Please use this identifier to cite or link to this item: http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3759
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dc.contributor.authorNawade S.A
dc.contributor.authorHangarge M
dc.contributor.authorDhawale C
dc.contributor.authorReaz M.B.I
dc.contributor.authorPardeshi R
dc.contributor.authorArsad N.
dc.date.accessioned2020-06-12T15:01:15Z-
dc.date.available2020-06-12T15:01:15Z-
dc.date.issued2018
dc.identifier.citation2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 , Vol. , , p. -en_US
dc.identifier.uri10.1109/ICSCEE.2018.8538370
dc.identifier.urihttp://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3759-
dc.description.abstractAutomatic recognition of musical symbols received huge attention in the last two decades. Most of the work is carried out for the recognition of printed symbols whereas little attention is given to handwritten symbols. In handwritten musical symbols, when we deal with historical and old handwritten musical symbols, the problem becomes more challenging. In this paper, we have dealt with recognition ofold handwritten musical symbols. In our method, we have used directional multi-resolution statistical descriptors by combining Radon Transform, Discrete Wavelet Transform, and Statistical Filters. Simple k-NN classifier is used with fivefold cross validation. We have achieved encouraging results on our dataset. © 2018 IEEE.en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectDiscrete Wavelet Transform
dc.subjectk-NN Classifier
dc.subjectOptical Music Symbol Recognition
dc.subjectRadon Transform
dc.subjectStatistical Filters
dc.titleOld Handwritten Music Symbol Recognition Using Directional Multi-Resolution Spatial Featuresen_US
dc.typeConference Paper
Appears in Collections:2. Conference Papers

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