# Full-Scale Piano Score Recognition
## 6 Result  
This section presents the performance analysis of four models in the following sequence: (1) YOLOv8 accuracy for dynamics symbols; (2) Bounding box detection accuracy; (3) CRNN accuracy; (4) Overall model accuracy. Figures 11 and 12 show confusion matrices illustrating YOLOv8's accuracy on dynamics symbols, with results for both original images and images that have undergone erosion or dilation. YOLOv8 demonstrates strong performance in recognizing the types and positions of dynamics symbols, although the mAP50-95 score is slightly lower--approximately 0.86 for character-type dynamics symbols and around 0.8 for long-structure dynamics symbols. This discrepancy is likely due to the smaller number of long-structure dynamics symbols. However, in this study, even when the bounding box is slightly larger, as long as it does not overlap with the note information (i.e., the notes are not whitewashed), it does not significantly impact the CRNN's ability to recognize dynamics symbols. Figure 13 presents examples of dynamics symbols detected by the YOLO model. The value next to the class name indicates YOLOv8's confidence level for each object class and the corresponding bounding box. As shown in Figure 13, YOLO's prediction of the coordinates and range of dynamics symbols is highly accurate, with confidence levels consistently exceeding 0.9. Therefore, the removal of dynamics symbols has little to no effect on the accuracy of note information. From the confusion matrix in Figures 11 and 12, we observe minimal differences in the results between images with erosion/dilation and normal images in terms of distinguishing dynamics symbols, further underscoring YOLOv8's robustness in recognizing dynamics symbols. Figure 11: Confusion matrix for YOLOv8’s accuracy in recognizing dynamics symbols on original images. The horizontal axis represents the ground truth classification, while the vertical axis represents the predicted classification. Figure 12: Confusion matrix for YOLOv8’s accuracy in recognizing dynamics symbols on erosion or dilation images. The horizontal axis represents the ground truth classification, while the vertical axis represents the predicted classification. Table 2 shows YOLOv8's accuracy in detecting bounding boxes. The purpose of this model is to combine the positions of dynamics symbols with the KERN file output by CRNN, rather than using the original CRNN. This is because long-structure dynamics symbols are poorly recognized within CRNN and may even affect the output of the note section, so the design of separate models is suggested for recognition. In addition, Table 3 shows the YOLOv8's performance of object classification. Table 2 primarily explores the analysis of data for datasets without and with dynamics symbols when using CRNN, as well as CRNN in conjunction with YOLOv8. It also examines the differences in model performance on normal and eroded or dilated image data. The main findings of Table 2 include the following:  
1. Comparing normal sheet music images with eroded or dilated sheet music images (normal, erosion dilation tables), the latter has slightly higher error rates than the former, as expected. Since many datasets in previous studies have used images with noise and image shifts, this study also includes this data. 2. For rows CRNN+YOLOv8 for no dynam and CRNN+YOLOv8 for dynam(in both normal and eroded or dilated), the CER and SER are similar. However, there is a difference in LER, indicating that YOLOv8 has a low error rate in identifying dynamics symbols (YOLOv8_dynam), but has more error rate in identifying sheet music column (YOLOv8_column). 3. For rows CRNN+YOLOv8 for no dynam and CRNN for no dynam, the error rates are very close, indicating that YOLOv8 does not identify positions without dynamics symbols as having dynamics symbols. 4. When identifying sheet music images containing dynamics symbols, CRNN paired with YOLOv8 (CRNN+YOLOv8 for dynam) has a significantly lower error rate than CRNN without YOLOv8 (CRNN for dynam).