The ICDAR/GREC 2013 Music Scores Competition on Staff Removal (original) (raw)

ICDAR 2013 Music Scores Competition: Staff Removal

2013 12th International Conference on Document Analysis and Recognition, 2013

The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.

The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification

2011 International Conference on Document Analysis and Recognition, 2011

In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a groundtruth of handwritten music score images. This paper describes the competition details, including the dataset and groundtruth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results.

An Effective Staff Detection and Removal Technique for Musical Documents

2012

Musical staff line detection and removal techniques detect the staff positions in musical documents and segment musical score from musical documents by removing those staff lines. It is an important preprocessing step for ensuing the Optical Music Recognition tasks. This paper proposes an effective staff line detection and removal method that makes use of the global information of the musical document and models the staff line shape. It first estimates the staff height and space, and then models the shape of the staff line by examining the orientation of the staff pixels. At last the estimated model is used to find out the location of staff lines and hence to remove those detected staff lines. The proposed technique is simple, robust, and involves few parameters. It has been tested on the dataset of the recent staff removal competition [1] held under the International Conference of Document Analysis and Recognition(ICDAR) 2011. Experimental results show the effectiveness and robustness of our proposed technique on musical documents with various types of deformations.

An Efficient Staff Removal Approach from Printed Musical Documents

2010 20th International Conference on Pattern Recognition, 2010

Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in [8] for evaluation purpose. From experimentation we have got encouraging results.

Music staffline removal using Convolutional Neural Network

International Journal of Advance Research, Ideas and Innovations in Technology, 2019

For decade interest in the analysis of handwritten music, scores have been growing rapidly. It captured the focus in two type's recognition of handwritten music scores and writer identification. Many types of research have proposed different algorithms to improve recognition. Staff line removal cannot be considered as a solved problem that too when dealing with ancient music scripts. However, this work proposes to model the problem as a supervised learning classification task. This work proposes a staff line removal method using CNN to evaluate window size and mask size retaining the symbol information with improved accuracy.

The ICDAR/GREC 2013 Music Scores Competition: Staff Removal

Lecture Notes in Computer Science, 2014

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A connected path approach for staff detection on a music score

2008

The preservation of many music works produced in the past entails their digitalization and consequent accessibility in an easy-tomanage digital format. Carrying this task manually is very time consuming and error prone. While optical music recognition systems usually perform well on printed scores, the processing of handwritten musical scores by computers remain far from ideal. One of the fundamental stages to carry out this task is the staff line detection. In this paper a new method for the automatic detection of music staff lines based on a connected path approach is presented. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.

Building a system for writer identification on handwritten music scores

2003

17th and 18th century music scores were copied and distributed in a manual way. Music historians are interested in how the compositions were distributed or in other words, who copied the compositions when and where. Such information may also help to determine the composer when a piece of unknown origin is found. In this paper, we present ongoing work on the development of a software system to analyse such documents automatically and to aid the musicologists in their task to register handwritten music scores. In particular, we focus on the application and adaptation of image processing methods to separate music symbols for the identification task from irrelevant elements.

Region-based layout analysis of music score images

Expert Systems with Applications

The Layout Analysis (LA) stage is of vital importance to the correct performance of an Optical Music Recognition (OMR) system. It identifies the regions of interest, such as staves or lyrics, which must then be processed in order to transcribe their content. Despite the existence of modern approaches based on deep learning, an exhaustive study of LA in OMR has not yet been carried out with regard to the precision of different models, their generalization to different domains or, more importantly, their impact on subsequent stages of the pipeline. This work focuses on filling this gap in literature by means of an experimental study of different neural architectures, music document types and evaluation scenarios. The need for training data has also led to a proposal for a new semi-synthetic data generation technique that enables the efficient applicability of LA approaches in real scenarios. Our results show that: (i) the choice of the model and its performance are crucial for the entire transcription process; (ii) the metrics commonly used to evaluate the LA stage do not always correlate with the final performance of the OMR system, and (iii) the proposed data-generation technique enables state-of-the-art results to be achieved with a limited set of labeled data.

On the Use of Textural Features for Writer Identification in Old Handwritten Music Scores

2009

Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.