# Full-Scale Piano Score Recognition
## 2 Related Work  
[PERSON] et al. [3] used a CRNN to recognize English text in noisy images. The CRNN first applies convolutions to the image and outputs the corresponding feature maps. The RNN then splits the feature maps into fixed-length segments and passes them through a transcript layer to merge the RNN outputs into the final string output. Similar research includes [4, 5], which also attempt to use end-to-end models to recognize images containing text. In the field of OMR, [PERSON] et al. [6] used a convolutional sequence-to-sequence model to recognize single-line monophonic (without chords and one voice type) sheet music images, where each sheet music image was augmented with five different noise algorithms to generate five images. The model consists of a Convolutional Neural Network (CNN), an RNN encoder, and an RNN decoder. The sheet music images are first split into fixed-length segments before being input into different CNN blocks, then passed through the RNN encoder and RNN decoder to output the corresponding pitch and duration data (in the MusicXML encoding format). [PERSON] et al. [1] created the Printed Images of Music Staves (PrIMuS) dataset, which contains scanned images of single-line monophonic (without chords or voices) scores, with each image having several corresponding encoding formats, such as MEI (abbr., Music Encoding Initiative), semantic encoding, and agnostic encoding. They used this dataset to train two CRNNs, one outputting semantic encoding and the other outputting agnostic encoding. The results showed that the semantic encoding had a higher recognition accuracy. The KERN encoding format serves as a core representation of pitch and duration in common practice music notation. This format provides a text-based description of musical scores, primarily intended for computational music analysis using the Hundrum Toolkit as the principal music encoding method [2]. The KERN scheme is designed to represent fundamental musical information for Western music from the common practice period. It encodes pitch and canonical duration while also offering limited capabilities for representing accidentals, articulation, ornamentation, ties, slurs, phrasing, bar lines, stem direction, and beaming. Generally, KERN is intended to capture the underlying semantic information of a musical score rather than its visual or orthographic features, as seen in printed images. It is specifically designed for analytical applications rather than music printing or sound synthesis. This KERN music notation is among the most frequently used representations in computational music analysis. Its key features include a simple vocabulary and an easy-to-parse file structure, making it highly suitable for end-to-end OMR applications. Additionally, KERN files are compatible with dedicated music software [7, 8] and can be automatically converted into other music encoding formats through straightforward operations. An illustrated example of a KERN-encoded score is presented in Figure 1. For a more detailed explanation, please refer to the official Hundrum KERN syntax documentation: [[https://www.hundrum.org/rep/kern/](https://www.hundrum.org/rep/kern/)]([https://www.hundrum.org/rep/kern/](https://www.hundrum.org/rep/kern/)) (accessed on 20 February 2025). [PERSON] et al. (2019) used a Residual Recurrent CRNN (R2-CRNN) model to recognize single-line monophonic (without chords or voices) scores from the Camera-PriMuS dataset, with and without distortion noise. The sheet music images were converted into feature maps by multiple CRNNs and then passed through an RNN to output both semantic and agnostic encoding formats. [PERSON] et al. (2019) used the SEILS Dataset and CAPITAN Dataset and employed a CRNN to recognize full-page monophonic scores, converting them to the MEI format. The SEILS dataset consists of black-and-white scanned images from 16 th-century Italian matigals, while the CAPITAN dataset contains color images of handwritten scores from the 17 th century. Both datasets have simpler structures compared to modern scores.