# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 2 Related Work  
Based on various data sources, OMR tasks can be categorized into two primary domains: handwritten music score recognition and printed music score recognition. Handwritten music recognition focuses on extracting and interpreting musical notations from handwritten manuscripts, including complexities such as varying handwriting styles and potential ambiguities. In recent years, there have been some breakthroughs in the research of handwritten music recognition with deep learning methods [8; 9; 10; 14; 15]. In contrast, printed music score recognition involves analyzing and extracting musical symbols and annotations from printed music scores, typically characterized by standardized fonts and precise formatting. This paper focuses on printed music recognition. Based on the different approaches, printed music score recognition methods can be categorized into two types: multi-stage method and end-to-end method. As a past mainstream, multi-stage methods [16; 17; 18; 19; 20; 21] used to decompose the task of OMR into multiple subproblems. The research of multi-stage methods mainly includes the steps of binarization [17], staff line removal [18; 19], note detection, classification [20; 21], and notation assembly [18]. [PERSON] et al. [17] proposed a binarization based on domain knowledge. [PERSON] et al. [18] applied horizontal projection to remove staff lines. [PERSON] et al. [21] solved the problem of note head recognition and pitch position by adopting template matching. [PERSON] et al. [18] proposed a context-free attributed grammar for notation assembly. In recent years, more and more scholars have been exploring deep learning methods for OMR. The object detection is used for multi-stage OMR pipeline to detect musical symbols in music sheets [22; 23; 24], particularly when dealing with certain musical symbols that are relatively small in size compared to the overall dimensions of the music score [25; 26; 27]. The methods based on object detection can be divided into two categories according to the research objectives: the detection of simple musical symbols and the detection of note pitch and note type. The majority of research only focuses on the detection of musical symbols' position rather than the pitch and type of notes. [PERSON] et al. [22] considered the detection performance of three state-of-the-art networks on the DeepScores dataset, which include Faster R-CNN [28], RetinaNet [29], and U-Net [30]. The results show that the U-Net is able to achieve higher detection accuracy with an average classification accuracy of barely 24.8%. [PERSON] et al. [23] proposed the Deep Watershed Detector (DWD). It is an object detection network based on synthetic energy maps and the watershed transform and has the ability to predicate the confidence, position, and type of each musical symbol. In addition, some scholars have also preliminarily studied the detection of note pitch and type. For example, [PERSON] et al. [24] used the YOLOv3 [31] network based on darnet53 to predict the pitch and type of notes in an end-to-end manner and achieved 92% type accuracy and 96% pitch accuracy on the MuseScore dataset. The end-to-end method [2; 4; 5; 6; 11; 32; 33; 34] has become the mainstream of OMR for its simplicity of data preprocessing. The vast majority of the end-to-end methods is sequence recognition, which means that it directly converts the stave images into symbol sequences with the pitch and type of each note. Sequence recognition methods come from the study of recognizing sequence-like objects in images. [PERSON] et al. [35] first proposed a Convolutional Recurrent Neural Network (CRNN), which is able to naturally handle sequences in arbitrary lengths and achieve an accuracy of 84% for music score recognition on a closed-source dataset. Based on the CRNN, [PERSON] et al.