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dc.contributor.authorFu, HCen_US
dc.contributor.authorXu, YYen_US
dc.date.accessioned2014-12-08T15:27:13Z-
dc.date.available2014-12-08T15:27:13Z-
dc.date.issued1998en_US
dc.identifier.isbn0-8194-2876-0en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/19453-
dc.identifier.urihttp://dx.doi.org/10.1117/12.311079en_US
dc.description.abstractIn this paper, we present a Bayesian decision-based neural networks (BDNN) for handwritten Chinese character recognition. The proposed Self-growing Probabilistic Decision-based Neural Networks (SPDNN) adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Our prototype system demonstrates a successful utilization of SPDNN to the handwriting of Chinese character recognition on the public databases, CCL/HCCR1 and in house database (NCTU/NNL). Regarding the performance, experiments on three different databases all demonstrated high recognition (86 similar to 94%) accuracy as well as low rejection/acceptance (6.7%) rates. As to the processing speed, the whole recognition process (including image preprocessing feature extraction, and recognition) consumes approximately 0.27 second/character on a Pentium-100 based personal computer, without using hardware accelerator or co-processor.en_US
dc.language.isoen_USen_US
dc.subjectBayesian decision-based neural networksen_US
dc.subjectSelf-growing Probabilistic Decision-based Neural Networksen_US
dc.subjectsupervised learningen_US
dc.subjectoptical character recognitionen_US
dc.titleRecognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networksen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1117/12.311079en_US
dc.identifier.journalINPUT/OUTPUT AND IMAGING TECHNOLOGIESen_US
dc.citation.volume3422en_US
dc.citation.spage134en_US
dc.citation.epage145en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000077456300015-
Appears in Collections:Conferences Paper


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