Staff-line detection and removal using a convolutional neural network

Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939), the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds) and the Instituto Universitario de Investigación Informática (IUII) from the University of Alicante

Stafflines pattern detection using the swarm intelligence algorithm

This paper demonstrates the application of the Swarm Intelligence (SI) algorithm to recognize the specific patterns that are present in the digital images of handwritten music scores. The application introduced in this paper involves the detection of stafflines using particle swarm. The introduced solution described in this paper is a new approach to the problem, and illustrates how optimization algorithm can be modified and successfully applied in different subjects such as pattern recognition. The developed algorithm can be used as a first stage in Optical Music Recognition (OMR) that is followed by the staffline removal phase. It is worth pointing out, that contrary to most state-of-the-art algorithms, the proposed method does not require a binarization step in the preprocessing stage