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

Fornés, Alícia; Dutta, Anjan; Gordo, Albert; Lladós, Josep

doi:10.1109/icdar.2011.300

Cited by 47 publications

(27 citation statements)

References 8 publications

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“…It is important to note that some distortions such as interruption and thickness made the detection process very difficult in most of these methods and most images are rejected because of the lack of identification. The one which gets better results in most cases and also without rejecting any image is ISI01-HA and its total algorithm is mentioned in [1]. The comparison between our proposed method and ISI01-HA method is shown in table 1.…”

Section: Resultsmentioning

confidence: 99%

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Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets

Alirezazadeh¹,

Ahmadzadeh

2014

Journal of Advanced Computer Science &Amp; Technology

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Musical staff detection and removal is one of the most important preprocessing steps of an Optical Music Recognition (OMR) system. This paper proposes a new method for detecting and restoring staff lines from global information of music sheets. First of all the location of staff lines is determined. Therefore, music staff is sliced. The staff line segments are recognized at each slice and then with adequate knowledge of staff line locations, the deformed, interrupted or partly removed staff lines can be rebuilt. A new approach for staff removal algorithm is suggested in this paper fundamentally based on removing all detected staff lines. At last, the Fourier transform and Gaussian lowpass filter will help to reconstruct the separated and interrupted symbols. It has been tested on the dataset of the musical staff removal competition held under ICDAR 2012. The experimental results show the effectiveness of this method under various kinds of deformations in staff lines.

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Section: Resultsmentioning

confidence: 99%

“…Several types of methods suggested in music staff removal competition are explained in [1]. It is important to note that some distortions such as interruption and thickness made the detection process very difficult in most of these methods and most images are rejected because of the lack of identification.…”

Section: Resultsmentioning

confidence: 99%

Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets

Alirezazadeh¹,

Ahmadzadeh

2014

Journal of Advanced Computer Science &Amp; Technology

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“…Because of the excellent results obtained with oBIFs and sparse or collaborative classifiers -99.8% vs. 77%, the best reported result so far [10] -we proceed further to validate the system with different train/test partitions of CVCMUSCIMA. Thus, we consider splits with 1 training page per writer, up to 10 training pages, and the remaining pages being the testing pages.…”

Section: Writer Identificationmentioning

confidence: 99%

“…For writer identification we use the recently introduced CVCMUSCIMA [10,11] dataset. This dataset has 50 writers and 20 music scores pages per ( Table 1).…”

Section: Writer Identificationmentioning

confidence: 99%

A Training-free Classification Framework for Textures, Writers, and Materials

Timofte¹,

Gool²

2012

Procedings of the British Machine Vision Conference 2012

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We advocate the idea of a training-free texture classification scheme. This we demonstrate not only for traditional texture benchmarks, but also for the identification of materials and of the writers of musical scores. State-of-the-art methods operate using local descriptors, their intermediate representation over trained dictionaries, and classifiers. For the first two steps, we work with pooled local Gaussian derivative filters and a small dictionary not obtained through training, resp. Moreover, we build a multi-level representation similar to a spatial pyramid which captures region-level information. An extra step robustifies the final representation by means of comparative reasoning. As to the classification step, we achieve robust results using nearest neighbor classification, and state-of-the-art results with a collaborative strategy. Also these classifiers need no training.To the best of our knowledge, the proposed system yields top results on five standard benchmarks: 99.4% for CUReT, 97.3% for Brodatz, 99.5% for UMD, 99.4% for KTHTIPS, and 99% for UIUC. We significantly improve the state-of-the-art for three other benchmarks: KTHTIPS2b -66.3% (from 58.1%), CVC-MUSCIMA -99.8% (from 77.0%), and FMD -55.8% (from 54%).

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“…We evaluate the proposed method on ICDAR 2011 dataset for music score removal competition [22]. This dataset consists of 1000 ideal handwritten music sheet images and these ideal images are distorted by 12 deformation models.…”

Section: Evaluation Of Staff Line Removalmentioning

confidence: 99%

A Robust Staff Line Height and Staff Line Space Estimation for the Preprocessing of Music Score Recognition

Na¹,

Kim²,

Nquyen³

2015

Journal of Internet Computing and Services

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In this paper, we propose a robust pre-processing module for camera-based Optical Music Score Recognition (OMR) on mobile device. The captured images likely suffer for recognition from many distortions such as illumination, blur, low resolution, etc. Especially, the complex background music sheets recognition are difficult. Through any symbol recognition system, the staff line height and staff line space are used many times and have a big impact on recognition module. A robust and accurate staff line height and staff line space are essential. Some staff line height and staff line space are proposed for binary image. But in case of complex background music sheet image, the binarization results from common binarization algorithm are not satisfactory. It can cause incorrect staff line height and staff line space estimation. We propose a robust staff line height and staff line space estimation by using run-length encoding technique on edge image. Proposed method is composed of two steps, first step, we conducted the staff line height and staff line space estimation based on edge image using by Sobel operator on image blocks. Each column of edge image is encoded by run-length encoding algorithm Second step, we detect the staff line using by Stable Path algorithm and removal the staff line using by adaptive Line Track Height algorithm which is to track the staff lines positions. The result has shown that robust and accurate estimation is possible even in complex background cases.

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

Cited by 47 publications

References 8 publications

Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets

Effective staff line detection, restoration and removal approach for different quality of scanned handwritten music sheets

A Training-free Classification Framework for Textures, Writers, and Materials

A Robust Staff Line Height and Staff Line Space Estimation for the Preprocessing of Music Score Recognition

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