Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/apacha/omr-datasets
Collection of datasets used for Optical Music Recognition
https://github.com/apacha/omr-datasets
dataset music music-information-retrieval music-scores optical-music-recognition
Last synced: about 3 hours ago
JSON representation
Collection of datasets used for Optical Music Recognition
- Host: GitHub
- URL: https://github.com/apacha/omr-datasets
- Owner: apacha
- License: mit
- Created: 2017-07-13T08:38:39.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2024-04-01T09:31:10.000Z (9 months ago)
- Last Synced: 2024-12-19T17:31:17.897Z (6 days ago)
- Topics: dataset, music, music-information-retrieval, music-scores, optical-music-recognition
- Language: Python
- Size: 6.88 MB
- Stars: 317
- Watchers: 17
- Forks: 41
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# [Optical Music Recognition Datasets](https://apacha.github.io/OMR-Datasets/)
This repository contains a collection of many datasets used for various Optical Music Recognition tasks, including
staff-line detection and removal, training of Convolutional Neuronal Networks (CNNs) or validating existing systems by
comparing your system with a known ground-truth.
Note that most datasets have been developed by researchers and using their dataset requires accepting a certain license
and/or citing their respective publications, as indicated for each dataset. Most datasets link to the official website,
where you can download the dataset.
If you are interested in Optical Music Recognition research, you can find a curated bibliography
at [https://omr-research.github.io/](https://omr-research.github.io/).
## Overview
The following datasets are referenced from this repository:
| Name | Engraving | Size | Format | Typical usages |
|---------------------------------------------------------------------------------------------------------------------------|:--------------------------:|:-------------------------------------------------------:|:---------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|
| [Handwritten Online Musical Symbols (HOMUS)](#handwritten-online-musical-symbols-homus) | Handwritten | 15200 symbols | Text-File | Symbol Classification (online + offline) |
| [Universal Music Symbol Collection](#universal-music-symbol-collection) | Typeset + Handwritten | ~ 90000 symbols | Images | Symbol Classification (offline) |
| [CVC-MUSCIMA](#cvc-muscima) | Handwritten | 1000 score images | Images | Staff line removal, writer identification |
| [MUSCIMA++](#muscima) | Handwritten | > 90000 annotatations | Images, Measure Annotations, MuNG | Symbol Classification, Object Detection, End-To-End Recognition, Measure Recognition |
| [Mashcima](#mashcima) | Handwritten | unlimited | Images | Various |
| [DeepScores V1](#deepscores-v1) | Typeset | 300000 images | Images, XML | Symbol Classification, Object Detection, Semantic Segmentation |
| [DeepScores V2](#deepscores-v2) | Typeset | 255385 images | Images, XML | Object Detection, Semantic Segmentation, Instance Segmentation |
| [DoReMi](#doremi) | Typeset | 6432 images | Images, XML, musicXML, MEI, MIDI | Symbol Classification, Object Detection, Semantic Segmentation, Instance Segmentation |
| [PrIMuS](#primus) | Typeset | 87678 incipits | Images, MEI, Simplified encoding, agnostic encoding | End-to-End Recognition |
| [Baro Single Stave Dataset](#baró-single-stave-dataset) | Handwritten | 95 images | Images, Simplified encoding | End-to-End Recognition |
| [Multimodal Sheet Music Dataset](#multimodal-sheet-music-dataset) | Typeset | 497 songs | Images, MIDI, Lilypond, MuNG (noteheads) | End-to-End Recognition, Multimodal Retrieval, Score Following |
| [Sheet Midi Retrieval Dataset](#sheet-midi-retrieval-dataset) | Typeset | 200 songs | Images (Jpg and PDF), MIDI, CSV | Multimodal Retrieval, Score Following |
| [AudioLabs v1](#audiolabs-v1) | Typeset | 940 score images; 24,329 bounding boxes | Images | Box Annotation Detection |
| [AudioLabs v2](#audiolabs-v2) | Typeset | 940 score images; 85,980 bounding boxes | Images | Box Annotation Detection |
| [OpenScore Lieder](#openscore-lieder) | Typeset | 1356 files | MuseScore | Various |
| [OpenScore String Quartets](#openscore-string-quartets) | Typeset | 106 files | MuseScore | Various |
| [MuseScore](#musescore) | Typeset | > 340000 files | MuseScore, PDF, MusicXML | Various |
| [MuseScore Monophonic MusicXML Dataset](#musescore-monophonic-musicxml-dataset) | Typeset | 17000 IDs | IDs for MuseScore files | Various |
| [Capitan collection](#capitan-collection) | Handwritten | 10230 symbols | Images, Text-File | Symbol Classification |
| [SEILS Dataset](#seils-dataset) | Typeset | 30 madrigals, 150 original images, 930 symbolic files | Images (PDF), .ly, .mid, .xml, .musx, .krn, .mei, .mns, .agnostic, .semantic | Various |
| [Rebelo Dataset](#rebelo-dataset) | Typeset | 15000 symbols | Images | Symbol Classification |
| [Fornes Dataset](#fornes-dataset) | Handwritten | 4100 symbols | Images | Symbol Classification |
| [Choi Accidentals Dataset](#choi-accidentals-dataset) | Typeset | 2955 images | Images with special filename | Symbol Classification |
| [Audiveris OMR](#audiveris-omr) | Typeset | 800 annotations | Images, XML | Symbol Classification, Object Detection |
| [Printed Music Symbols Dataset](#printed-music-symbols-dataset) | Typeset | 200 symbols | Images | Symbol Classification |
| [Music Score Classification Dataset](#music-score-classification-dataset) | Typeset | 1000 score images | Images | Sheet Classification |
| [OpenOMR Dataset](#openomr-dataset) | Typeset | 706 symbols | Images | Symbol Classification |
| [Gamera MusicStaves Toolkit](#gamera-musicstaves-toolkit) | Typeset | 32 score images | Images | Staff line removal |
| [Early Typographic Prints](#early-typographic-prints) | Typeset | 240 score images | | |
| [Silva Online Handwritten Symbols](#silva-online-handwritten-symbols) | Handwritten | 12600 symbols | | |
| [IMSLP](#imslp) | Typeset | >420000 score images | PDF | Various |
| [Byrd Dataset](#byrd-dataset) | Typeset | 34 score images | Images | Various |
| [MScoreLib Dataset](#MScoreLib) | Typeset | ~6000 MusicXML scores | MusicXML | Various |
If you find mistakes or know of any relevant datasets, that are missing in this list,
please [open an issue](https://github.com/apacha/OMR-Datasets/issues/new) or directly file a pull request.
## Tools for working with the datasets
A collection of tools that simplify the downloading and handling of datasets used for Optical Music Recognition (OMR).
These tools are available as Python package ``omrdatasettools`` on PyPi.
[](https://travis-ci.org/apacha/OMR-Datasets) [](https://codecov.io/gh/apacha/OMR-Datasets) [](https://badge.fury.io/py/omrdatasettools) [](http://omr-datasets.readthedocs.io/en/latest/?badge=latest) [](https://raw.githubusercontent.com/apacha/OMR-Datasets/master/LICENSE.txt)
# Handwritten Online Musical Symbols (HOMUS)
**Official website**: [http://grfia.dlsi.ua.es/homus/](http://grfia.dlsi.ua.es/homus/)
[](http://grfia.dlsi.ua.es/homus/)
**Summary**: The Handwritten Online Musical Symbols (HOMUS) dataset is a reference corpus with around 15000 samples for
research on the recognition of online handwritten music notation. For each sample, the individual strokes that the
musicians wrote on a Samsung Tablet using a stylus were recorded and can be used in online and offline scenarios.
**Scientific Publication**: J. Calvo-Zaragoza and J. Oncina, "Recognition of Pen-Based Music Notation: The HOMUS
Dataset," 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp.
3038-3043. [DOI: 10.1109/ICPR.2014.524](http://dx.doi.org/10.1109/ICPR.2014.524)
**Example**:
*Remarks*: The original dataset contains around 20 artifacts and misclassifications that were reported to the authors
and [corrected by Alexander Pacha](https://github.com/apacha/Homus).
# Universal Music Symbol Collection
**Official website**: [https://github.com/apacha/MusicSymbolClassifier](https://github.com/apacha/MusicSymbolClassifier), [Slides](https://docs.google.com/presentation/d/14g97TnrcI9o-5D6DIY-dMFfBp9kUAqpbe86c-VE83Bk/edit?usp=sharing)
[](https://opensource.org/licenses/MIT)
**Summary**: A collection of various other datasets which combines 7 datasets into a large unified dataset of 90000 tiny
music symbol images from 79 classes that can be used to train a universal music symbol classifier. 74000 symbols are
handwritten and 16000 are printed symbols.
**Scientific Publication**: Alexander Pacha, Horst Eidenberger. Towards a Universal Music Symbol Classifier. Proceedings
of the 12th IAPR International Workshop on Graphics Recognition, Kyoto, Japan, November 2017. [DOI: 10.1109/ICDAR.2017.265](http://dx.doi.org/10.1109/ICDAR.2017.265)
**Example**:
# CVC-MUSCIMA
**Official website**: [http://www.cvc.uab.es/cvcmuscima/index_database.html](http://www.cvc.uab.es/cvcmuscima/index_database.html)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: The CVC-MUSCIMA database contains handwritten music score images, which has been specially designed for
writer identification and staff removal tasks. The database contains 1,000 music sheets written by 50 different
musicians. All of them are adult musicians, in order to ensure that they have their own characteristic handwriting
style. Each writer has transcribed the same 20 music pages, using the same pen and the same kind of music paper (with
printed staff lines). The set of the 20 selected music sheets contains music scores for solo instruments and music
scores for choir and orchestra.
**Scientific Publication**: Alicia Fornés, Anjan Dutta, Albert Gordo, Josep Lladós. CVC-MUSCIMA: A Ground-truth of
Handwritten Music Score Images for Writer Identification and Staff Removal. International Journal on Document Analysis
and Recognition, Volume 15, Issue 3, pp 243-251, 2012. [DOI: 10.1007/s10032-011-0168-2](http://dx.doi.org/10.1007/s10032-011-0168-2)
**Example**:
# MUSCIMA++
**Official website**: [https://ufal.mff.cuni.cz/muscima](https://ufal.mff.cuni.cz/muscima)
**Current development**: [https://github.com/OMR-Research/muscima-pp](https://github.com/OMR-Research/muscima-pp)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection that is based on the
MUSCIMA dataset. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects,
such as key signatures or time signatures. There are 23352 notes in the dataset, of which 21356 have a full notehead,
1648 have an empty notehead, and 348 are grace notes. Composite objects, such as notes, are captured through explicitly
annotated relationships of the notation primitives (noteheads, stems, beams...). This way, the annotation provides an
explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature.
**Scientific Publication**: Jan Hajič jr., Pavel Pecina. The MUSCIMA++ Dataset for Handwritten Optical Music
Recognition. 14th International Conference on Document Analysis and Recognition, ICDAR 2017. Kyoto, Japan, November
13-15, pp. 39-46, 2017. [DOI: 10.1109/ICDAR.2017.16](http://dx.doi.org/10.1109/ICDAR.2017.16)
**Example**:
*Remarks*: Since this dataset is derived from the CVC-MUSCIMA dataset, using it requires to reference the CVC-MUSCIMA as
well.
## MUSCIMA++ Measure Annotations
**Website**: [https://omr-datasets.readthedocs.io](https://omr-datasets.readthedocs.io/en/latest/downloaders.html#omrdatasettools.OmrDataset.OmrDataset.MuscimaPlusPlus_MeasureAnnotations).
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: Based on the MUSCIMA++ dataset, a subset of the annotations was constructed, that contains only annotations
for measure and stave recognition.
The dataset has some errors fixed that version MUSCIMA++ 1.0 exhibits and comes in a plain JSON format, as well as in
the COCO format.
This dataset was created by [Alexander Pacha](https://alexanderpacha.com) and can be directly downloaded
from [here](https://github.com/apacha/OMR-Datasets/releases/download/datasets/MUSCIMA-pp_v1.0-measure-annotations.zip).
**Example**:
## Mashcima
**Website**: [https://github.com/Jirka-Mayer/Mashcima](https://github.com/Jirka-Mayer/Mashcima).
[](https://opensource.org/licenses/MIT)
**Summary**: Mashcima is a tool to synthesize handwritten monophonic music scores, based on the MUSCIMA++ dataset.
The aim of this tool is to provide abundant training data for researchers in the field of handwritten music recognition.
It works by taking symbol masks from the MUSCIMA++ dataset and placing them onto a blank staff according to a given
annotation. This annotation may be your own, may be generated randomly, or may be taken from the PrIMuS dataset.
**Scientific Publication**: Jiří Mayer and Pavel Pecina. Synthesizing Training Data for Handwritten Music Recognition.
16th International Conference on Document Analysis and Recognition, ICDAR 2021. Lausanne, September 8-10, pp. 626-641, 2021.
**Example**:
# DeepScores V1
**Official website**: [https://tuggeluk.github.io/deepscores/](https://tuggeluk.github.io/deepscores/)
[](https://tuggeluk.github.io/deepscores/)
**Summary**: Synthetic dataset of 300000 annotated images of written music for object classification, semantic
segmentation and object detection. Based on a large set of MusicXML documents that were obtained
from [MuseScore](#musescore), a sophisticated pipeline is used to convert the source into LilyPond files, for which
LilyPond is used to engrave and annotate the images. Images are rendered in five different fonts to create a variation
of the visual appearance.
**Scientific Publication**: Lukas Tuggener, Isamil Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann.
[DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects](https://arxiv.org/abs/1804.00525).
ICPR 2018.
**Example**:
# DeepScores V2
**Official website**: https://zenodo.org/records/4012193
[](https://creativecommons.org/licenses/by/4.0/)
**Summary**: The DeepScoresV2 Dataset for Music Object Detection contains digitally rendered images of written sheet
music, together with the corresponding ground truth to fit various types of machine learning models. A total of 151
Million different instances of music symbols, belonging to 135 different classes are annotated. The total Dataset
contains 255,385 Images. For most researches, the dense version, containing 1714 of the most diverse and interesting
images, should suffice.
**Scientific Publication**: Lukas Tuggener, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, and Thilo
Stadelmann. The DeepScoresV2 Dataset and Benchmark for Music Object Detection. 25th International Conference on Pattern
Recognition (ICPR2020), Milan, Italy.
**Example**:
# DoReMi
**Official website**: [https://github.com/steinbergmedia/DoReMi/](https://github.com/steinbergmedia/DoReMi/)
[](https://github.com/steinbergmedia/DoReMi/)
**Summary**
DoReMi includes around 6432 images of sheet music with nearly a million annotated objects. Each object on the page is
annotated with category labels from 94 different classes. Given that this dataset is generated using Dorico (a music
notation software from Steinberg), we had the chance to retrieve more musical information, but also different types of
data. Using Dorico we generate 5 types of data that could possibly be used in different steps of OMR. These data are
images of scores (binary or colour), the musicXML file, XML files with metadata such as bounding boxes of each object
and pixel mask information together with musical information on how noteheads, beams stems and slurs are linked but also
their duration beats, onset beats, pitch octave, staff id. Most of the images include one system per page; depending on
the number of voices, they will have one or more staves per page.
**Scientific Publication**: Shatri, Elona, and György Fazekas. DoReMi: First glance at a universal OMR dataset. WoRMS
2021, 2021. [https://arxiv.org/abs/2107.07786](https://arxiv.org/abs/2107.07786)
**Example**
```xml
504
noteheadBlack
466
2236
28
24
0:12 1:11 0:14 1:16 0:10 1:19 0:8 1:21 0:6 1:22 0:5 1:24 0:3 1:25 0:2 1:26 0:1 1:27 0:1 1:27 0:1 1:54 0:1 1:27
0:1 1:26 0:2 1:26 0:2 1:25 0:3 1:24 0:4 1:23 0:5 1:22 0:6 1:21 0:8 1:19 0:10 1:16 0:13 1:13 0:18 1:6 0:16
541 559 571
0.333333
47.666668
4
69
A
1120
0
```
# PrIMuS
**Official website**: [https://grfia.dlsi.ua.es/primus/](https://grfia.dlsi.ua.es/primus/)
[](https://grfia.dlsi.ua.es/primus/)
**Summary**: The Printed Images of Music Staves (PrIMuS) contains the 87678 real-music incipits (an incipit is a
sequence of notes, typically the first ones, used for identifying a melody or musical work) in five different formats:
As rendered PNG image, as MIDI-file, as MEI-file and as two custom encodings (semantic encoding and agnostic encoding).
The incipits are originally taken from the [RISM dataset](http://opac.rism.info/).
PrIMuS has been extended into the [Camera-PrIMuS dataset](https://grfia.dlsi.ua.es/primus/) that contains the same
scores, but the images have been distorted to simulate imperfections introduced by taking pictures of sheet music in a
real scenario.
**Scientific Publications**:
- Jorge Calvo-Zaragoza and David Rizo. End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied
Sciences, 2018, 8, 606. [http://www.mdpi.com/2076-3417/8/4/606](http://www.mdpi.com/2076-3417/8/4/606) (for PrIMuS)
- Jorge Calvo-Zaragoza and David Rizo. Camera-PrIMuS: Neural end-to-end Optical Music Recognition on realistic
monophonic scores. In Proceedings of the 19th International Society for Music Information Retrieval Conference, Paris,
2018. [http://ismir2018.ircam.fr/doc/pdfs/33.pdf](http://ismir2018.ircam.fr/doc/pdfs/33.pdf) (for Camera-PrIMuS)
**Example**:
# Baró Single Stave Dataset
**Official website**: [http://www.cvc.uab.es/people/abaro/datasets.html](http://www.cvc.uab.es/people/abaro/datasets.html)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: The Single Stave dataset by Arnau Baró is a derived dataset from the CVC-MUSCIMA dataset and contains 95
single stave music scores with ground truth labels on the symbol level.
**Scientific Publication**: Arnau Baró, Pau Riba, Jorge Calvo-Zaragoza, and Alicia Fornés. From Optical Music
Recognition to Handwritten Music Recognition: a Baseline. Patter Recognition Letters, 2019 (in
press). [DOI: 10.1016/j.patrec.2019.02.029](https://doi.org/10.1016/j.patrec.2019.02.029)
**Example**:
# Multimodal Sheet Music Dataset
**Official website**: [https://github.com/CPJKU/msmd](https://github.com/CPJKU/msmd)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: MSMD is a synthetic dataset of 497 pieces of (classical) music that contains both audio and score
representations of the pieces aligned at a fine-grained level (344,742 pairs of noteheads aligned to their audio/MIDI
counterpart). It can be used for training and evaluating multimodal models that enable crossing from one modality to the
other, such as retrieving sheet music using recordings or following a performance in the score image.
**Scientific Publication**:
Matthias Dorfer, Jan Hajič jr., Andreas Arzt, Harald Frostel, Gerhard
Widmer. [Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification](https://transactions.ismir.net/articles/10.5334/tismir.12/).
Transactions of the International Society for Music Information Retrieval, issue 1, 2018.
**Example**:
# Sheet Midi Retrieval Dataset
**Official website**: [https://github.com/tjtsai/SheetMidiRetrieval](https://github.com/tjtsai/SheetMidiRetrieval)
[](https://opensource.org/licenses/MIT)
**Summary**: This dataset contains the scores for 200 music pieces along with their MIDI representation and query images
with they ground-truth alignment.
**Scientific Publications**:
- Timothy Tsai, Daniel Yang, Mengyi Shan, Thitaree Tanprasert, TTeerapat
Jenrungrot. [Using Cell Phone Pictures of Sheet Music To Retrieve MIDI Passages](https://ieeexplore.ieee.org/document/8999506).
IEEE Transactions on Multimedia. 2020
# AudioLabs v1
**Official website**: [https://www.audiolabs-erlangen.de/resources/MIR/2019-ISMIR-LBD-Measures](https://www.audiolabs-erlangen.de/resources/MIR/2019-ISMIR-LBD-Measures)
[](https://www.audiolabs-erlangen.de/resources/MIR/2019-ISMIR-LBD-Measures)
**Summary**: The data set provides measure annotations for several hundred pages of sheet music, including the complete
cycle *Der Ring des Nibelungen* by Richard Wagner, selected piano sonatas by Ludwig von Beethoven, the complete cycle
*Winterreise* by Franz Schubert, as well as selected pieces from the Carus publishing house.
**Scientific Publication**: Frank Zalkow, Angel Villar Corrales, TJ Tsai, Vlora Arifi-Müller, and Meinard Müller: "Tools
for Semi-Automatic Bounding Box Annotation of Musical Measures in Sheet Music". Late Breaking/Demo at the 20th
International Society for Music Information Retrieval, Delft, The Netherlands, 2019. [Download the PDF](http://archives.ismir.net/ismir2019/latebreaking/000006.pdf)
**Example**:
# AudioLabs v2
**Official website**: [Download the dataset](https://github.com/apacha/OMR-Datasets/releases/download/datasets/AudioLabs_v2.zip)
[](https://www.audiolabs-erlangen.de/resources/MIR/2019-ISMIR-LBD-Measures)
**Summary**: AudioLabs v2 is an extension of the AudioLabs v1 dataset with 24,186 bounding boxes for system measures,
11,143 bounding boxes for stave annotations and 50,651 bounding boxes for staff measures, which where generated with the
help of a neural network and the original dataset. Annotations are available in the original CSV format, plain JSON
format and COCO format.
**Example**:
# OpenScore Lieder
**Official website**: [https://github.com/OpenScore/Lieder](https://github.com/OpenScore/Lieder)
[](https://creativecommons.org/publicdomain/zero/1.0)
**Summary**: Collection of songs by 19th century composers in MuseScore format with associated data.
**Publication**: Gotham, M. R. H.; and Jonas, P. The OpenScore Lieder Corpus. In Münnich, S.; and Rizo, D., editor(s),
Music Encoding Conference Proceedings 2021, pages 131–136, 2022. Humanities Commons. Best Poster Award.
# OpenScore String Quartets
**Official website**: [https://github.com/OpenScore/StringQuartets](https://github.com/OpenScore/StringQuartets)
[](https://creativecommons.org/publicdomain/zero/1.0)
**Summary**: Collection of more than 100 string quartets by over 40 composers, encoded by a dedicated team of volunteers
in MuseScore with associated data.
**Publication**: Mark Gotham, Maureen Redbond, Bruno Bower, and Peter Jonas. 2023. The “OpenScore String Quartet”
Corpus. In Proceedings of the 10th International Conference on Digital Libraries for Musicology (DLfM '23).
Association for Computing Machinery, New York, NY, USA, 49–57.
[https://doi.org/10.1145/3625135.3625155](https://doi.org/10.1145/3625135.3625155)
# MuseScore
**Official website**: [https://musescore.com/sheetmusic](https://musescore.com/sheetmusic)
[](https://musescore.com/sheetmusic)
**Summary**: [MuseScore](https://musescore.org/) is a free music notation software and also allows their users to upload
their sheet music to their website and share it with others. Currently (Jan. 2018) the website hosts over 340000 music
sheets, that can be downloaded as MuseScore file (mscz), PDF, MusicXML, MIDI and MP3.
**Publication**: [https://musescore.org](https://musescore.org)
**Example**:
# MuseScore Monophonic MusicXML Dataset
**Official website**: [https://github.com/eelcovdw/mono-musicxml-dataset](https://github.com/eelcovdw/mono-musicxml-dataset)
[](https://opensource.org/licenses/MIT)
**Summary**: This dataset contains the IDs to 17000 monophonic scores, that can be downloaded from musescore.com. A
sample script is given that downloads one score, given you've obtained a developer key from the MuseScore developers.
**Scientific Publication**: Eelco van der Wel, Karen Ullrich. Optical Music Recognition with Convolutional
Sequence-to-Sequence Models. CoRR, arXiv:1707.04877, 2017. [https://arxiv.org/abs/1707.04877](https://arxiv.org/abs/1707.04877)
**Examples**:
# Capitan collection
**Official website**: [http://grfia.dlsi.ua.es/](http://grfia.dlsi.ua.es)
[](http://grfia.dlsi.ua.es/homus/) (Freely available for
research purposes)
**Summary**: A corpus collected by an electronic pen while tracing isolated music symbols from Early manuscripts. The
dataset contains information of both the sequence followed by the pen and the patch of the source under the tracing
itself. In total it contains 10230 samples unevenly spread over 30 classes. Each symbol is described as stroke (capitan
stroke) and including the piece of score below it (capitan score).
**Scientific Publication**: Jorge Calvo-Zaragoza, David Rizo and Jose M. Iñesta. Two (note) heads are better than one:
pen-based multimodal interaction with music scores. Proceedings of the 17th International Society of Music Information
Retrieval conference, 2016. [Download the PDF](http://grfia.dlsi.ua.es/repositori/grfia/pubs/345/two-note-heads.pdf)
**Example**:
*Remarks*: This dataset exists in two flavours:
* As raw dataset, which contains only the textual descriptions of the strokes and the images, called *Bimodal music
symbols from Early notation*. This format is similar to the HOMUS dataset.
* As rendered images inside of the *Isolated handwritten music symbols* dataset. Also refered to as Capitan collection.
# SEILS Dataset
**Official website**: [https://github.com/SEILSdataset/SEILSdataset](https://github.com/SEILSdataset/SEILSdataset)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
**Summary**: The SEILS dataset is a corpus of scores in lilypond, music XML, MIDI, Finale, **kern, MEI, **mens,
agnostic, semantic and pdf formats, in white mensural and modern notation. The transcribed scores have been taken from
the 16th century anthology of Italian madrigals Il Lauro Secco, published for the first time in 1582 by Vittorio Baldini
in Ferrara (Italy). The corpus contains scores of 30 different madrigals for five unaccompanied voices composed by a
variety of composers.
**Scientific Publications**:
* Emilia Parada-Cabaleiro, Anton Batliner, Alice Baird, Björn W. Schuller. The SEILS dataset:
Symbolically Encoded Scores in ModernAncient Notation for Computational Musicology. Proceedings of the 18th
International Society of Music Information Retrieval conference, 2017, Suzhou, P.R. China, pp.
575-581. [Download the PDF](https://ismir2017.smcnus.org/wp-content/uploads/2017/10/14_Paper.pdf)
* Emilia Parada-Cabaleiro, Maximilian Schmitt, Anton Batliner, Björn W. Schuller.
Musical-Linguistic annotation of Il Lauro Secco. Proceedings of the 19th International Society of Music Information
Retrieval conference, 2018, Paris, France, pp.
461-467. [Download the PDF](http://ismir2018.ircam.fr/doc/pdfs/11_Paper.pdf)
* Emilia Parada-Cabaleiro, Anton Batliner, Björn W. Schuller. A diplomatic edition of Il Lauro
Secco: Ground truth for OMR of white mensural notation. Proceedings of the 20th International Society of Music
Information Retrieval conference, 2019, Delft, The Netherlands, pp.
557-564. [Download the PDF](http://archives.ismir.net/ismir2019/paper/000067.pdf)
**Example**:
# Rebelo Dataset
**Official websites**: [http://www.inescporto.pt/~arebelo/index.php](http://www.inescporto.pt/~arebelo/index.php)
and [http://www.inescporto.pt/~jsc/projects/OMR/](http://www.inescporto.pt/~jsc/projects/OMR/)
[](https://creativecommons.org/licenses/by-sa/4.0/)
**Summary**: Three datasets of perfect and scanned music symbols including an extensive set of synthetically modified
images for staff-line detection and removal. Contains approximately 15000 music symbols.
**Scientific Publication**: A. Rebelo, G. Capela, and J. S. Cardoso, "Optical recognition of music symbols: A
comparative study" in International Journal on Document Analysis and Recognition, vol. 13, no. 1, pp. 19-31, 2010. [DOI: 10.1007/s10032-009-0100-1](http://dx.doi.org/10.1007/s10032-009-0100-1)
**Examples**:
*Remarks*: The dataset is usually only available upon request, but with written permission of Ana Rebelo I hereby make
the datasets available under a permissive CC-BY-SA license, which allows you to use it freely given you properly mention
her work by citing the above mentioned
publication: [Download the dataset](https://github.com/apacha/OMR-Datasets/releases/download/datasets/Rebelo.Dataset.zip).
# Fornes Dataset
**Official website**: [http://www.cvc.uab.es/~afornes/](http://www.cvc.uab.es/~afornes/)
[](http://grfia.dlsi.ua.es/homus/)
**Summary**: A dataset of 4100 black and white symbols of 7 different symbol classes: flat, natural, sharp,
double-sharp, c-clef, g-clef, f-clef.
**Scientific Publication**: A.Fornés and J.Lladós and G. Sanchez, "Old Handwritten Musical Symbol Classification by a
Dynamic Time Warping Based Method", in Graphics Recognition: Recent Advances and New Opportunities. Liu, W. and Lladós,
J. and Ogier, J.M. editors, Lecture Notes in Computer Science, Volume 5046, Pages 51-60, Springer-Verlag Berlin,
Heidelberg, 2008. [DOI: 10.1007/978-3-540-88188-9_6
](http://dx.doi.org/10.1007/978-3-540-88188-9_6)
**Example**:
# Choi Accidentals Dataset
**Official website**: [https://www-intuidoc.irisa.fr/en/choi_accidentals/](https://www-intuidoc.irisa.fr/en/choi_accidentals/)
[](https://www-intuidoc.irisa.fr/en/choi_accidentals/)
**Summary**: A dataset of 2955 small black and white images of accidentals (flat, natural, sharp) in context, including
968 images without accidentals (reject class).
Annotations are included into the filename such
as `{composer}-{page number}_{accidental class}_{window box}_{accidental box}_{note head box}.jpg` with
the boxes containing absolute coordinates, relative to the original music score page in the
format: `{left}x{top}x{right}x{bottom}`.
**Example**:
# Audiveris OMR
**Official website**: [https://github.com/Audiveris/omr-dataset-tools](https://github.com/Audiveris/omr-dataset-tools)
[](https://www.gnu.org/licenses/agpl-3.0.en.html)
**Summary**: A collection of four music sheets with approximately 800 annotated music symbols.
The [DeepScore project](https://www.zhaw.ch/no_cache/en/research/people-publications-projects/detail-view-project/projekt/2895/)
in cooperation with the ZHAW targets
towards [automatically generating these images](https://github.com/Audiveris/omr-dataset-tools/wiki/Synthetic-Images)
and the annotations from MuseScore or Lilypond documents.
**Example**:
# Printed Music Symbols Dataset
**Official website**: [https://github.com/apacha/PrintedMusicSymbolsDataset](https://github.com/apacha/PrintedMusicSymbolsDataset)
[](https://opensource.org/licenses/MIT)
**Summary**: A small dataset of about 200 printed music symbols out of 36 different classes. Partially with their
context (staff-lines, other symbols) and partially isolated.
**Example**:
# Music Score Classification Dataset
**Official website**: [https://github.com/apacha/MusicScoreClassifier](https://github.com/apacha/MusicScoreClassifier)
[](https://opensource.org/licenses/MIT)
**Summary**: A dataset of 2000 images, containing 1000 images of music scores and 1000 images of other objects including
text documents. The images were taken with a smartphone camera from various angles and different lighting conditions.
**Scientific Publication**: Alexander Pacha, Horst Eidenberger, Towards Self-Learning Optical Music Recognition. 2017
16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancún, Mexiko, Dezember 2017. [DOI: 10.1109/ICMLA.2017.00-60](http://dx.doi.org/10.1109/ICMLA.2017.00-60)
**Example**:
# OpenOMR Dataset
**Official website**: [http://sourceforge.net/projects/openomr/](http://sourceforge.net/projects/openomr/)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
**Summary**: A dataset of 706 symbols (g-clef, f-clef) and symbol primitives (note-heads, stems with flags, beams) of 16
classes created by Arnaud F. Desaedeleer as part of his master thesis to train artificial neural networks.
**Scientific Publication**: Arnaud F. Desaedeleer, "Reading Sheet Music", Master Thesis, University of London, September
2006, [Download](http://sourceforge.net/projects/openomr/)
**Example**:
# Gamera MusicStaves Toolkit
**Official website**: [http://music-staves.sf.net/](http://music-staves.sf.net/)
and [https://github.com/hsnr-gamera](https://github.com/hsnr-gamera)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
**Summary**: The Synthetic Score Database by Christoph Dalitz that contains 32 scores that have been computer generated
with different music typesetting programs. It contains ground truth data and is suitable for the deformations
implemented in the toolkit.
**Scientific Publication**: C. Dalitz, M. Droettboom, B. Pranzas, I. Fujinaga: A Comparative Study of Staff Removal
Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 753-766 (2008) [DOI: 10.1109/TPAMI.2007.70749](http://dx.doi.org/10.1109/TPAMI.2007.70749)
**Example**:
# Early Typographic Prints
**Summary**: 240 pages of early typographic music having a total of 1478 staves and 52178 characters corresponding to
175 different symbols with ground-truth obtained by manually entering via a MIDI keyboard.
**Scientific Publication**: Laurent Pugin. Optical Music Recognition of Early Typographic Prints using Hidden Markov
Models. 7th International Conference on Music Information Retrieval (ISMIR’06), Victoria, Canada, October 2006. [http://www.aruspix.net/publications/pugin06optical.pdf](http://www.aruspix.net/publications/pugin06optical.pdf)
**Example**:
# Silva Online Handwritten Symbols
**Summary**: Dataset of 12600 trajectories of handwritten music symbols, drawn by 50 writers with an Android
application. Every writer drew each of the 84 different symbols three times.
**Scientific Publication**: Rui Miguel Filipe da Silva. Mobile framework for recognition of musical characters. Master
Thesis. Universidade do Porto, June 2013. [https://repositorio-aberto.up.pt/bitstream/10216/68500/2/26777.pdf](https://repositorio-aberto.up.pt/bitstream/10216/68500/2/26777.pdf)
# IMSLP
**Official website**: [http://imslp.org](http://imslp.org)
[](http://creativecommons.org/licenses/by-sa/4.0/)
**Summary**: The Petrucci Music Library is the largest collection of public domain music, with over 420000 (Jan. 2018)
freely available PDF scores by almost 16000 composers accompanied by almost 50000 recordings. It also maintains an
extensive list of [other music score websites](http://imslp.org/wiki/IMSLP:Other_music_score_websites), where you can
find many more music sheets, e.g. collected during research projects by universities.
**Example**:
# Byrd Dataset
**Official website**: [
~~http://www.diku.dk/hjemmesider/ansatte/simonsen/suppmat/jnmr/~~](http://www.diku.dk/hjemmesider/ansatte/simonsen/suppmat/jnmr/) (broken). Download
from [Github mirror](https://github.com/apacha/OMR-Datasets/releases/download/datasets/ByrdOmrTestCorpus.zip).
 (Authors want to be
contacted)
**Summary**: A small dataset of 34 high quality images with individual music score pages of increasing difficulty.
**Scientific Publication**: Donald Byrd & Jakob Grue Simonsen: "Towards a Standard Testbed for Optical Music
Recognition: Definitions, Metrics, and Page Images". Journal of New Music Research, vol 44, nr.3, pages 169-195, 2015. [DOI: 10.1080/09298215.2015.1045424](http://dx.doi.org/10.1080/09298215.2015.1045424)
**Example**:
# MScoreLib
**Official website**: [http://mscorelib.com/](http://mscorelib.com/)
[](http://mscorelib.com/)
**Summary**: A dataset of both manually typeset music and OMR processed scores for comparison of their performance.
**Scientific Publication**: https://github.com/MultiOMR
**Example**:
