strings, in particular, for an off-line recognition system. However, little work has been done in this area due to the difficulties involved. A length estimation approach is presented as a part of our automatic off-line digit recognition system. The kernel of our approach is a neural network estimator with a set of structure-based features as the inputs. The system outputs are a set of fuzzy membership grades reflecting the degrees of an input digit string of having different lengths. Experimental results on National Institute of Standards and Technology (NIST) Special Database 3 and other derived digit strings shows that our approach can achieve an about 99.4% correct estimation if the best two estimations are considered. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of electronic imaging, Jan. 1998, v. 7, no. 1, p. 79-85 | - |
dcterms.isPartOf | Journal of electronic imaging | - |
dcterms.issued | 1998-01 | - |
dc.identifier.isi | WOS:000074613900010 | - |
dc.identifier.scopus | 2-s2.0-0348225192 | - |
dc.identifier.eissn | 1560-229X | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Lu_Lenath_Estimation_Diait.pdf | 142.96 kB | Adobe PDF | View/Open |
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