## 5 Experiments
### Staff Line Detection
#### 5.2.2 Results on All Data  
The results of the staff line detection using a five cross-fold on all available data are shown in Table 2.  
Clearly over 99% of all staff lines are found (recall) and over 99% of all predicted staff lines are correct (precision). The resulting \(F_{1}^{\text{D}}\)-score is about 99.7%.  
The length of the correctly detected staff lines has a \(F_{1}^{\text{LF}}\)-score of 98.5%; however, the recall that is higher than the precision shows that lines mostly are too long.  
Typical errors are shown in Figure 12. The green, red and blue colors indicate TP, FP and FN pixels, respectively, though FN are rare. Most errors are either staff lines that are too long, for example caused by falsely extending a staff or staff lines that are predicted on background. Both error types are, among other things, caused by background noise such as bleeding staves or foreground text or drop capitals. Naturally, other errors occur on damaged pages or very thin lines that must almost be guessed. Furthermore, there are always small inaccuracies at the exact beginning and at the end of a staff line which however are also a problem of the GT itself. The severity of the errors on the symbol detection will be discussed in Section 5.3.5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Line Detection**} & \multicolumn{3}{c}{**Length Fit**} & \\
**Model** & **Prec.** & **Rec.** & \(F_{1}^{\text{D}}\) & **Prec.** & **Rec.** & \(F_{1}^{\text{LF}}\) & **Total \(F_{1}\)** \\ \hline Default & 0.996 & 0.998 & 0.997 & 0.977 & 0.995 & 0.985 & 0.982 \\ Data aug. & 0.987 & 0.991 & 0.989 & 0.972 & 0.995 & 0.983 & 0.972 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Staff line detection and length metrics with and without data augmentation. The last column combines the two individual \(F_{1}\) measures by multiplication.  
Figure 11: Visualisation of the metric used to evaluate the staff line detection. The blue and red solid lines in the upper image denote the GT and prediction of a single staff line, respectively. The colored area is the allowed space of 3 px where lines must overlap to be counted as a match. If at least one vertical pixel is green it is counted as correct. The bottom line shows the intervals which are counted as false negative (FN), true positive (TP) and false positive (FP). The deviation in the first part counts both as FN and FP.  
Table 3 lists the recognition rate of complete staves. It is astonishing that the scores of the staff detection are (almost) identical to the staff line detection; however, this shows that the staff line detection only predicts wrong lines across a whole staff, for example all four lines are too long or a whole staff is predicted on a blank page, as seen in Figure 12. This is also shown by a precision and recall of 100% in the measure of hit lines. This states, that if a staff is detected all lines are correct. In the following, we will only report the staff line detection accuracy because it is almost equivalent to the staff accuracy but also includes the metric concerning the exact length of the lines.  
Applying data augmentation yields no improvements in both metrics, instead the results clearly worsen. However, especially the precision drops compared to the recall. This is justified by errors that are mostly confusions if a staff is bleeding or real, which is possibly caused by the strong augmentations in contrast and brightness levels.