A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916023042572288 |
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| author | Torras, Pau Mayer, Jiří Badal, Carles Dvořáková, Martina Vlková, Markéta Herzanová Asbert, Gerard Dvořák, Vojtěch Šomorjai, Samuel Hajič jr., Jan Fornés, Alicia |
| author_facet | Torras, Pau Mayer, Jiří Badal, Carles Dvořáková, Martina Vlková, Markéta Herzanová Asbert, Gerard Dvořák, Vojtěch Šomorjai, Samuel Hajič jr., Jan Fornés, Alicia |
| contents | A large amount of musical heritage has been digitised by memory institutions: libraries, museums, and archives. Nevertheless, the field of Optical Music Recognition (OMR) has struggled with making this music machine-readable, despite advances in deep learning, mostly because no datasets for training systems in realistic conditions were available. The MusiCorpus dataset aims to remedy this situation by providing 1,309 pages of historical sheet music, primarily handwritten, with MusicXML transcriptions and symbol annotations. It is the largest dataset of handwritten music to date and the first dataset containing a realistic and representative sample of musical document collections from memory institutions, suitable for training and evaluating both end-to-end and object detection-based OMR systems and comparing their performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation Torras, Pau Mayer, Jiří Badal, Carles Dvořáková, Martina Vlková, Markéta Herzanová Asbert, Gerard Dvořák, Vojtěch Šomorjai, Samuel Hajič jr., Jan Fornés, Alicia Computer Vision and Pattern Recognition A large amount of musical heritage has been digitised by memory institutions: libraries, museums, and archives. Nevertheless, the field of Optical Music Recognition (OMR) has struggled with making this music machine-readable, despite advances in deep learning, mostly because no datasets for training systems in realistic conditions were available. The MusiCorpus dataset aims to remedy this situation by providing 1,309 pages of historical sheet music, primarily handwritten, with MusicXML transcriptions and symbol annotations. It is the largest dataset of handwritten music to date and the first dataset containing a realistic and representative sample of musical document collections from memory institutions, suitable for training and evaluating both end-to-end and object detection-based OMR systems and comparing their performance. |
| title | A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.18436 |