# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 7 Experiments
### Experimental Setting  
In total, there were 228 chants in the Mul 2 dataset. In 186 of the 228 cases, similar texts could be found using HTR. In the evaluation for the Mul2 dataset, we restrict ourselves to these 186 chants. The models utilized to generate the results were trained on Nevers P1, Nevers P2, Nevers P3, and Latin 14819 book. The performance of symbol recognition was evaluated with and without fine-tuning on a dataset of 20 pages from the Mul 2 book that were not included in the original Mul 2 dataset. Additionally, a similar experiment was conducted on the Graduate Synopticum dataset. Here, the line detection and symbol detection models underwent fine-tuning with an additional 50 pages from the Graduate Synopticum dataset. The syllable assignment model, on the other hand, was trained from scratch using synthetically generated text images extracted from texts available on the Graduate Synopticum website. The metric used for evaluation is the number of actions required to correct the automatic transcription divided by the total number of reference units (e.g., for the symbol detection step, the number of actions required to correct all the symbols divided by the total number of symbols of the dataset). We orient us at the correction actions (symbol insert, symbol delete, syllable drag and drop, etc.) provided by OMMR4 all [35]. For each correction step (layout, symbols, text, and syllable alignment), the respective percentage is indicated. This percentage pertains to various quantities: for the line step, it refers to the number of lines that need correction divided by the total number of lines; for the symbol step, it refers to the number of symbols requiring correction divided by the total number of symbols; for the text step, it refers to the number of characters requiring correction divided by the total number of characters; and for the syllable assignment step, it refers to the number of syllables requiring correction divided by the total number of syllables in the dataset. For the qualitative evaluation, we randomly selected 20 pages of the Mul2 dataset (Mul2 Sub1, Mul2 Sub2) to measure the duration required to manually rectify melodic inaccuracies (e.g., missing or extra notes, textual errors, and inaccurate syllable assignments) using OMMR4 all. Table 4 gives an overview of the statistics of all test datasets. Quantitative Evaluation Results  
Table 5 provides an overview of the results. The reported results were attained with the pipeline outlined in Section 5 and evaluated by counting the actions needed to rectify the automated transcriptions as outlined in Section 7.1. The layout detection (including \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Dataset** & **\#Pages** & **\#Chants** & **\#Staff** & **\#Symb** & **\#Clefs** & **\#Notes** & **\#Acids** & **\#Chars** & **\#Sylllabels** \\ \hline Mul2 & 102 & 186 & 621 & 18,643 & 711 & 17,747 & 185 & 16,416 & 5233 \\ Graduale no & 124 & 649 & 649 & 69,672 & 691 & 68,732 & 249 & 92,312 & 28,702 \\ Graduale minor & 275 & 1622 & 1622 & 264,192 & 2470 & 260,613 & 1109 & 230,735 & 71,443 \\ Graduale all & 304 & 1892 & 1892 & 305,746 & 2962 & 301,564 & 1220 & 262,333 & 81,223 \\ Mul2 Sub1 & 20 & 58 & 560 & 4548 & 164 & 4363 & 21 & 3914 & 1271 \\ Mul2 Sub2 & 20 & 53 & 139 & 3971 & 164 & 3776 & 31 & 4081 & 1292 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of the datasets used for evaluation: The number of symbols, staff lines, clefs, accidentals, other note components, chars, words, and syllables is listed (“Graduale no”: Subset of chants from Graduate synopticum with no text variations; “Graduale minor”: Subset with no or minor text variations; “Graduale all” with all chants; “Mul2 Sub1” and “Mul2 sub2” are two subsets from the “Mul2” dataset. staffline recognition) works very well, with no errors detected in the case of the staff lines. Occasionally, staff lines may be recognized as too long.