Please use this identifier to cite or link to this item: http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3759
Title: Old Handwritten Music Symbol Recognition Using Directional Multi-Resolution Spatial Features
Authors: Nawade S.A
Hangarge M
Dhawale C
Reaz M.B.I
Pardeshi R
Arsad N.
Keywords: Discrete Wavelet Transform
k-NN Classifier
Optical Music Symbol Recognition
Radon Transform
Statistical Filters
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 , Vol. , , p. -
Abstract: Automatic 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.
URI: 10.1109/ICSCEE.2018.8538370
http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3759
Appears in Collections:2. Conference Papers

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