# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 7 Experiments
### Qualitative Evaluation  
Furthermore, to assess the quality of the mass transcription, we conducted a qualitative analysis by measuring the duration required to rectify melodic inaccuracies (e.g., missing or extra notes, textual errors, and inaccurate syllable assignments). To accomplish this, 20 pages from the results of the automatic transcription were selected and afterward manually corrected using an updated variant of the overlay editor OMMR4 all [35]. OMMR4 all is an overlay editor that facilitates graphical-level corrections. It operates by utilizing precise \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Mul2**} & \multicolumn{2}{c}{**Graduale Synopticum**} \\ \hline  & & **No** & **Minor** & **All** \\ \hline Layout & & & & & \\ Lines & 0\% & 0\% & 0\% & 0\% \\ Lines \& Regions & 0\% & 0\% & 0\% & 0\% \\ Lines \& Regions \& Chants & 0.42\% & - & - & - \\ \hline Symbols & & & & & \\ Existence & 1.48/0.71\% & 0.22\% & 0.38\% & 0.39\% \\ Existence \& Type & 1.71/0.82\% & 0.23\% & 0.39\% & 0.39\% \\ Existence \& Type \& Pitch & 2.22/1.53\% & 0.58\% & 0.90\% & 0.91\% \\ Existence \& Type \& Pitch \& Reading Order & 4.40/2.4\% & 1.26\% & 1.69\% & 1.70\% \\ Existence \& Type \& Pitch \& Reading Order \& & & \\ Graphical Connections & & & & & \\ \hline Text & & & & & \\ Text only & 14.9\% & - & - & - \\ Text with syllables and line segmentation & & & & & \\ without GT text, but song repository & 3.8\% & - & - & - \\ with GT text & 1.48\% & - & - & - \\ \hline Syllable Alignment & & & & & \\ with inferred symbols and inferred text & 13.06/11.2\% & - & - & - \\ with inferred symbols and GT text & 9.0/7.12\% & 3.63\% & 4.36\% & 6.61\% \\ with GT symbols and GT text & 2.3\% & 2.70\% & 2.70\% & 2.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Necessary manual interventions in percent for correcting the automatic transcription of pages from Mul2 dataset and the Graduate Synopticum dataset. The “/” in the MUL2 column indicates without/with fine-tuning. In the case of the Graduate Synopticum dataset, “no text variations” means that the texts of the chants within one page were identical, while “minor text variations” had up to 5 character differences per chant including “no text variations”. positions of transcription components, including staves, symbols, and syllables, to improve error detection and correction. To avoid confusion, it is important to clarify that all participants in the experiment possess a musical background and lack expertise in computer science. This distinction is crucial because the Monodi+ tool necessitates musical knowledge, such as identifying note pitches, a process that is automatically handled by the OMMR4 all tool. Additionally, even in the OMMR4 all tool, musical expertise is advantageous for resolving ambiguous cases. We assessed two approaches for correcting the automatic transcription, one of which divided the correction into two phases. The first phase involved addressing errors at the symbol level, including adding missing symbols or removing incorrect symbols. The second phase addressed textual errors, which included errors caused by the OCR or post-processing algorithm at both the text and syllable levels. Examples of such errors included incorrect word separation and misaligned syllables. Instances of corrections at both the symbol and text levels are depicted in Figure 7. These phases were corrected sequentially in 10-page splits. In the second approach the correction is no longer divided into two phases (initially correcting the symbols and subsequently correcting at the text level) but instead simultaneously addresses errors in both symbols and text (Author 3). In addition, a second experiment was conducted where the first five pages were manually transcribed with the Monodi+ [4] tool to compare the transcription times. This tool is an optimized editor specially designed for the input of chants written in square notation (or similar notations) via the keyboard.