Savitri Apparao Nawade, M. Hangarge, C. Dhawale, M. Reaz, Rajmohan Pardeshi, N. Arsad
{"title":"Old Handwritten Music Symbol Recognition Using Directional Multi-Resolution Spatial Features","authors":"Savitri Apparao Nawade, M. Hangarge, C. Dhawale, M. Reaz, Rajmohan Pardeshi, N. Arsad","doi":"10.1109/ICSCEE.2018.8538370","DOIUrl":null,"url":null,"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.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCEE.2018.8538370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
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.