# Full-Scale Piano Score Recognition
###### Abstract  
Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. Therefore, digitizing sheet music is an essential task, such that the encoded music object could be effectively utilized for tasks such as editing or playback. Although there have been a few studies focused on recognizing sheet music images with simpler structures--such as monophonic scores or more modern scores with relatively simple structures, only containing clets, time signatures, key signatures, and notes--in this paper we focus on the issue of classical sheet music containing dynamics symbols and articulation signs, more than only clefs, time signatures, key signatures, and notes. Therefore, this study augments the data from the GrandStaff dataset by concatenating single-line scores into multi-line scores and adding various classical music dynamics symbols not included in the original GrandStaff dataset. Given a full-scale piano score in pages, our approach first applies three YOLOv8 models to perform the three tasks: 1. Converting a full page of sheet music into multiple single-line scores; 2. Recognizing the classes and absolute positions of dynamics symbols in the score; and 3. Finding the relative positions of dynamics symbols in the score. Then, the identified dynamics symbols are removed from the original score, and the remaining score serves as the input into a Convolutional Recurrent Neural Network (CRNN) for the following steps. The CRNN outputs KERN notation (KERN, a core pitch/duration representation for common practice music notation) without dynamics symbols. By combining the CRNN output with the relative and absolute position information of the dynamics symbols, the final output is obtained. The results show that with the assistance of YOLOv8, there is a significant improvement in accuracy. s 28 January 2  
Utilizes recurrent neural network (RNN) models to convert note position data into sequential representations. (3) Format Conversion: Transforms the sequential data into standard formats, such as MIDI or MusicXML, using fully connected layers or autoencoders. However, the previous approach suffers from error propagation. Errors in each step are passed on to the next, affecting the accuracy of the final result. For example, incomplete staff line removal may lead to errors in note identification, which in turn affects subsequent sequence reconstruction and encoding conversion. Although OMR research is relatively limited, related fields such as Optical Character Recognition (OCR) have made significant progress. However, sheet music differs fundamentally from text in structure and reading rules, requiring specialized algorithms for recognition and data reorganization. Researchers like [PERSON] [1] have developed an improved model based on Convolutional Recurrent Neural Network (CRNN) technology used in text recognition, specifically for identifying single-voice sheet music without chords. This model only requires sheet music images as input and corresponding encodings as ground truth for effective training. In addition, most OMR research mainly focuses on basic elements of sheet music such as clefs, time signatures, key signatures, and notes, while neglecting the identification of dynamics and articulation symbols. Dynamics symbols indicate changes in volume and intensity of music, such as loud, soft, crescendo, diminuendo, etc. Articulation symbols specify how to express or play the music, such as legato, staccato, pedaling, etc. These symbols are crucial for music performers, as they greatly influence the understanding of the score and interpretation of the music. Therefore, incorporating the identification of these elements in OMR research would more comprehensively capture the complete information of sheet music. As is well established, high-quality and moderately sized training datasets are essential for data-driven approaches.