# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 4 Dataset and Experiment  
### Dataset  
_PrIMuS_. The PrIMuS [3] dataset is a monophonic printed music dataset for sequence recognition, which contains 87,678 monodic single-staff real scores in common Western notation. Five different formats are used to describe each record: a rendered PNG image, a MIDI file, an MEI file, and two label files in custom encodings (semantic encoding and agnostic encoding). We selected 15,062 scores from PrIMuS as a part of the training set for the non-photographic images. For object detection, every 10 images are concatenated vertically into one image, and each musical symbol in the music sheets is labeled with LabelMe software.  
_DeepScoresV2_. The DeepScoresV2 dataset is the current mainstream printed dataset for music object detection [47]. It contains 255,386 images, 135 category annotations, and a total of 151 million musical symbols. The DeepScoreV2 dataset provides bounding boxes for 135 different classes of musical symbols, along with pitch and type information for notes. Additionally, DeepScoresV2 has a dense version, including 1714 of the most diverse music score images in total. We choose the dense version as a part of the training set.  
_CPMS_ ([[https://github.com/itec-hust/CPMS](https://github.com/itec-hust/CPMS)]([https://github.com/itec-hust/CPMS](https://github.com/itec-hust/CPMS)) (accessed on 15 August 2023)). The Camera Printed Music Staves (CPMS) dataset is a monophonic printed single-staff music score dataset, differing from PrIMuS and CameraPrIMuS in that we provide photos captured by mobile phone cameras under various angles and lighting conditions in realistic scenarios for each record. In the CPMS, the training set consists of 910 lines containing 31,262 symbols, whereas the test set consists of 600 lines containing 15,542 symbols. The training set and the test set have different sources: the training set is sourced from the musescore-dataset ([[https://github.com/Xmader/musescore-dataset](https://github.com/Xmader/musescore-dataset)]([https://github.com/Xmader/musescore-dataset](https://github.com/Xmader/musescore-dataset)) (accessed on 15 August 2023)) and the test set is sourced from the public repertoire of the 2020 sight-singing exam of the Wuhan Conservatory of Music in China ([[http://www.hbea.edu.cn/html/2019-09/12349.html](http://www.hbea.edu.cn/html/2019-09/12349.html)]([http://www.hbea.edu.cn/html/2019-09/12349.html](http://www.hbea.edu.cn/html/2019-09/12349.html)) (accessed on 15 August 2023)). Specifically, the training set is obtained by taking photos of A4-sized printed music scores, whereas the test set is sourced from real books. All the sheet music photos were captured from (1) flat (Figure (a)a), (2) bend (Figure (b)b), (3) keystone distortion (Figure (c)c), and (4) uneven light distribution (Figure (d)d) scenes that may appear in real books. For each image in the CPMS dataset, we labeled the position of all the symbols and provided a manually cropped PNG file and a semantic encoding file for each line, similar to the PrIMuS dataset. It means that the CPMS is available for both object detection and sequence recognition. We choose the test set of CPMS as the test set of the experiments behind because there is no other public OMR dataset that is also available for  
Figure 8: Overview of notation assembly.  
both object detection and sequence recognition. That is also one of the reasons why we built the CPMS.