CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal

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

doi:10.1007/s10032-011-0168-2

Cited by 66 publications

(41 citation statements)

References 25 publications

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“…The results of our experiments are shown in Table I. This table reports the average Precision, Recall and the Error rate [15]. Our proposed method provide noteworthy results for all forms of deformations.…”

Section: Resultsmentioning

confidence: 96%

A novel staff removal method for printed music image

Tajeripour

Sotoodeh

2012

IEICE Electron. Express

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Abstract:The first phase in optical music recognition (OMR) is staff-removal. The goal of this phase is to remove staffs and keep musical symbols with high precision, which is very important for next phases in OMR including segmentation and classification. In this paper a novel staff-removal method is proposed. In this method we used run-length coding to compress the input image and then the staff-lines are detected and removed using two common features, namely the thickness of lines and the space between them. Afterwards, The nonstaff objects such as long objects, dynamic wage and destroyed texts are removed using projection and morphological operations. Keywords: staff-removal, staff-line, run-length, OMR, morphology, projection Classification: Other communication hardwares & Syst., vol. E79-D, no. 5, pp. 548-554, 1996. [7] F. Rossant and I. Bloch, "Robust and adaptive omr system including fuzzy modeling, fusion of musical rules, and possible error detection," References

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Section: Resultsmentioning

confidence: 96%

A novel staff removal method for printed music image

Tajeripour

Sotoodeh

2012

IEICE Electron. Express

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“…This competition makes use of the CVC-MUSCIMA database [11], which contains handwritten music score images with a perfect ground truth on staff removal. Many of the most advanced methods showed a decreasing accuracy when different distortions were applied to the input scores.…”

Section: Introductionmentioning

confidence: 99%

Music staff removal with supervised pixel classification

Calvo-Zaragoza

Micó

Oncina

2016

IJDAR

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“…The DocCreator ability to create synthetic documents that mimic real ones is effective for typewritten and handwritten characters (as long as the characters are apart from one another). Images created with DocCreator have already been used in many DIAR contexts: text/background/image pixel classification [36]; staff removal [13,37,38]; and handwritten character recognition [39]. In this article we present how DocCreator can be useful to enhance a binarization algorithm and for OCR performance prediction.…”

Section: Algorithms For Synthetic Data Augmentationmentioning

confidence: 99%

“…The 3D distortion and the character degradation models were used in order to generate an extended database from the 1000 images of the MUSCIMA database [13]. As a result, the extended database contains 6000 semi-synthetic grayscale images and 6000 semi-synthetic binary images.…”

Section: Document Image Generation For Performance Evaluationmentioning

confidence: 99%

“…; for handwritten documents IAM database [6], RIMES [7], GERMANA [8], etc. ; for graphical documents: chemical symbol database [9], logo databases [10,11], architectural symbol database [12] or musical symbol database CVC-MUSICMA [13]; camera-based document image analysis [14,15]. The International Association for Pattern Recognition, for instance, gathered some interesting datasets [16] mostly used for different conference competitions over the last two decades.…”

Section: Introductionmentioning

confidence: 99%

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DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

et al. 2017

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Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth. DocCreator has been used in various experiments, showing the interest of using such synthetic images to enrich the training stage of DIAR tools.

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CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal

Cited by 66 publications

References 25 publications

A novel staff removal method for printed music image

A novel staff removal method for printed music image

Music staff removal with supervised pixel classification

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

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