## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline
**Interpretation** & **Detection** & **Ground Truth** & **Image** \\ \hline Detection of staff line & 1 & 1 & 1 \\ Interpolation of staff line & 1 & 1 & 0 \\ Missed detection & 0 & 1 & 1 \\ Missed interpolation & 0 & 1 & 0 \\ False detection & 1 & 0 & 1 \\ False interpolation & 1 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Description of the staff line detection and tracking scheme performance score. At a certain pixel location, value ‘1’ indicates that: (a) Detection: a staff line detected reconstructed goes over such location; (b) Ground truth: a ground truth staff line goes over such location; (c) Image: a foreground pixel in the binary image is found at such location. the percentage of interpolated pixels is small, this is not an indication of wrong behavior but of the fact that interpolation occurs in a small percentage of the cases. The error measures were low for all the types considered, with the largest one corresponding to the case of missed detections (2.17%), this can be related to the tight length of the reconstructed staff lines, which was previously mentioned. The specificity of the task considered does not allow to find other works aimed at the same objective, however, in order to obtain some data for comparison, we have employed the schemes by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) and [PERSON] et al. [PERSON] et al. (2019) implemented by [PERSON] ([[http://pages.cs.wisc.edu/~vinitsky/omr/](http://pages.cs.wisc.edu/~vinitsky/omr/)]([http://pages.cs.wisc.edu/~vinitsky/omr/](http://pages.cs.wisc.edu/~vinitsky/omr/))), as well as his machine-learning-based scheme for staff line removal. These schemes are built for staff line removal on music scores, so, we use as input for these schemes images drawn by the Pixelwise scheme containing both staves and musical symbols so that the schemes perform correctly. The combination of images shown in Figure 1b,d serves as an illustration of such images. The images are binarized by using [PERSON]'s method Otsu (2018) before the start of the staff search procedure. The evaluation of the run-length scheme [PERSON] and [PERSON] (2018) draws the following numbers: correctly reconstructed pixels: 41.09%, correctly detected pixels: 40.73%, correctly interpolated pixels: 0.36%. The algorithm finds staff lines but it is not designed to create full staff lines based on interpolation, detection and tracking. An example of the results achieved by this scheme is shown in Figure 11a. The shortest path scheme attains 2.35% of correctly reconstructed pixels on the evaluation dataset. The scheme fails to detect staves in our particular scenario, this is the reason for such low number (no image is shown in Figure 11 since no staff lines were detected). The clustering algorithm by [PERSON] based on the utilization of feature vectors defined upon \(9\times 9\) tiles centered at each image pixel and the usage of the k-mean algorithm with Euclidean distance attains 91.85% of pixels correctly reconstructed, with 86.66% and 5.19% of pixels correctly detected and interpolated, respectively. This scheme successfully tracks most of the staff lines but gaps are not interpolated and the tracked line deviates from the actual staff line due to the presence of musical objects. As a matter of fact, a number of pixels are marked as correctly detected although they separate from the ground truth a notably observable distance, see Figure 11b. The reason for this behavior relies on the allowed detection displacement established in the evaluation scheme. Also, note that this scheme detects 107.29% of pixels with respect to the ground truth, which means that it marks as staff lines more pixels than in the ground truth. Figure 11c shows the result obtained by the proposed scheme on the top three staves on folio 7r of [PERSON] book. Figure 10: Histogram of the vertical separation between ground truth and detected pixels.