## 6 Conclusion and Future Work  
This paper introduces a new system for automatic staff line and symbol detection on historical medieval notations based on deep neural networks. The main results on our data set are (compare Table 11):  
* Staff lines and staves are recognised with an \(F_{1}\)-score greater than 99%. The major error sources are staves that are detected on a bled background or which are extended into other foregrounds (e.g., text). * A trained staff line model generalises flawlessly to different layout types and thus needs no retraining. * Our best model finds symbols with an \(F_{1}\)-score of above 96% and yields a dSAR of about 87%. * Pretraining is helpful but not mandatory for the symbol detection, since the dSAR only increases about 4%. * The full transcription (compare Figure 13) comprises staves consisting of four staff lines stored as polylines, the position and type of clefs, the position of accidentals, the position and graphical connection of NCs which comprise neumes. To further improve the staff detection, which is close to perfect, more experiments must be conducted to evaluate the effect of data augmentation which is unclear in the proposed manner. Augmentations in brightness and contrast must especially be reconsidered to allow for more robust models. Furthermore, staves that are detected on background can be dropped by introducing a threshold of minimum symbols per staff because the symbol detection only occasionally detects symbols on background. Another problem are staves that extend into text or drop capitals. However a symbol detection which is explicitly trained to ignore text could help to determine the boundaries between staff and text because no symbols should be detected there. However, the primary goal of our future work is to further reduce the errors of the symbol detection. First, the error analysis shows that false notes are the main source of symbol errors. The connection and location of notes induces many errors, which however are also difficult tasks for humans. We proposed to evaluate a two stage approach similar to Reference [7] that first predict neumes as combined components and then separates them into individual NCs; however, with incorporation of deep neural networks for object detection. Furthermore, to improve the quality of clef or accidental prediction more data must be gathered or over-sampled in the training data either by data augmentation or by presenting these lines more often. A completely different approach, which is based on the state-of-the-art networks in OCR, is to directly use sequence-to-sequence networks as already proposed in Reference [28] on contemporary OMR. A CNN/LSTM hybrid network with a combination of a CTC-loss function directly predicts an NC sequence. The main advantage is that GT production is easier since only the sequence has to be transcribed but not the actual position of each single NC. However, it is to be expected that more GT is required since it has to learn autonomously both the shape of all music symbols and the staff lines to infer the location of the symbols. It is still an open issue whether this CTC-approach in fact yields improved results. Furthermore, because this approach directly predicts a symbol sequence and no actual note positions relative to the image, it cannot be used if this information is mandatory. \begin{table}
\begin{tabular}{l l} \hline \hline
**Task** & **Score** \\ \hline \(F_{1}\)-scores of staff line \& staff detection & \textgreater{}99\% \\ \(F_{1}\)-score of detected Symbols & \textgreater{}96\% \\ Diplomatic sequence accuracy (dSAR) & \(\approx\)87\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Overview of the main results of this paper. Author Contributions:[PERSON] conceived and performed the experiments and created the GT data. [PERSON] and [PERSON] contributed the staff line detection algorithm. [PERSON] designed the symbol detection algorithm. [PERSON] and [PERSON] analysed the results. [PERSON] wrote the paper with substantial contributions of [PERSON].