![]() The MUSCIMA++ Dataset for Handwritten Optical Music Recognition. This way, the annotation provides an explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature. Composite objects, such as notes, are captured through explicitly annotated relationships of the notation primitives (noteheads, stems, beams.). There are 23352 notes in the dataset, of which 21356 have a full notehead, 1648 have an empty notehead, and 348 are grace notes. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects, such as key signatures or time signatures. : MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection that is based on the MUSCIMA dataset. International Journal on Document Analysis and Recognition, Volume 15, Issue 3, pp 243-251, 2012. CVC-MUSCIMA: A Ground-truth of Handwritten Music Score Images for Writer Identification and Staff Removal. : Alicia Fornés, Anjan Dutta, Albert Gordo, Josep Lladós. The set of the 20 selected music sheets contains music scores for solo instruments and music scores for choir and orchestra. Each writer has transcribed the same 20 music pages, using the same pen and the same kind of music paper (with printed staff lines). All of them are adult musicians, in order to ensure that they have their own characteristic handwriting style. The database contains 1,000 music sheets written by 50 different musicians. : The CVC-MUSCIMA database contains handwritten music score images, which has been specially designed for writer identification and staff removal tasks. Proceedings of the 12th IAPR International Workshop on Graphics Recognition, Kyoto, Japan, November 2017. Towards a Universal Music Symbol Classifier. ![]() 74000 symbols are handwritten and 16000 are printed symbols. : 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. : The original dataset contains around 20 artifacts and misclassifications that were reported to the authors and Oncina, "Recognition of Pen-Based Music Notation: The HOMUS Dataset," 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp. 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. ![]() : 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. These tools are available as Python package If you find mistakes or know of any relevant datasets, that are missing in this list, pleaseĪ collection of tools that simplify the downloading and handling of datasets used for Optical Music Recognition (OMR). Images, MEI, Simplified encoding, agnostic encodingĮnd-to-End Recognition, Multimodal Retrieval, Score Followingģ0 madrigals, 150 original images, 930 symbolic files Symbol Classification, Object Detection, Semantic Segmentation Symbol Classification, Object Detection, End-To-End Recognition, Measure Recognition Staff line removal, writer identification Handwritten Online Musical Symbols (HOMUS) The following datasets are referenced from this repository: If you are interested in Optical Music Recognition research, you can find a curated bibliography at Most datasets link to the official website, where you can download the dataset. 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. 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.
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