Tran, MT., Vo, QN. & Lee, GS. Binarization of music score with complex background by deep convolutional neural networks. Multimed Tools Appl 80, 11031–11047 (2021). https://doi.org/10.1007/s11042-020-10272-2
Binarization of music score with complex background by deep convolutional neural networks
|Author:||Tran, Minh-Trieu1; Vo, Quang-Nhat2; Lee, Guee-Sang1|
1Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021041910914
|Publish Date:|| 2021-04-18
Binarization is an important step for most of document analysis systems. Regarding music score images with a complex background, the existence of background clutters with a variety of shapes and colors creates many challenges for the binarization. This paper presents a model for binarization of the complex background music score images by fusion of deep convolutional neural networks. Our model is directly trained from image regions using pixel values as inputs and the binary ground truth as labels. By utilizing the generalization capability of the residual network backbone and useful feature learning ability of dense layer, the proposed network structures can differentiate foreground pixels from background clutters, minimize the possibility of overfitting phenomenon and thus can deal with complex background noises appearing in the music score images. Comparing to traditional algorithms, binary images generated by our method have a cleaner background and better-preserved strokes. The experiments with captured and synthetic music score images show promising results compared to existing methods.
Multimedia tools and applications
|Pages:||11031 - 11047|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A3B05049058 & NRF-2020R1A4A1019191).
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